minor fixes

fix(groot): address review findings for the N1.7 port
N1.5 removal is now explicit and actionable: - Legacy N1.5 checkpoint configs (tokenizer_assets_repo) parse and fail with a single clear error pointing to lerobot==0.5.1 instead of a cryptic draccus DecodingError - Removed N1.5 processor registry names (groot_pack_inputs_v3, groot_eagle_encode_v3, groot_eagle_collate_v3) are stubbed to raise the same guidance; groot_action_unpack_unnormalize_v1 changed semantics, so the step is re-registered as _v2 and _v1 is stubbed - N1.5 detection also recognizes checkpoint config.json content (model_type/architectures/eagle backbone), not just path names; every rejection surface includes the migration guidance - groot.mdx documents the breaking change and migration path Runtime fixes: - use_bf16=False no longer crashes (compute_dtype only set when used) - GrootN17ActionDecodeStep handles the 2-D (B, D) actions delivered by sync select_action (relative eef/non-eef decode was broken in lerobot-eval/record flows) - Postprocessor falls back to dataset stats when a raw checkpoint lacks the configured embodiment tag instead of silently emitting normalized [-1, 1] actions - Hub-hosted finetuned N1.7 checkpoints load: the processor config is resolved via hf_hub_download for non-local paths, with a tolerant retry when inspection fails - Raw-checkpoint processor branch honors caller overrides (device, rename_map) instead of dropping them - Relative-action raw-state cache is per-instance instead of process-global (cross-instance contamination) - Camera/modality-key mismatches warn, including the zero-match fallback; checkpoint revision is no longer forwarded into backbone loading; deprecated Qwen2VLImageProcessorFast replaced with Qwen2VLImageProcessor Config/UX: - GrootConfig defaults are the N1.7 values; explicitly passed legacy N1.5-era values (chunk_size=50, max_state_dim=64, ...) are remapped with a warning instead of silently - Explicit action_decode_transform='none' wins over the libero_sim default (new 'auto' sentinel) and survives save/load round-trips Tests/CI: - pytest.importorskip guards so fast_tests tiers pass without transformers (was 10 failures, now 0) - Regression tests for every fix; from_pretrained rejection tests now actually exercise from_pretrained - Parity test reads the artifact seed, fails on shape mismatch instead of silently truncating, and a new case runs LeRobot's real Qwen3-VL preprocessing on raw observations dumped by the producer - docs: dead huggingface-cli download replaced with hf download Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-06-16 15:57:03 +00:00 · 2026-06-12 18:45:24 +02:00 · 2026-06-12 16:51:14 +02:00 · 2026-06-12 11:01:25 +02:00 · 2026-06-12 08:42:45 +00:00 · 2026-06-12 10:14:05 +02:00
115 changed files with 30669 additions and 4237 deletions
@@ -105,7 +105,7 @@ lerobot-train \
 | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md) |
 | **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                             |
-| **VLAs Models**            | [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx)            |
+| **VLAs Models**            | [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.7](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx)            |

 Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub

@@ -9,6 +9,8 @@
 - sections:
  - local: il_robots
    title: Imitation Learning for Robots
+  - local: lelab
+    title: LeLab - Lerobot GUI
  - local: bring_your_own_policies
    title: Adding a Policy
  - local: integrate_hardware
@@ -59,10 +61,14 @@
    title: π₀-FAST (Pi0Fast)
  - local: pi05
    title: π₀.₅ (Pi05)
+  - local: molmoact2
+    title: MolmoAct2
+  - local: vla_jepa
+    title: VLA-JEPA
  - local: eo1
    title: EO-1
  - local: groot
-    title: NVIDIA GR00T N1.5
+    title: NVIDIA GR00T
  - local: xvla
    title: X-VLA
  - local: multi_task_dit
@@ -73,6 +79,10 @@
 - sections:
  - local: sarm
    title: SARM
+  - local: robometer
+    title: ROBOMETER
+  - local: topreward
+    title: TOPReward
  title: "Reward Models"
 - sections:
  - local: inference
@@ -79,17 +79,13 @@ If your local computer doesn't have a powerful GPU, you can utilize Google Colab
 Once training is complete, you can evaluate your ACT policy using the `lerobot-record` command with your trained policy. This will run inference and record evaluation episodes:

 ```bash
-lerobot-record \
-  --robot.type=so100_follower \
+lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=${HF_USER}/act_policy \
+  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \
-  --robot.id=my_robot \
  --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
  --display_data=true \
-  --dataset.repo_id=${HF_USER}/eval_act_your_dataset \
-  --dataset.num_episodes=10 \
-  --dataset.single_task="Your task description" \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.camera_encoder.vcodec=auto \
-  --policy.path=${HF_USER}/act_policy
+  --task="Your task description" \ # can be skipped for ACT
+  --duration=60
 ```
@@ -193,7 +193,7 @@ To learn more about training policies with LeRobot, please refer to the training

 - [SmolVLA](./smolvla)
 - [Pi0.5](./pi05)
- [GR00T N1.5](./groot)
+- [GR00T N1.7](./groot)

 Sample IsaacLab Arena datasets are available on HuggingFace Hub for experimentation:

@@ -1,16 +1,19 @@
-# GR00T N1.5 Policy
+# GR00T Policy

-GR00T N1.5 is an open foundation model from NVIDIA designed for generalized humanoid robot reasoning and skills. It is a cross-embodiment model that accepts multimodal input, including language and images, to perform manipulation tasks in diverse environments.
+GR00T is an NVIDIA foundation model family for generalized humanoid robot reasoning and skills. It is a cross-embodiment policy that accepts multimodal input, including language, images, and proprioception, to perform manipulation tasks in diverse environments.

-This document outlines the specifics of its integration and usage within the LeRobot framework.
+LeRobot integrates GR00T N1.7 through the `groot` policy type.
+
+> [!WARNING]
+> **Breaking change:** GR00T N1.5 support was removed from LeRobot, and current releases support GR00T N1.7 only. N1.5 checkpoints, configs, and `--policy.model_version=n1.5` are rejected with a clear error. To keep using an N1.5 checkpoint, pin the last release that supports it: `pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 (`model_version='n1.7'`, base model [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B)).

 ## Model Overview

-NVIDIA Isaac GR00T N1.5 is an upgraded version of the GR00T N1 foundation model. It is built to improve generalization and language-following abilities for humanoid robots.
+GR00T N1.7 uses a Cosmos-Reason2/Qwen3-VL backbone and provides checkpoints for SimplerEnv, DROID, and LIBERO.

-Developers and researchers can post-train GR00T N1.5 with their own real or synthetic data to adapt it for specific humanoid robots or tasks.
+Developers and researchers can post-train GR00T with their own real or synthetic data to adapt it for specific humanoid robots or tasks.

-GR00T N1.5 (specifically the GR00T-N1.5-3B model) is built using pre-trained vision and language encoders. It utilizes a flow matching action transformer to model a chunk of actions, conditioned on vision, language, and proprioception.
+GR00T uses pre-trained vision and language encoders with a flow matching action transformer to model a chunk of actions conditioned on vision, language, and proprioception.

 <img
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/lerobot-groot-paper1%20(1).png"
@@ -28,33 +31,46 @@ This approach allows the model to be highly adaptable through post-training for

 ## Installation Requirements

-As of today, GR00T N1.5 requires flash attention for it's internal working.
+GR00T is intended for NVIDIA GPU-accelerated systems. The `groot` extra still includes Flash Attention on non-macOS platforms, and Flash Attention needs a compatible PyTorch/CUDA environment before it is installed. Install the dependencies in this order:

-We are working on making this optional, but in the meantime that means that we require an extra installation step and it can only be used in CUDA enabled devices.
-
-1. Following the Environment Setup of our [Installation Guide](./installation). **Attention** don't install `lerobot` in this step.
-2. Install [Flash Attention](https://github.com/Dao-AILab/flash-attention) by running:
+1. Follow the Environment Setup in the [Installation Guide](./installation). Do not install `lerobot` yet.
+2. Install PyTorch, TorchVision, and the build dependencies used by Flash Attention:
+
+```bash
+# Check https://pytorch.org/get-started/locally/ for the right CUDA wheel index for your system.
+pip install "torch>=2.7,<2.12.0" "torchvision>=0.22.0,<0.27.0" \
+  --index-url https://download.pytorch.org/whl/cu128
+pip install "ninja>=1.11.1,<2.0.0" "packaging>=24.2,<26.0"
+```
+
+3. Install and verify Flash Attention:

 ```bash
-# Check https://pytorch.org/get-started/locally/ for your system
-pip install "torch>=2.2.1,<2.8.0" "torchvision>=0.21.0,<0.23.0" # --index-url https://download.pytorch.org/whl/cu1XX
-pip install ninja "packaging>=24.2,<26.0" # flash attention dependencies
 pip install "flash-attn>=2.5.9,<3.0.0" --no-build-isolation
 python -c "import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')"
 ```

-3. Install LeRobot by running:
+4. Install LeRobot with the GR00T extra:

 ```bash
-pip install lerobot[groot]
+pip install "lerobot[groot]"
 ```

+For a source checkout, use the same order, then install the local package with:
+
+```bash
+pip install -e ".[groot]"
+```
+
+If your CUDA/PyTorch build needs a different Flash Attention wheel or source build, follow the [Flash Attention project](https://github.com/Dao-AILab/flash-attention) instructions, but keep the same ordering: PyTorch first, Flash Attention next, then `lerobot[groot]`.
+
 ## Usage

-To use GR00T in your LeRobot configuration, specify the policy type as:
+To use GR00T N1.7:

-```python
-policy.type=groot
+```bash
+--policy.type=groot \
+--policy.model_version=n1.7
 ```

 ## Training
@@ -87,28 +103,63 @@ accelerate launch \

 ## Performance Results

-### Libero Benchmark Results
+### LIBERO Benchmark Results

 > [!NOTE]
-> Follow our instructions for Libero usage: [Libero](./libero)
+> Follow the [LIBERO](./libero) setup instructions before running `lerobot-eval`.

-GR00T has demonstrated strong performance on the Libero benchmark suite. To compare and test its LeRobot implementation, we finetuned the GR00T N1.5 model for 30k steps on the Libero dataset and compared the results to the GR00T reference results.
+GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To reproduce LeRobot results, follow the instructions in the [LIBERO](./libero) section.

-| Benchmark          | LeRobot Implementation | GR00T Reference |
-| ------------------ | ---------------------- | --------------- |
-| **Libero Spatial** | 82.0%                  | 92.0%           |
-| **Libero Object**  | 99.0%                  | 92.0%           |
-| **Libero Long**    | 82.0%                  | 76.0%           |
-| **Average**        | 87.0%                  | 87.0%           |
+### GR00T N1.7 LIBERO Checkpoints

-These results demonstrate GR00T's strong generalization capabilities across diverse robotic manipulation tasks. To reproduce these results, you can follow the instructions in the [Libero](https://huggingface.co/docs/lerobot/libero) section.
+NVIDIA publishes GR00T N1.7 LIBERO checkpoints at [`nvidia/GR00T-N1.7-LIBERO`](https://huggingface.co/nvidia/GR00T-N1.7-LIBERO), with one subdirectory per LIBERO suite:
+
+| Suite          | Checkpoint subdirectory |
+| -------------- | ----------------------- |
+| LIBERO Spatial | `libero_spatial`        |
+| LIBERO Object  | `libero_object`         |
+| LIBERO Goal    | `libero_goal`           |
+| LIBERO 10      | `libero_10`             |
+
+Preliminary LeRobot integration results:
+
+| Suite          | Status | Success rate | n_episodes |
+| -------------- | ------ | -----------: | ---------: |
+| LIBERO Spatial | ✓      |         ~95% |         XX |
+| LIBERO Object  | ✓      |          XX% |         XX |
+| LIBERO Goal    | ✓      |          XX% |         XX |
+| LIBERO 10      | ✓      |          XX% |         XX |
+| **Average**    | ✓      |      **XX%** |     **XX** |
+
+Replace the `XX` placeholders with final eval artifacts before merge.
+
+Download the suite checkpoint locally, then point `--policy.base_model_path` at the downloaded subdirectory. `--policy.path` is reserved for LeRobot checkpoints that contain a LeRobot `config.json` with a `type` field.
+
+```bash
+hf download nvidia/GR00T-N1.7-LIBERO \
+  --include "libero_spatial/*" \
+  --local-dir ./GR00T-N1.7-LIBERO
+
+lerobot-eval \
+  --policy.type=groot \
+  --policy.model_version=n1.7 \
+  --policy.base_model_path=./GR00T-N1.7-LIBERO/libero_spatial \
+  --policy.embodiment_tag=libero_sim \
+  --env.type=libero \
+  --env.task=libero_spatial \
+  --eval.n_episodes=50
+```
+
+Use `eval.n_episodes >= 50` per suite when reporting success rates.

 ### Evaluate in your hardware setup

-Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Imitation Learning for Robots](./il_robots). For example:
+Once you have trained your model using your parameters you can run inference in your downstream task. Follow the instructions in [Policy Deployment (lerobot-rollout)](./inference). For example:

 ```bash
-lerobot-record \
+lerobot-rollout\
+  --strategy.type=sentry \
+  --strategy.upload_every_n_episodes=5 \
  --robot.type=bi_so_follower \
  --robot.left_arm_port=/dev/ttyACM1 \
  --robot.right_arm_port=/dev/ttyACM0 \
@@ -119,16 +170,14 @@ lerobot-record \
  }' \
  --display_data=true \
  --dataset.repo_id=<user>/eval_groot-bimanual  \
-  --dataset.num_episodes=10 \
  --dataset.single_task="Grab and handover the red cube to the other arm" \
  --dataset.streaming_encoding=true \
  --dataset.encoder_threads=2 \
  # --dataset.camera_encoder.vcodec=auto \
  --policy.path=<user>/groot-bimanual \ # your trained model
-  --dataset.episode_time_s=30 \
-  --dataset.reset_time_s=10
+  --duration=600
 ```

 ## License

-This model follows NVIDIA's proprietary license, consistent with the original [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T). Future versions (starting from N1.7) will follow **Apache 2.0 License**.
+GR00T N1.7 is released under the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
@@ -68,13 +68,13 @@ from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
 from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig

 robot_config = SO101FollowerConfig(
-    port="/dev/tty.usbmodem58760431541",
-    id="my_red_robot_arm",
+    port="/dev/tty.usbmodem5AB90687491",
+    id="my_follower_arm",
 )

 teleop_config = SO101LeaderConfig(
-    port="/dev/tty.usbmodem58760431551",
-    id="my_blue_leader_arm",
+    port="/dev/tty.usbmodem5AB90689011",
+    id="my_leader_arm",
 )

 robot = SO101Follower(robot_config)
@@ -108,13 +108,13 @@ With `rerun`, you can teleoperate again while simultaneously visualizing the cam
 <hfoption id="Command">
 ```bash
 lerobot-teleoperate \
-    --robot.type=koch_follower \
-    --robot.port=/dev/tty.usbmodem58760431541 \
-    --robot.id=my_awesome_follower_arm \
-    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
-    --teleop.type=koch_leader \
-    --teleop.port=/dev/tty.usbmodem58760431551 \
-    --teleop.id=my_awesome_leader_arm \
+    --robot.type=so101_follower \
+    --robot.port=/dev/tty.usbmodem5AB90687491 \
+    --robot.id=my_follower_arm \
+    --robot.cameras="{front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --teleop.type=so101_leader \
+    --teleop.port=/dev/tty.usbmodem5AB90689011 \
+    --teleop.id=my_leader_arm \
    --display_data=true
 ```
 </hfoption>
@@ -122,34 +122,48 @@ lerobot-teleoperate \

 <!-- prettier-ignore-start -->
 ```python
+import time
+from lerobot.teleoperators.so_leader import SO101Leader, SO101LeaderConfig
+from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.teleoperators.koch_leader import KochLeader, KochLeaderConfig
-from lerobot.robots.koch_follower import KochFollower, KochFollowerConfig
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data, shutdown_rerun

-camera_config = {
-    "front": OpenCVCameraConfig(index_or_path=0, width=1920, height=1080, fps=30)
-}
-
-robot_config = KochFollowerConfig(
-    port="/dev/tty.usbmodem585A0076841",
-    id="my_red_robot_arm",
-    cameras=camera_config
+robot_config = SO101FollowerConfig(
+    port="/dev/tty.usbmodem5AB90687491",
+    id="my_follower_arm",
+    cameras={
+        "wrist": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+        "top": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30)
+    }
 )

-teleop_config = KochLeaderConfig(
-    port="/dev/tty.usbmodem58760431551",
-    id="my_blue_leader_arm",
+teleop_config = SO101LeaderConfig(
+    port="/dev/tty.usbmodem5AB90689011",
+    id="my_leader_arm",
 )

-robot = KochFollower(robot_config)
-teleop_device = KochLeader(teleop_config)
+init_rerun(session_name="teleoperation")
+
+robot = SO101Follower(robot_config)
+teleop_device = SO101Leader(teleop_config)
 robot.connect()
 teleop_device.connect()

+TARGET_HZ = 30
+TIME_PER_FRAME = 1.0 / TARGET_HZ
+
 while True:
+    start_time = time.perf_counter()
+
    observation = robot.get_observation()
    action = teleop_device.get_action()
    robot.send_action(action)
+    log_rerun_data(observation=observation, action=action)
+
+    elapsed_time = time.perf_counter() - start_time
+    sleep_time = TIME_PER_FRAME - elapsed_time
+    if sleep_time > 0:
+        time.sleep(sleep_time)
 ```
 <!-- prettier-ignore-end -->

@@ -202,10 +216,11 @@ lerobot-record \
 <!-- prettier-ignore-start -->
 ```python
 from lerobot.cameras.opencv import OpenCVCameraConfig
-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.feature_utils import hw_to_dataset_features
-from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
-from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
+from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
+from lerobot.teleoperators.so_leader.config_so_leader import SO101LeaderConfig
+from lerobot.teleoperators.so_leader.so_leader import SO101Leader
 from lerobot.common.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
@@ -218,71 +233,56 @@ EPISODE_TIME_SEC = 60
 RESET_TIME_SEC = 10
 TASK_DESCRIPTION = "My task description"

-# Create robot configuration
-robot_config = SO100FollowerConfig(
-    id="my_awesome_follower_arm",
-    cameras={
-        "front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS) # Optional: fourcc="MJPG" for troubleshooting OpenCV async error.
-    },
-    port="/dev/tty.usbmodem58760434471",
-)
-
-teleop_config = SO100LeaderConfig(
-    id="my_awesome_leader_arm",
-    port="/dev/tty.usbmodem585A0077581",
-)
-
-# Initialize the robot and teleoperator
-robot = SO100Follower(robot_config)
-teleop = SO100Leader(teleop_config)
-
-# Configure the dataset features
-action_features = hw_to_dataset_features(robot.action_features, "action")
-obs_features = hw_to_dataset_features(robot.observation_features, "observation")
-dataset_features = {**action_features, **obs_features}
-
-# Create the dataset
-dataset = LeRobotDataset.create(
-    repo_id="<hf_username>/<dataset_repo_id>",
-    fps=FPS,
-    features=dataset_features,
-    robot_type=robot.name,
-    use_videos=True,
-    image_writer_threads=4,
-)
-
-# Initialize the keyboard listener and rerun visualization
-_, events = init_keyboard_listener()
-init_rerun(session_name="recording")
-
-# Connect the robot and teleoperator
-robot.connect()
-teleop.connect()
-
-# Create the required processors
-teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
-
-episode_idx = 0
-while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
-
-    record_loop(
-        robot=robot,
-        events=events,
-        fps=FPS,
-        teleop_action_processor=teleop_action_processor,
-        robot_action_processor=robot_action_processor,
-        robot_observation_processor=robot_observation_processor,
-        teleop=teleop,
-        dataset=dataset,
-        control_time_s=EPISODE_TIME_SEC,
-        single_task=TASK_DESCRIPTION,
-        display_data=True,
+def main():
+    # Create robot configuration
+    robot_config = SO101FollowerConfig(
+        port="/dev/tty.usbmodem5AB90687491",
+        id="my_follower_arm",
+        cameras={
+            "wrist": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=30),
+            "top": OpenCVCameraConfig(index_or_path=1, width=640, height=480, fps=30)
+        }
    )

-    # Reset the environment if not stopping or re-recording
-    if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-        log_say("Reset the environment")
+    teleop_config = SO101LeaderConfig(
+        port="/dev/tty.usbmodem5AB90689011",
+        id="my_leader_arm",
+    )
+
+    # Initialize the robot and teleoperator
+    robot = SO101Follower(robot_config)
+    teleop = SO101Leader(teleop_config)
+
+    # Configure the dataset features
+    action_features = hw_to_dataset_features(robot.action_features, "action")
+    obs_features = hw_to_dataset_features(robot.observation_features, "observation")
+    dataset_features = {**action_features, **obs_features}
+
+    # Create the dataset
+    dataset = LeRobotDataset.create(
+        repo_id="<hf_username>/<dataset_repo_id>",
+        fps=FPS,
+        features=dataset_features,
+        robot_type=robot.name,
+        use_videos=True,
+        image_writer_threads=4,
+    )
+
+    # Initialize the keyboard listener and rerun visualization
+    _, events = init_keyboard_listener()
+    init_rerun(session_name="recording")
+
+    # Connect the robot and teleoperator
+    robot.connect()
+    teleop.connect()
+
+    # Create the required processors
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
+    episode_idx = 0
+    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+
        record_loop(
            robot=robot,
            events=events,
@@ -291,26 +291,50 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
            robot_action_processor=robot_action_processor,
            robot_observation_processor=robot_observation_processor,
            teleop=teleop,
-            control_time_s=RESET_TIME_SEC,
+            dataset=dataset,
+            control_time_s=EPISODE_TIME_SEC,
            single_task=TASK_DESCRIPTION,
            display_data=True,
        )

-    if events["rerecord_episode"]:
-        log_say("Re-recording episode")
-        events["rerecord_episode"] = False
-        events["exit_early"] = False
-        dataset.clear_episode_buffer()
-        continue
+        # Reset the environment if not stopping or re-recording
+        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
+            log_say("Reset the environment")
+            record_loop(
+                robot=robot,
+                events=events,
+                fps=FPS,
+                teleop_action_processor=teleop_action_processor,
+                robot_action_processor=robot_action_processor,
+                robot_observation_processor=robot_observation_processor,
+                teleop=teleop,
+                control_time_s=RESET_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+            )

-    dataset.save_episode()
-    episode_idx += 1
+        if events["rerecord_episode"]:
+            log_say("Re-recording episode")
+            events["rerecord_episode"] = False
+            events["exit_early"] = False
+            dataset.clear_episode_buffer()
+            continue

-# Clean up
-log_say("Stop recording")
-robot.disconnect()
-teleop.disconnect()
-dataset.push_to_hub()
+        dataset.save_episode()
+        episode_idx += 1
+
+    # finalize dataset
+    log_say("Finalizing dataset...")
+    dataset.finalize()
+    # Clean up
+    log_say("Stop recording")
+    robot.disconnect()
+    teleop.disconnect()
+    dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()
 ```
 <!-- prettier-ignore-end -->

@@ -348,7 +372,7 @@ The `record` function provides a suite of tools for capturing and managing data
 ##### 2. Checkpointing and Resuming

 - Checkpoints are automatically created during recording.
- If an issue occurs, you can resume by re-running the same command with `--resume=true`. When resuming a recording, `--dataset.num_episodes` must be set to the **number of additional episodes to be recorded**, and not to the targeted total number of episodes in the dataset !
+- If an issue occurs or you want to record additional episodes in the same dataset, you can resume by re-running the same command with `--resume=true`. When resuming a recording, `--dataset.num_episodes` must be set to the **number of additional episodes to be recorded**, and not to the targeted total number of episodes in the dataset! Make sure that you also set `--dataset.root="local_path"`, it's a local path to save the new part of the dataset and is required to resume.
 - To start recording from scratch, **manually delete** the dataset directory.

 ##### 3. Recording Parameters
@@ -422,7 +446,7 @@ from lerobot.utils.utils import log_say

 episode_idx = 0

-robot_config = SO100FollowerConfig(port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm")
+robot_config = SO100FollowerConfig(port="/dev/tty.usbmodem5AB90687491", id="my_follower_arm")

 robot = SO100Follower(robot_config)
 robot.connect()
@@ -490,6 +514,83 @@ Additionally you can provide extra `tags` or specify a `license` for your model

 If your local computer doesn't have a powerful GPU you could utilize Google Colab to train your model by following the [ACT training notebook](./notebooks#training-act).

+#### Train using Hugging Face Jobs
+
+Hugging Face jobs let's you easily select hardware and run the training in the cloud. So if you don't have a powerful GPU or you need more VRAM or just want to train a model much faster use HF Jobs! It's pay as you go and you simply pay for each second of use, you can see the pricing and additional information [here](https://huggingface.co/docs/hub/jobs).
+
+To run the training use this command:
+
+<hfoptions id="train_with_hf_jobs">
+<hfoption id="Command">
+```bash
+hf jobs run \
+  --flavor a10g-small \
+  --timeout 4h \
+  --secrets HF_TOKEN \
+  huggingface/lerobot-gpu:latest \
+  -- \
+  python -m lerobot.scripts.lerobot_train \
+    --dataset.repo_id=username/dataset \
+    --policy.type=act \
+    --steps=5000 \
+    --batch_size=16 \
+    --policy.device=cuda \
+    --policy.repo_id=username/your_policy \
+    --log_freq=100
+```
+</hfoption>
+<hfoption id="API example">
+
+<!-- prettier-ignore-start -->
+```python
+from huggingface_hub import run_job, get_token
+
+run_name = "act_so101_hf_jobs"
+dataset_id = "username/dataset"
+user_hub_id = "username"
+
+command_args = [
+    "python", "-m", "lerobot.scripts.lerobot_train",
+    "--dataset.repo_id", dataset_id,
+    "--policy.type", "act",
+    "--steps", "5000",
+    "--batch_size", "16",
+    "--num_workers", "4",
+    "--policy.device", "cuda",
+    "--log_freq", "100",
+    "--save_freq", "1000",
+    "--save_checkpoint", "true",
+    "--wandb.enable", "false",
+    "--policy.repo_id", f"{user_hub_id}/{run_name}"
+]
+
+print(f"Submitting job '{run_name}' to Hugging Face Infrastructure...")
+
+job_info = run_job(
+    image="huggingface/lerobot-gpu:latest",
+    command=command_args,
+    flavor="a10g-small",
+    timeout="4h",
+    secrets={"HF_TOKEN": get_token()}
+)
+
+print("\n🚀 Job successfully launched!")
+print(f"🔹 Job ID: {job_info.id}")
+print(f"🔗 Live UI Dashboard & Logs: {job_info.url}")
+```
+<!-- prettier-ignore-end -->
+
+</hfoption>
+</hfoptions>
+
+You can modify the `--flavor` to use different hardware, for example: `t4-small`, `a100-large`, `h200`. Use `hf jobs hardware` to see the full list with pricing.
+Depending on the model you want to train and the hardware you selected you can also modify the `--batch_size` and `--number_of_workers`.
+For longer training sessions increase the timeout.
+
+Once the training is started you can go to [Jobs](https://huggingface.co/settings/jobs) and see if your jobs is running as well as all the outputs. Sometimes it takes a few minutes to schedule your job so be patient.
+
+After training the model will be pushed to hub and you can use it as any other model with LeRobot.
+
 #### Upload policy checkpoints

 Once training is done, upload the latest checkpoint with:
@@ -0,0 +1,29 @@
+# LeLab - LeRobot Guide
+
+LeLab is a graphical user interface built on top of the LeRobot library, designed to make robotics accessible without needing to memorize CLI commands. From a single app you can configure your robot, teleoperate it, collect datasets, train policies locally or on cloud GPUs via HF Jobs, and deploy trained models back onto your robot. It's the easiest way to go from an unboxed SO-101 to a working policy, and a great companion for anyone learning the LeRobot workflow. Source code and issues live on GitHub: [huggingface/leLab](https://github.com/huggingface/leLab).
+
+> [!TIP]
+> For now LeLab is compatible only with SO-ARM101
+
+<Youtube id="VqyKUuW9V1g" />
+
+### Installation
+
+Requires [`uv`](https://docs.astral.sh/uv/getting-started/installation/). Install and launch in one command:
+
+```
+uv tool install git+https://github.com/huggingface/leLab.git && lelab
+```
+
+After install, run `lelab` from your terminal anytime to start the app.
+
+### Features
+
+- **Add robots** — Select arm type (leader/follower), calibrate each joint from the middle position, and attach cameras.
+- **Teleoperation** — Control the follower arm with the leader and see a live 3D visualization of the arms.
+- **Dataset recording** — Define a task description, number of episodes, and episode/reset durations. Press spacebar to advance between episodes. 30+ episodes recommended.
+- **Local training** — Train a policy directly on your own machine with a selected dataset, policy type, batch size, and step count.
+- **Cloud training with HF Jobs** — Train on powerful GPUs via [HF Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) with transparent pricing. Run `hf auth login` first. See the [Compute HW Guide](hardware_guide) for hardware/batch size tips.
+- **Training visualization** — Watch progress live in the app, with checkpoints saved automatically.
+- **Run trained policies** — Pick any model from your jobs list and run inference on your robot with one click.
+- **Use community datasets** — Provide any Hugging Face dataset ID to train on datasets you didn't record yourself.
@@ -275,7 +275,7 @@ A converter aggregates per‑episode files into larger shards and writes episode
 pip install "https://github.com/huggingface/lerobot/archive/33cad37054c2b594ceba57463e8f11ee374fa93c.zip"

 # Convert an existing v2.1 dataset hosted on the Hub:
-python -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID>
+python -m lerobot.scripts.convert_dataset_v21_to_v30 --repo-id=<HF_USER/DATASET_ID>
 ```

 **What it does**
@@ -0,0 +1,433 @@
+# MolmoAct2 Policy
+
+MolmoAct2 is the LeRobot policy implementation of
+[MolmoAct2](https://allenai.org/blog/molmoact2), ported into the LeRobot
+training, evaluation, checkpointing, and dataset interfaces for easier use with
+LeRobot datasets.
+
+This implementation currently supports training and evaluation for the regular
+MolmoAct2 model. MolmoAct2-Think, which supports adaptive depth reasoning, is
+not included in this LeRobot policy yet and is coming soon.
+
+For the original MolmoAct2 training code used for the experiments reported in
+the paper, see [allenai/molmoact2](https://github.com/allenai/molmoact2).
+
+## Installation Requirements
+
+Install LeRobot with the MolmoAct2 optional dependencies:
+
+```bash
+pip install -e ".[molmoact2]"
+```
+
+To run the models in this repository, you need an NVIDIA GPU. The measurements
+below were taken on a single NVIDIA H100 80GB with bf16 model loading, LIBERO with two RGB cameras. MolmoAct2 rows use `chunk_size=10`, action dim 7
+padded to `expected_max_action_dim=32`, and `num_flow_timesteps=8`. Training measurements use
+`gradient_checkpointing=true` and include the forward pass, backward pass,
+gradient clipping, optimizer step, and optimizer state allocation. Values are
+peak GPU memory sampled with `nvidia-smi`. Leave a few GiB of headroom for
+dataloader workers, CUDA context, and fragmentation.
+
+Multi-GPU training through `accelerate` increases throughput and global batch
+size, but this LeRobot port does not currently expose the original MolmoAct2
+`fsdp_devices` model-parallel training path. The current training script has
+not been tested for multi-node training.
+
+| Mode                                             | Peak Memory, bs=8 | Peak Memory, bs=16 | Peak Memory, bs=32 |
+| ------------------------------------------------ | ----------------: | -----------------: | -----------------: |
+| Inference, continuous, CUDA graph enabled (bs=1) |          12.1 GiB |                  - |                  - |
+| Fine-tuning, action expert only, continuous      |          16.5 GiB |           18.3 GiB |           21.4 GiB |
+| Fine-tuning, LoRA VLM, both action modes         |          20.2 GiB |           26.8 GiB |           41.3 GiB |
+| Fine-tuning, full model, both action modes       |          48.3 GiB |           49.8 GiB |           60.1 GiB |
+
+The repo has been tested with Ubuntu 22.04.
+
+## Usage
+
+To use MolmoAct2 in a LeRobot training config, set:
+
+```python
+policy.type=molmoact2
+```
+
+## Training
+
+MolmoAct2 can be fine-tuned from either the released MolmoAct2 Hugging Face
+checkpoint format or from a checkpoint already saved by LeRobot. Both routes use
+the same LeRobot training loop, dataset transforms, checkpoint saving, and
+logging. The difference is only how the initial policy weights and processor
+state are loaded.
+
+### Training With Original MolmoAct2 Weight
+
+Use `policy.checkpoint_path` when starting from a released MolmoAct2 checkpoint,
+for example `allenai/MolmoAct2` or `allenai/MolmoAct2-LIBERO`. LeRobot will load
+the original HF model files, then build its own policy processor from the
+dataset metadata and the policy options below.
+
+The command below shows full fine-tuning on the merged LIBERO dataset. It uses
+bf16 model loading, 8 flow timesteps, LeRobot dataset statistics, image
+augmentation, and LeRobot's checkpointing/logging path.
+
+```bash
+accelerate launch \
+  --num_processes=8 \
+  --mixed_precision=bf16 \
+  -m lerobot.scripts.lerobot_train \
+  --dataset.repo_id=allenai/MolmoAct2-LIBERO-Dataset \
+  --dataset.root=/path/to/lerobot/data/allenai/MolmoAct2-LIBERO-Dataset \
+  --dataset.video_backend=pyav \
+  --dataset.image_transforms.enable=true \
+  --policy.type=molmoact2 \
+  --policy.checkpoint_path=allenai/MolmoAct2-LIBERO \
+  --policy.device=cuda \
+  --policy.action_mode=both \
+  --policy.chunk_size=10 \
+  --policy.n_action_steps=10 \
+  --policy.setup_type="single franka robotic arm in libero" \
+  --policy.control_mode="delta end-effector pose" \
+  --policy.image_keys='["observation.images.image","observation.images.wrist_image"]' \
+  --policy.model_dtype=bfloat16 \
+  --policy.num_flow_timesteps=8 \
+  --policy.gradient_checkpointing=true \
+  --policy.freeze_embedding=true \
+  --policy.normalize_gripper=false \
+  --policy.enable_knowledge_insulation=false \
+  --policy.push_to_hub=false \
+  --wandb.enable=true \
+  --wandb.entity=<wandb_entity> \
+  --wandb.project=<wandb_project> \
+  --job_name=<job_name> \
+  --output_dir=outputs/<job_name> \
+  --steps=10000 \
+  --batch_size=32 \
+  --num_workers=4 \
+  --log_freq=20 \
+  --eval_freq=-1 \
+  --save_checkpoint=true \
+  --save_freq=2000
+```
+
+### Training With LeRobot MolmoAct2 Weight
+
+Use `policy.path` when starting from a MolmoAct2 checkpoint that was saved by
+LeRobot, either from a local `pretrained_model` directory or from the Hub. This
+restores the saved LeRobot policy config, model weights, processor, and
+normalization statistics. You can still override training-time options such as
+`batch_size`, `steps`, LoRA flags, or `policy.action_mode`.
+
+```bash
+accelerate launch \
+  --num_processes=8 \
+  --mixed_precision=bf16 \
+  -m lerobot.scripts.lerobot_train \
+  --dataset.repo_id=allenai/MolmoAct2-LIBERO-Dataset \
+  --dataset.root=/path/to/lerobot/data/allenai/MolmoAct2-LIBERO-Dataset \
+  --dataset.video_backend=pyav \
+  --dataset.image_transforms.enable=true \
+  --policy.path=/path/to/pretrained_model \
+  --policy.device=cuda \
+  --policy.action_mode=both \
+  --policy.chunk_size=10 \
+  --policy.n_action_steps=10 \
+  --policy.model_dtype=bfloat16 \
+  --policy.num_flow_timesteps=8 \
+  --policy.gradient_checkpointing=true \
+  --wandb.enable=true \
+  --wandb.entity=<wandb_entity> \
+  --wandb.project=<wandb_project> \
+  --job_name=<job_name> \
+  --output_dir=outputs/<job_name> \
+  --steps=10000 \
+  --batch_size=32 \
+  --num_workers=4 \
+  --log_freq=20 \
+  --eval_freq=-1 \
+  --save_checkpoint=true \
+  --save_freq=2000
+```
+
+### Common Practices
+
+For fine-tuning on a comparatively small dataset, such as a single LIBERO suite
+or a real-world dataset with less than 200 demonstrations, a global batch size of
+16 to 32 is a good starting point. In these settings, `policy.enable_lora_vlm=true` or `policy.train_action_expert_only=true` is also a practical choice. In both
+cases, we intentionally keep the action expert fully trainable, which we found
+to be crucial for model performance. For larger fine-tuning datasets, larger
+global batch sizes and full fine-tuning are usually preferred.
+
+### Common Policy Options
+
+- `policy.checkpoint_path`: original MolmoAct2 HF checkpoint to initialize from.
+  Use this for released MolmoAct2 weights.
+- `policy.path`: LeRobot checkpoint to initialize from. Use this for checkpoints
+  created by LeRobot training.
+- `policy.action_mode`: training target, one of `continuous`, `discrete`, or
+  `both`. `both` trains the flow-matching action expert and the discrete
+  action-token loss.
+- `policy.train_action_expert_only`: trains only parameters whose names contain
+  `action_expert`. It requires `policy.action_mode=continuous`.
+- `policy.enable_lora_vlm`: enables LoRA on VLM linear layers. Use
+  `policy.enable_lora_action_expert=true` only if LoRA should also cover action
+  expert linear layers. When `policy.enable_lora_action_expert=false`, the
+  action expert base weights remain fully trainable while the VLM is trained
+  through LoRA adapters. When `policy.enable_lora_action_expert=true`, the
+  action expert is also adapter-tuned instead of fully fine-tuned.
+- `policy.enable_knowledge_insulation`: when `true`, detaches action-expert
+  context K/V states before the action loss. The default is `false`.
+- `policy.chunk_size`: action horizon used by the policy. For LIBERO we use
+  `10`. This LeRobot port overrides the loaded checkpoint's
+  `max_action_horizon` with this value.
+- `policy.n_action_steps`: number of actions consumed from each predicted
+  chunk before querying the policy again. For LIBERO, set it to `chunk_size`.
+- `policy.setup_type`: text inserted into the prompt to describe the robot and
+  scene, e.g. `single franka robotic arm in libero`. More examples are listed
+  in the `metadata_by_tag` entries of
+  [`norm_stats.json`](https://huggingface.co/allenai/MolmoAct2/blob/main/norm_stats.json).
+- `policy.control_mode`: text inserted into the prompt to describe the action
+  space, e.g. `delta end-effector pose` or `absolute joint pose`.
+- `policy.image_keys`: ordered LeRobot image observation keys passed to the
+  processor.
+- `policy.model_dtype`: checkpoint/forward dtype, one of `float32`,
+  `bfloat16`, or `float16`. Use `bfloat16` for normal training.
+- `policy.num_flow_timesteps`: number of flow-matching timesteps sampled per
+  example during training. We use `8` for fine-tuning.
+- `policy.num_inference_steps`: optional override for continuous action
+  generation steps at inference time.
+- `policy.gradient_checkpointing`: enables checkpointing in the VLM/action path
+  to reduce activation memory.
+- `policy.freeze_embedding`: freezes input embeddings. The default is `true`.
+- `policy.normalize_gripper`: controls whether gripper dimensions are included
+  in state/action quantile normalization. The default is `false`.
+- `policy.normalize_language`: normalizes task strings before prompt
+  construction. The default is `true`.
+- `policy.mask_action_dim_padding`: masks padded dimensions in the flow loss.
+  Released checkpoints use `policy.expected_max_action_dim=32`.
+- `policy.max_sequence_length`: optional manual sequence cap. Leave unset to
+  infer it from images, state dimension, action dimension, action horizon, and
+  discrete-action mode.
+
+### Learning Rates
+
+MolmoAct2 uses parameter-group learning rates to match the original MolmoAct2
+fine-tuning experiments.
+
+- Full fine-tuning uses `policy.optimizer_lr=1e-5` for the VLM,
+  `policy.optimizer_vit_lr=5e-6` for the vision tower,
+  `policy.optimizer_connector_lr=5e-6` for image connector layers, and
+  `policy.optimizer_action_expert_lr=5e-5` for the action expert.
+- LoRA VLM fine-tuning sets the VLM, vision, and connector LoRA parameter
+  groups to `5e-5` when `policy.enable_lora_vlm=true`. By default,
+  `policy.enable_lora_action_expert=false`, so the action expert is still fully
+  fine-tuned with `policy.optimizer_action_expert_lr`. If
+  `policy.enable_lora_action_expert=true`, the action expert is trained through
+  LoRA adapters instead.
+- Action-expert-only fine-tuning trains only the action expert and uses
+  `policy.optimizer_action_expert_lr=5e-5`.
+
+You can override the full fine-tuning and action-expert learning rates with
+`policy.optimizer_lr`, `policy.optimizer_vit_lr`,
+`policy.optimizer_connector_lr`, and `policy.optimizer_action_expert_lr`.
+Scheduler settings can be changed with `policy.scheduler_warmup_steps`,
+`policy.scheduler_decay_steps`, and `policy.scheduler_decay_lr`.
+
+### Dataset Quantile Statistics
+
+MolmoAct2 defaults to quantile normalization for state and action features. If
+your dataset has not been converted with quantile statistics, you can add them
+with:
+
+```bash
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
+  --repo-id=your_dataset
+```
+
+Alternatively, train MolmoAct2 with mean/std normalization:
+
+```bash
+--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'
+```
+
+## Evaluation
+
+Evaluation also supports both LeRobot-saved checkpoints and original MolmoAct2
+HF checkpoints. For LIBERO replication, keep the EGL rendering environment
+fixed and use `policy.per_episode_seed=true`.
+
+**Important:** We found that `num_steps_wait=10` does not reliably let the
+LIBERO scene stabilize and can degrade measured success. All LIBERO evaluation
+results reported here use `num_steps_wait=50`.
+
+### Evaluation With LeRobot MolmoAct2 Weight
+
+Use `policy.path` for a checkpoint saved by LeRobot. The saved processor and
+normalization statistics are restored together with the model.
+
+```bash
+export MUJOCO_GL=egl
+export PYOPENGL_PLATFORM=egl
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+
+lerobot-eval \
+  --policy.path=allenai/MolmoAct2-LIBERO-LeRobot \
+  --policy.inference_action_mode=continuous \
+  --policy.model_dtype=bfloat16 \
+  --policy.use_amp=true \
+  --policy.enable_inference_cuda_graph=true \
+  --policy.device=cuda \
+  --policy.per_episode_seed=true \
+  --policy.eval_seed=1000 \
+  --env.type=libero \
+  --env.task=libero_10,libero_goal,libero_object,libero_spatial \
+  --env.camera_name_mapping='{"agentview_image":"image","robot0_eye_in_hand_image":"wrist_image"}' \
+  --eval.batch_size=1 \
+  --eval.n_episodes=50 \
+  --seed=1000
+```
+
+### Evaluation With Original MolmoAct2 Weight
+
+You can evaluate a released Hugging Face checkpoint directly without first
+converting it to a LeRobot checkpoint. In this case, set
+`policy.checkpoint_path` to the HF model repo and provide `policy.norm_tag`.
+For LIBERO, `policy.norm_tag=libero` loads the LIBERO action/state
+normalization statistics, action horizon, prompt metadata, and image-key order
+from the checkpoint's `norm_stats.json`.
+
+To fully replicate the MolmoAct2 paper results with released Hugging Face
+checkpoints, we recommend using the v0.5.1-pinned
+[`allenai/lerobot` `molmoact2-hf-inference`](https://github.com/allenai/lerobot/tree/molmoact2-hf-inference)
+branch. That branch matches the original evaluation settings used for the
+reported numbers.
+
+```bash
+export MUJOCO_GL=egl
+export PYOPENGL_PLATFORM=egl
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+
+lerobot-eval \
+  --policy.type=molmoact2 \
+  --policy.checkpoint_path=allenai/MolmoAct2-LIBERO \
+  --policy.norm_tag=libero \
+  --policy.inference_action_mode=continuous \
+  --policy.model_dtype=float32 \
+  --policy.use_amp=false \
+  --policy.enable_inference_cuda_graph=true \
+  --policy.device=cuda \
+  --policy.per_episode_seed=true \
+  --policy.eval_seed=1000 \
+  --env.type=libero \
+  --env.task=libero_goal \
+  --env.camera_name_mapping='{"agentview_image":"image","robot0_eye_in_hand_image":"wrist_image"}' \
+  --eval.batch_size=1 \
+  --eval.n_episodes=50 \
+  --seed=1000
+```
+
+Use `--env.task=libero_10,libero_goal,libero_object,libero_spatial` to run the
+full LIBERO suite. The same command works for other released MolmoAct2
+checkpoints as long as the requested `policy.norm_tag` exists in that
+checkpoint's `norm_stats.json`.
+
+### Common Evaluation Options
+
+- `policy.inference_action_mode`: required for rollout. Use `continuous` for
+  flow-matching inference or `discrete` for action-token inference. It must be
+  compatible with the training-time `policy.action_mode` saved in the
+  checkpoint.
+- `policy.path`: LeRobot checkpoint path or Hub repo. Use this for checkpoints
+  saved by LeRobot.
+- `policy.checkpoint_path`: original MolmoAct2 HF checkpoint path or Hub repo.
+  Use this with `policy.type=molmoact2` and `policy.norm_tag`.
+- `policy.norm_tag`: selects normalization statistics, prompt metadata,
+  image-key order, and action horizon from the original checkpoint's
+  `norm_stats.json`. It is required for direct original-HF checkpoint
+  evaluation.
+- `policy.model_dtype`: model load/forward dtype. Use `bfloat16` for normal
+  GPU evaluation. Use `float32` only when you explicitly want fp32 inference.
+- `policy.use_amp`: runs the policy forward under autocast during eval. For
+  `model_dtype=bfloat16`, keep this enabled.
+- `policy.enable_inference_cuda_graph`: enables the MolmoAct2 inference CUDA
+  graph path for faster repeated continuous-action rollout.
+- `policy.per_episode_seed` and `policy.eval_seed`: make stochastic continuous
+  action generation deterministic per episode for replication.
+- `env.task`: comma-separated LIBERO suites or a single suite. Use
+  `libero_10,libero_goal,libero_object,libero_spatial` for the full benchmark.
+- `env.camera_name_mapping`: maps LIBERO camera names to the image keys expected
+  by the policy processor.
+
+## Performance Results
+
+### LIBERO Benchmark Results
+
+MolmoAct2 has demonstrated strong performance on the LIBERO benchmark suite. To
+compare and test its LeRobot implementation, we fine-tuned
+[`allenai/MolmoAct2-LIBERO`](https://huggingface.co/allenai/MolmoAct2-LIBERO)
+for an additional 10k steps on the LIBERO dataset with per-GPU batch size 32 on
+8 H100 GPUs, then compared the results to the original MolmoAct2 reference
+results.
+
+The LeRobot fine-tuned checkpoint reported here is available at
+[`allenai/MolmoAct2-LIBERO-LeRobot`](https://huggingface.co/allenai/MolmoAct2-LIBERO-LeRobot)
+and was trained on
+[`allenai/MolmoAct2-LIBERO-Dataset`](https://huggingface.co/datasets/allenai/MolmoAct2-LIBERO-Dataset).
+
+| Benchmark      | LeRobot Implementation | MolmoAct2 Original |
+| -------------- | ---------------------: | -----------------: |
+| LIBERO Spatial |                  98.4% |              97.8% |
+| LIBERO Object  |                 100.0% |             100.0% |
+| LIBERO Goal    |                  98.0% |              97.8% |
+| LIBERO 10      |                  96.6% |              93.2% |
+| Average        |                 98.25% |             97.20% |
+
+These results demonstrate MolmoAct2's strong performance across diverse robotic
+manipulation tasks. To reproduce them, follow the instructions in the LIBERO
+evaluation section.
+
+## Differences From the Original Implementation
+
+This LeRobot port is intended to match MolmoAct2 behavior while using LeRobot's
+dataset, training, evaluation, checkpoint, and logging infrastructure. The main
+differences from the original training repository are:
+
+- The original paper training stack loads the model in fp32 and trains under
+  mixed precision. This LeRobot port usually loads the checkpoint directly in
+  `policy.model_dtype=bfloat16` for lower memory use.
+- The original repository uses its own FSDP/model-parallel training path. The
+  LeRobot port uses the standard LeRobot/Accelerate training path and has not
+  been tested for multi-node training.
+- The original repository supports sequence packing. The LeRobot port trains on
+  one LeRobot sample per item and pads to an inferred fixed sequence budget.
+- The LeRobot port follows LeRobot's optimizer, scheduler, checkpoint saving,
+  dataset transforms, image augmentation, and Weights & Biases logging
+  conventions.
+- The original training path supports mixed action horizons by padding to
+  `max_action_horizon` and masking padded horizon slots in the action expert
+  self-attention. This is useful when training across datasets with different
+  control frequencies. The LeRobot port currently targets single-dataset
+  fine-tuning, so `policy.chunk_size` overrides the checkpoint
+  `max_action_horizon` and horizon masking is not implemented yet. Support for
+  this mixed-horizon path is planned.
+
+## Citation
+
+```bibtex
+@misc{fang2026molmoact2actionreasoningmodels,
+      title={MolmoAct2: Action Reasoning Models for Real-world Deployment},
+      author={Haoquan Fang and Jiafei Duan and Donovan Clay and Sam Wang and Shuo Liu and Weikai Huang and Xiang Fan and Wei-Chuan Tsai and Shirui Chen and Yi Ru Wang and Shanli Xing and Jaemin Cho and Jae Sung Park and Ainaz Eftekhar and Peter Sushko and Karen Farley and Angad Wadhwa and Cole Harrison and Winson Han and Ying-Chun Lee and Eli VanderBilt and Rose Hendrix and Suveen Ellawela and Lucas Ngoo and Joyce Chai and Zhongzheng Ren and Ali Farhadi and Dieter Fox and Ranjay Krishna},
+      year={2026},
+      eprint={2605.02881},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2605.02881},
+}
+```
+
+## License
+
+This model is licensed under Apache 2.0. It is intended for research and
+educational use in accordance with
+[Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use),
+consistent with [allenai/molmoact2](https://github.com/allenai/molmoact2).
@@ -91,7 +91,7 @@ lerobot-train \
 If your dataset is not converted with `quantiles`, you can convert it with the following command:

 ```bash
-python src/lerobot/datasets/v30/augment_dataset_quantile_stats.py \
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
    --repo-id=your_dataset \
 ```

@@ -1,6 +1,13 @@
 ## Research Paper

-Paper: https://research.nvidia.com/labs/gear/gr00t-n1_5/
+GR00T N1 technical report (covers the GR00T N1.x family, including N1.7): https://arxiv.org/abs/2503.14734
+
+GR00T N1.7 model card: https://huggingface.co/nvidia/GR00T-N1.7-3B
+
+GR00T N1.5 research page (earlier version): https://research.nvidia.com/labs/gear/gr00t-n1_5/
+
+> GR00T N1.5 support was removed from LeRobot; the last release supporting it is `lerobot==0.5.1`.
+> Current releases support GR00T N1.7 only.

 ## Repository

@@ -24,4 +31,103 @@ Code: https://github.com/NVIDIA/Isaac-GR00T

 Blog: https://developer.nvidia.com/isaac/gr00t

-Hugging Face Model: https://huggingface.co/nvidia/GR00T-N1.5-3B
+Hugging Face Models:
+
+- GR00T N1.7: https://huggingface.co/nvidia/GR00T-N1.7-3B
+- GR00T N1.7 LIBERO checkpoints: https://huggingface.co/nvidia/GR00T-N1.7-LIBERO
+
+## Original-vs-LeRobot parity test
+
+`tests/policies/groot/test_groot_vs_original.py` verifies this LeRobot
+reimplementation of GR00T N1.7 (Qwen3-VL backbone + flow-matching action head)
+against NVIDIA's original `gr00t` package with two comparisons, each parametrized
+over every embodiment tag present in the checkpoint:
+
+1. **Model parity** — given byte-identical pre-processed inputs and the same
+   flow-matching seed (recorded in each artifact), both implementations must produce
+   the **same raw model output** (`get_action(...)["action_pred"]`, the normalized
+   flow-matching prediction). Output shapes must match exactly; any action-horizon
+   or action-dim mismatch fails the test.
+2. **Preprocessor parity** — given the identical raw observations (per-camera
+   frames, state vectors, language instruction), LeRobot's own preprocessor pipeline
+   (real Qwen3-VL chat template / tokenizer / image packing + checkpoint-driven
+   state normalization, no mocks) must produce the **same collated model inputs**
+   (`input_ids`, `attention_mask`, `pixel_values`, `image_grid_thw`, `state`,
+   `embodiment_id`) as the original package's processor.
+
+### Why two environments
+
+The original `gr00t` package pins `transformers==4.57.3` (Python 3.10); this
+integration requires `transformers>=5.x` (Qwen3-VL). Under 5.x, `PretrainedConfig`
+is itself a defaulted dataclass, so the original config dataclasses fail to import
+(`non-default argument follows default argument`). The two implementations therefore
+**cannot be imported in the same Python process**.
+
+So the test uses a **producer / consumer** split across two venvs:
+
+1. **Producer** — `tests/policies/groot/utils/dump_original_n1_7.py`, run in the _original_
+   gr00t venv. For each embodiment it builds dummy inputs generically from the
+   checkpoint metadata (state dims from `statistics.json`; camera/language keys from
+   the processor modality configs), runs the original model, and saves to one `.npz`
+   per tag: the raw observations (`raw::` keys), the exact collated inputs
+   (`in::` keys), the seed, and the raw `action_pred`.
+2. **Consumer** — the pytest above, run in the _LeRobot_ venv. It discovers every
+   `.npz`; the model-parity case replays the byte-identical collated inputs through
+   the LeRobot model with the recorded seed and asserts the outputs match, and the
+   preprocessor-parity case replays the raw observations through LeRobot's full
+   preprocessor pipeline and asserts the collated tensors match.
+
+> Artifacts generated by older versions of the dump script contain no `raw::`
+> fields; the preprocessor-parity case then **skips** with a regeneration hint.
+> Re-run the producer to refresh them.
+
+### Fairness controls
+
+- **Same pre-processed inputs (model parity)** — the original processor's `input_ids`,
+  `pixel_values`, `image_grid_thw`, `attention_mask`, `state`, `embodiment_id` are
+  fed verbatim to the LeRobot model (no re-tokenization / re-normalization), so the
+  model comparison isolates the model. LeRobot's own tokenization / image packing is
+  covered separately by the preprocessor-parity case, which compares its output
+  against those same collated tensors from identical raw observations.
+- **Same precision + attention kernel** — both sides run **fp32 + SDPA**. The
+  original defaults to `use_flash_attention=True` (flash_attention_2 + bf16); the
+  producer forces SDPA + fp32. (With the defaults the gap is ~3e-2 — pure
+  kernel/rounding noise, not an implementation difference.)
+- **Same flow-matching seed** — fixed right before sampling on both sides; the
+  producer records it in each artifact (`--seed`, default 42) and the consumer
+  replays the recorded value.
+
+### How to run
+
+```bash
+# Resolve a local checkpoint (GR00T-N1.7-LIBERO / libero_10)
+CKPT=$(python - <<'PY'
+import os
+from huggingface_hub import snapshot_download
+print(os.path.join(snapshot_download("nvidia/GR00T-N1.7-LIBERO",
+      allow_patterns=["libero_10/*"]), "libero_10"))
+PY
+)
+
+# 1) Produce the original-side artifacts for all embodiments (original gr00t venv, CUDA)
+CUDA_VISIBLE_DEVICES=0 /path/to/Isaac-GR00T/.venv-original/bin/python \
+    tests/policies/groot/utils/dump_original_n1_7.py \
+    --ckpt "$CKPT" --out-dir tests/policies/groot/artifacts --device cuda --seed 42
+
+# 2) Run the parity test (LeRobot venv) — one parametrized case per embodiment
+CUDA_VISIBLE_DEVICES=0 GROOT_PARITY_DEVICE=cuda \
+    uv run pytest tests/policies/groot/test_groot_vs_original.py -v -s
+```
+
+The `.npz` artifacts are local-only (gitignored, ~6–10 MB each) and are regenerated by
+the producer; they are never committed. The tests **skip** (do not fail) on CI or
+when the checkpoint / artifacts are absent.
+
+#### Env knobs (all optional)
+
+| Var                                       | Default                          | Purpose                               |
+| ----------------------------------------- | -------------------------------- | ------------------------------------- |
+| `GROOT_N1_7_PARITY_DIR`                   | `tests/policies/groot/artifacts` | directory of per-tag `.npz` artifacts |
+| `GROOT_N1_7_LIBERO_CKPT`                  | auto (HF cache)                  | override checkpoint dir               |
+| `GROOT_PARITY_DEVICE`                     | `cuda` if available              | `cpu` or `cuda`                       |
+| `GROOT_PARITY_ATOL` / `GROOT_PARITY_RTOL` | `1e-3`                           | comparison tolerance                  |
@@ -0,0 +1,39 @@
+# MolmoAct2
+
+This repository contains the LeRobot policy implementation of
+[MolmoAct2](https://allenai.org/blog/molmoact2), ported into LeRobot for
+training, evaluation, checkpointing, and dataset compatibility.
+
+This implementation currently supports training and evaluation for the regular
+MolmoAct2 model. MolmoAct2-Think, which supports adaptive depth reasoning, is
+not included in this LeRobot policy yet and is coming soon.
+
+For the original MolmoAct2 training code used for the experiments reported in
+the paper, see [allenai/molmoact2](https://github.com/allenai/molmoact2).
+
+## LIBERO Evaluation
+
+Important: we found that `num_steps_wait=10` does not reliably let the LIBERO
+scene stabilize and can degrade measured success. All LIBERO evaluation results
+reported for this LeRobot implementation use `num_steps_wait=50`.
+
+## Citation
+
+```bibtex
+@misc{fang2026molmoact2actionreasoningmodels,
+      title={MolmoAct2: Action Reasoning Models for Real-world Deployment},
+      author={Haoquan Fang and Jiafei Duan and Donovan Clay and Sam Wang and Shuo Liu and Weikai Huang and Xiang Fan and Wei-Chuan Tsai and Shirui Chen and Yi Ru Wang and Shanli Xing and Jaemin Cho and Jae Sung Park and Ainaz Eftekhar and Peter Sushko and Karen Farley and Angad Wadhwa and Cole Harrison and Winson Han and Ying-Chun Lee and Eli VanderBilt and Rose Hendrix and Suveen Ellawela and Lucas Ngoo and Joyce Chai and Zhongzheng Ren and Ali Farhadi and Dieter Fox and Ranjay Krishna},
+      year={2026},
+      eprint={2605.02881},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2605.02881},
+}
+```
+
+## License
+
+This model is licensed under Apache 2.0. It is intended for research and
+educational use in accordance with
+[Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use),
+consistent with [allenai/molmoact2](https://github.com/allenai/molmoact2).
@@ -0,0 +1,39 @@
+# VLA-JEPA
+
+This repository contains the LeRobot port of **VLA-JEPA**, a Vision-Language-Action model that combines a Qwen3-VL language backbone with a self-supervised video world model (V-JEPA2) and a flow-matching DiT action head.
+
+Converted from [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA).
+
+---
+
+## Architecture Overview
+
+| Component               | Module                            | Role                                                    |
+| ----------------------- | --------------------------------- | ------------------------------------------------------- |
+| **Qwen3-VL backbone**   | `Qwen3VLInterface`                | Fuses images + language instruction into context tokens |
+| **DiT-B action head**   | `VLAJEPAActionHead`               | Flow-matching diffusion over the action chunk           |
+| **V-JEPA2 world model** | `ActionConditionedVideoPredictor` | Self-supervised video prediction loss (training only)   |
+
+At inference time only the Qwen backbone and action head are used; the world model is not needed.
+
+---
+
+## Citation
+
+```bibtex
+@misc{sun2026vlajepaenhancingvisionlanguageactionmodel,
+  title         = {VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model},
+  author        = {Jingwen Sun and Wenyao Zhang and Zekun Qi and Shaojie Ren and Zezhi Liu and Hanxin Zhu and Guangzhong Sun and Xin Jin and Zhibo Chen},
+  year          = {2026},
+  eprint        = {2602.10098},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  url           = {https://arxiv.org/abs/2602.10098},
+}
+```
+
+---
+
+## License
+
+Weights are distributed under the license terms of the original [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA) repository (**Apache 2.0 License**). The LeRobot integration code follows the **Apache 2.0 License**.
@@ -300,7 +300,7 @@ This replaces the old episode-per-file structure with efficient, optimally-sized
 If you have existing datasets in v2.1 format, use the migration tool:

 ```bash
-python src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py \
+python src/lerobot/scripts/convert_dataset_v21_to_v30.py \
    --repo-id your_id/existing_dataset
 ```

@@ -0,0 +1,185 @@
+# ROBOMETER
+
+ROBOMETER is a **general-purpose video-language robotic reward model**. It predicts dense, frame-level task progress and frame-level success from a trajectory video and a task description.
+
+**Paper**: [ROBOMETER: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons](https://arxiv.org/abs/2603.02115)
+**Project**: [robometer.github.io](https://robometer.github.io/)
+**Original code**: [github.com/robometer/robometer](https://github.com/robometer/robometer)
+**Checkpoint**: [lerobot/Robometer-4B](https://huggingface.co/lerobot/Robometer-4B)
+
+## Overview
+
+ROBOMETER builds on `Qwen/Qwen3-VL-4B-Instruct` and adds three lightweight prediction heads:
+
+- **Progress head**: predicts per-frame task progress in `[0, 1]`.
+- **Success head**: predicts per-frame task success probability.
+- **Preference head**: predicts which of two trajectories better completes the task during training.
+
+The paper trains ROBOMETER with a composite objective:
+
+```text
+L = L_pref + L_prog + L_succ
+```
+
+The LeRobot integration is currently **inference-only**. It preserves the preference head so that the published `Robometer-4B` checkpoint loads without remapping, but `compute_reward()` queries the progress or success head only.
+
+## What the LeRobot Integration Covers
+
+- Standard `reward_model.type=robometer` configuration through LeRobot.
+- Qwen3-VL image and text preprocessing through `RobometerEncoderProcessorStep`.
+- LeRobot reward-model save/load APIs through `PreTrainedRewardModel`.
+- Dense, frame-level progress and success predictions internally.
+- A scalar reward through `compute_reward()` for downstream LeRobot reward-model usage.
+
+This page focuses on using the published ROBOMETER checkpoint as a zero-shot reward model. Training ROBOMETER from scratch is outside the current LeRobot integration.
+
+## Installation Requirements
+
+1. Install LeRobot by following the [Installation Guide](./installation).
+2. Install the ROBOMETER dependencies:
+
+```bash
+pip install -e ".[robometer]"
+```
+
+If you use `uv` directly from a source checkout:
+
+```bash
+uv sync --extra robometer
+```
+
+ROBOMETER uses a Qwen3-VL-4B backbone, so GPU inference is strongly recommended.
+
+## Model Inputs and Outputs
+
+ROBOMETER expects:
+
+- A trajectory video or sequence of frames.
+- A natural-language task description.
+
+In LeRobot datasets, the preprocessor reads:
+
+| Config field              | Default                  | Meaning                                               |
+| ------------------------- | ------------------------ | ----------------------------------------------------- |
+| `reward_model.image_key`  | `observation.images.top` | Camera/video observation used by ROBOMETER            |
+| `reward_model.task_key`   | `task`                   | Key in complementary data that stores the task string |
+| `reward_model.max_frames` | `8`                      | Maximum number of frames passed to ROBOMETER          |
+
+The model predicts per-frame progress and success internally. The LeRobot reward API returns a scalar per sample:
+
+- `reward_output="progress"` (default): return the last-frame progress, clamped to `[0, 1]`.
+- `reward_output="success"`: return `1.0` if the last-frame success probability is above `success_threshold`, otherwise `0.0`.
+
+## Usage
+
+### Load the Reward Model Directly
+
+```python
+from lerobot.rewards.robometer import RobometerConfig, RobometerRewardModel
+
+cfg = RobometerConfig(
+    pretrained_path="lerobot/Robometer-4B",
+    device="cuda",
+    reward_output="progress",
+)
+reward_model = RobometerRewardModel.from_pretrained(cfg.pretrained_path, config=cfg)
+```
+
+### Encode Frames and Compute a Reward
+
+For a direct Python call, provide frames as `uint8` arrays with shape `(T, H, W, C)` and a task string:
+
+```python
+from lerobot.rewards.robometer.modeling_robometer import ROBOMETER_FEATURE_PREFIX
+from lerobot.rewards.robometer.processor_robometer import RobometerEncoderProcessorStep
+
+# frames: np.ndarray, shape (T, H, W, C), dtype uint8
+# task: str
+encoder = RobometerEncoderProcessorStep(
+    base_model_id=cfg.base_model_id,
+    use_multi_image=cfg.use_multi_image,
+    use_per_frame_progress_token=cfg.use_per_frame_progress_token,
+    max_frames=cfg.max_frames,
+)
+
+encoded = encoder.encode_samples([(frames, task)])
+batch = {f"{ROBOMETER_FEATURE_PREFIX}{key}": value for key, value in encoded.items()}
+
+reward = reward_model.compute_reward(batch)
+```
+
+`reward` is a tensor of shape `(batch_size,)`.
+
+### Use the Reward Factory
+
+You can also instantiate ROBOMETER through the reward factory:
+
+```python
+from lerobot.rewards import make_reward_model, make_reward_model_config, make_reward_pre_post_processors
+
+cfg = make_reward_model_config(
+    "robometer",
+    pretrained_path="lerobot/Robometer-4B",
+    device="cuda",
+    image_key="observation.images.top",
+)
+reward_model = make_reward_model(cfg)
+preprocessor, postprocessor = make_reward_pre_post_processors(cfg)
+```
+
+The preprocessor writes Qwen-VL tensors under the `observation.robometer.*` namespace, and `compute_reward()` reads those encoded tensors.
+
+## Configuration Notes
+
+### Backbone and Vocabulary
+
+The published checkpoint uses a Qwen3-VL-4B backbone. ROBOMETER adds five special tokens to the tokenizer in a fixed order:
+
+```text
+<|split_token|>
+<|reward_token|>
+<|pref_token|>
+<|sim_token|>
+<|prog_token|>
+```
+
+`<|prog_token|>` is inserted after each frame and is the hidden-state position used for per-frame progress and success prediction. `<|split_token|>` and `<|pref_token|>` are used by the paper's pairwise trajectory preference objective. `<|reward_token|>` and `<|sim_token|>` are preserved for checkpoint compatibility.
+
+The LeRobot config stores a serialized `vlm_config` with the post-resize vocabulary so the model can reload from `config.json` without downloading the base Qwen weights first. For `Qwen/Qwen3-VL-4B-Instruct`, the tokenizer length is `151669`, and the five ROBOMETER tokens produce the checkpoint vocabulary size `151674`.
+
+### Progress Prediction
+
+In the published checkpoint, progress is discrete. The progress head outputs logits over `progress_discrete_bins=10` uniformly spaced bin centers in `[0, 1]`. LeRobot converts these logits into a continuous value by applying a softmax and taking the expectation over bin centers, matching the upstream ROBOMETER implementation.
+
+### Success Prediction
+
+The success head outputs raw logits per frame. LeRobot converts them to probabilities with `sigmoid`. When `reward_output="success"`, `compute_reward()` thresholds the last-frame success probability using `success_threshold`.
+
+## Limitations
+
+- The current LeRobot integration is inference-only; it does not implement ROBOMETER training or preference-pair training.
+- `compute_reward()` returns a scalar per sample for the LeRobot reward-model API, even though ROBOMETER predicts per-frame progress and success internally.
+- ROBOMETER is video-language based; it does not use privileged robot state such as contact forces or object poses.
+
+## References
+
+- [ROBOMETER project](https://robometer.github.io/)
+- [ROBOMETER paper](https://arxiv.org/abs/2603.02115)
+- [Original ROBOMETER code](https://github.com/robometer/robometer)
+- [Published ROBOMETER-4B checkpoint](https://huggingface.co/lerobot/Robometer-4B)
+- [Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct)
+
+## Citation
+
+```bibtex
+@inproceedings{liang2026robometer,
+title = {Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons},
+author={Anthony Liang and Yigit Korkmaz and Jiahui Zhang and Minyoung Hwang and Abrar Anwar and Sidhant Kaushik and Aditya Shah and Alex S. Huang and Luke Zettlemoyer and Dieter Fox and Yu Xiang and Anqi Li and Andreea Bobu and Abhishek Gupta and Stephen Tu and Erdem Biyik and Jesse Zhang},
+year={2026},
+booktitle={Robotics: Science and Systems 2026},
+}
+```
+
+## License
+
+This LeRobot integration follows the **Apache 2.0 License** used by LeRobot. Check the upstream ROBOMETER code and model pages for the licenses of the original implementation and released checkpoints.
@@ -97,22 +97,22 @@ Similarly for when recording an episode, it is recommended that you are logged i
 Once you are logged in, you can run inference in your setup by doing:

 ```bash
-lerobot-record \
+lerobot-rollout \
+  --strategy.type=base \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \ # <- Use your port
  --robot.id=my_blue_follower_arm \ # <- Use your robot id
  --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras
-  --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
-  --dataset.repo_id=${HF_USER}/eval_DATASET_NAME_test \  # <- This will be the dataset name on HF Hub
-  --dataset.episode_time_s=50 \
-  --dataset.num_episodes=10 \
-  --dataset.streaming_encoding=true \
-  --dataset.encoder_threads=2 \
-  # --dataset.camera_encoder.vcodec=auto \
+  --task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording
+  # <- RTC optional, use when running on low power hardware \
+  # --inference.type=rtc \
+  # --inference.rtc.execution_horizon=10 \
+  # --inference.rtc.max_guidance_weight=10.0 \
  # <- Teleop optional if you want to teleoperate in between episodes \
  # --teleop.type=so100_leader \
  # --teleop.port=/dev/ttyACM0 \
  # --teleop.id=my_red_leader_arm \
+  # --display_data=true #optional use if you want to see the camera stream \
  --policy.path=HF_USER/FINETUNE_MODEL_NAME # <- Use your fine-tuned model
 ```

@@ -0,0 +1,177 @@
+# TOPReward
+
+TOPReward is a **zero-shot reward model** that extracts token log-probabilities from an off-the-shelf vision-language model (VLM) as a robotic reward signal. Given a video trajectory and a task instruction, it returns the VLM's log-likelihood that the instruction is true — no fine-tuning required.
+
+**Paper**: [TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics](https://arxiv.org/abs/2602.19313)
+**Project**: [topreward.github.io](https://topreward.github.io/webpage/)
+**Original code**: [github.com/TOPReward/TOPReward](https://github.com/TOPReward/TOPReward)
+**Default backbone**: [Qwen/Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
+
+## Overview
+
+TOPReward asks a generic VLM how likely a task instruction is, **conditioned on the video** of a robot trying to complete that task. Concretely, given:
+
+- A trajectory video (a sequence of frames).
+- A task instruction (e.g. _"open the drawer"_).
+
+it builds a chat prompt of the form
+
+```text
+<video>
+"The above video shows a robot manipulation trajectory that completes the
+ following task: <instruction> Decide whether the above statement is True
+ or not. The answer is: True"
+```
+
+forwards it through the VLM, label-masks everything except the very last token, and reads back the log-probability of that token — by default the literal `"True"` that closes the suffix template. The resulting `log P("True" | video + prompt + instruction)` is the reward.
+
+Because the method only depends on a frozen VLM, TOPReward is **zero-shot**: there are no fine-tuned weights to host. The "model" in LeRobot is a small wrapper around `transformers`' `Qwen3VLForConditionalGeneration` plus the label-masking logic. The processor owns the tokeniser and builds the full chat prompt (EO-1/Robometer pattern).
+
+## What the LeRobot integration covers
+
+- Standard `reward_model.type=topreward` configuration through LeRobot.
+- VLM loading via the `transformers` `Qwen3VLForConditionalGeneration` API.
+- Prompt assembly + tokenisation in the processor (matching upstream `QwenClient.compute_instruction_reward`).
+- `compute_reward()` returns one scalar log-prob per sample.
+- LeRobot reward-model save/load — `save_pretrained` writes only `config.json` (the VLM is identified by `vlm_name`).
+- An offline labeling script that writes a `topreward_progress.parquet` (SARM-compatible schema) for RA-BC and overlay.
+
+The current LeRobot port supports the **Qwen3-VL client only**. Other upstream clients (Gemini, OpenAI, Gemma, Molmo) can be added as follow-up extras.
+
+## Installation Requirements
+
+1. Install LeRobot following the [Installation Guide](./installation).
+2. Install the TOPReward optional extra:
+
+```bash
+pip install -e ".[topreward]"
+```
+
+or, with `uv` from a source checkout:
+
+```bash
+uv sync --extra topreward
+```
+
+This pulls in `transformers`. The first time you run TOPReward, Hugging Face will also download the VLM weights from the Hub (~16 GB for Qwen3-VL-8B-Instruct). A GPU is strongly recommended.
+
+## Model Inputs and Outputs
+
+TOPReward expects:
+
+- A trajectory video or sequence of frames.
+- A natural-language task description.
+
+In LeRobot datasets the preprocessor reads:
+
+| Config field              | Default                     | Meaning                                       |
+| ------------------------- | --------------------------- | --------------------------------------------- |
+| `reward_model.image_key`  | `observation.images.top`    | Camera observation used by TOPReward          |
+| `reward_model.task_key`   | `task`                      | Key in complementary data for the task string |
+| `reward_model.max_frames` | `16`                        | Cap on frames per sample                      |
+| `reward_model.fps`        | `2.0`                       | Metadata passed to the Qwen video processor   |
+| `reward_model.vlm_name`   | `Qwen/Qwen3-VL-8B-Instruct` | Hugging Face Hub id of the underlying VLM     |
+
+The model returns:
+
+- `compute_reward(batch)`: one log-probability per sample. Higher = better task-video alignment. When `success_threshold` is finite, returns the binary thresholded value instead.
+
+## Usage
+
+### Load the reward model directly
+
+```python
+from lerobot.rewards.topreward import TOPRewardConfig, TOPRewardModel
+
+cfg = TOPRewardConfig(
+    vlm_name="Qwen/Qwen3-VL-8B-Instruct",
+    device="cuda",
+)
+reward_model = TOPRewardModel(cfg)
+```
+
+### Use the reward factory
+
+```python
+from lerobot.rewards import make_reward_model, make_reward_model_config, make_reward_pre_post_processors
+
+cfg = make_reward_model_config(
+    "topreward",
+    vlm_name="Qwen/Qwen3-VL-8B-Instruct",
+    device="cuda",
+    image_key="observation.images.top",
+)
+reward_model = make_reward_model(cfg)
+preprocessor, postprocessor = make_reward_pre_post_processors(cfg)
+```
+
+The preprocessor tokenises the full prompt (video + prefix + instruction suffix), writes Qwen-VL tensors + `prompt_length` under `observation.topreward.*`. The model reads those tensors, label-masks based on `prompt_length`, and extracts the log-prob reward.
+
+### Offline dataset labeling
+
+Write a `topreward_progress.parquet` for RA-BC training and overlay videos:
+
+```bash
+# Sparse-dense (15 anchors per episode, matches upstream)
+uv run python -m lerobot.rewards.topreward.compute_rabc_weights \
+    --dataset-repo-id lerobot/libero_10_image \
+    --num-samples 15 \
+    --device cuda
+```
+
+Then render the progress overlay for any episode:
+
+```bash
+uv run examples/dataset/create_progress_videos.py \
+    --repo-id lerobot/libero_10_image \
+    --episode 0 \
+    --progress-file topreward_progress.parquet \
+    --gif
+```
+
+## Configuration Notes
+
+### Prompt knobs
+
+The default prompt mirrors the upstream paper:
+
+```text
+prompt_prefix = "The above video shows a robot manipulation trajectory that completes the following task: "
+prompt_suffix_template = "{instruction} Decide whether the above statement is True or not. The answer is: True"
+```
+
+Both are exposed on `TOPRewardConfig` for ablation. The suffix template **must** contain `{instruction}`.
+
+### Chat template
+
+`add_chat_template=True` wraps the full prompt (including instruction) with the tokenizer's chat template before tokenisation. Default is `False`, matching the upstream paper's main experiments.
+
+## Limitations
+
+- The current LeRobot port is **inference-only and zero-shot**; `forward()` is not overridden and `is_trainable` returns `False`.
+- Only the **Qwen3-VL family** is supported; other upstream clients are out of scope.
+- TOPReward inherits the underlying VLM's biases.
+
+## References
+
+- [TOPReward project page](https://topreward.github.io/webpage/)
+- [TOPReward paper](https://arxiv.org/abs/2602.19313)
+- [Original TOPReward code](https://github.com/TOPReward/TOPReward)
+- [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct)
+
+## Citation
+
+```bibtex
+@article{chen2026topreward,
+  title={TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics},
+  author={Chen, Shirui and Harrison, Cole and Lee, Ying-Chun and Yang, Angela Jin and
+          Ren, Zhongzheng and Ratliff, Lillian J and Duan, Jiafei and Fox, Dieter and
+          Krishna, Ranjay},
+  journal={arXiv preprint arXiv:2602.19313},
+  year={2026}
+}
+```
+
+## License
+
+The original TOPReward codebase is MIT-licensed. The LeRobot port follows the LeRobot Apache 2.0 license; the wrapped Qwen3-VL weights are subject to the original Qwen license.
@@ -0,0 +1,235 @@
+# VLA-JEPA
+
+This is the LeRobot port of **VLA-JEPA**, a Vision-Language-Action model that combines a Qwen3-VL language backbone with a self-supervised video world model (V-JEPA2) and a flow-matching DiT action head.
+
+---
+
+## Architecture Overview
+
+VLA-JEPA has three main components:
+
+| Component               | Module                            | Role                                                    |
+| ----------------------- | --------------------------------- | ------------------------------------------------------- |
+| **Qwen3-VL backbone**   | `Qwen3VLInterface`                | Fuses images + language instruction into context tokens |
+| **DiT-B action head**   | `VLAJEPAActionHead`               | Flow-matching diffusion over the action chunk           |
+| **V-JEPA2 world model** | `ActionConditionedVideoPredictor` | Self-supervised video prediction loss (training only)   |
+
+### Data flow
+
+**Training:**
+
+1. A video clip of `num_video_frames` frames is encoded by V-JEPA2 into per-frame patch tokens.
+2. The Qwen3-VL backbone processes multi-view images + the task instruction and produces a sequence of context tokens that includes special action tokens (for world model conditioning) and embodied tokens.
+3. The action head receives those context tokens as cross-attention keys/values and predicts a denoised action chunk via flow matching.
+4. The world model predictor uses the action tokens extracted from Qwen to predict future V-JEPA2 frame embeddings; a regression loss on those predictions is added to the action loss.
+
+**Inference:**
+Only Qwen + the action head are used. The world model is not needed at inference time.
+
+### Action head details
+
+Available presets via `action_model_type`:
+
+| Preset  | Hidden dim | Heads | Head dim |
+| ------- | ---------- | ----- | -------- |
+| `DiT-B` | 768        | 12    | 64       |
+| `DiT-L` | 1536       | 32    | 48       |
+
+### World model details
+
+The video predictor is a ViT-style transformer (`ActionConditionedVideoPredictor`) that takes:
+
+- **Frame tokens**: V-JEPA2 patch embeddings projected to `predictor_embed_dim`
+- **Action tokens**: Qwen action token embeddings projected to `predictor_embed_dim`
+
+It uses block-causal attention so each temporal step can attend to all previous steps. The predictor's input `embed_dim` equals `num_views × video_encoder_hidden_size` (e.g. 2 views × 1024 = 2048 for the pretrained checkpoints).
+
+---
+
+## Pretrained Checkpoints
+
+Three checkpoints are available directly inside the LeRobot org here: [`lerobot/VLA-JEPA`](https://huggingface.co/collections/lerobot/vla-jepa), converted from [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA):
+
+| Checkpoint                    | Dataset           | Cameras                 | World model | Action dim |
+| ----------------------------- | ----------------- | ----------------------- | ----------- | ---------- |
+| `lerobot/VLA-JEPA-LIBERO`     | LIBERO-10         | 2 (agentview + wrist)   | Enabled     | 7          |
+| `lerobot/VLA-JEPA-Pretrain`   | DROID 1.0.1       | 2 (exterior left views) | Enabled     | 7          |
+| `lerobot/VLA-JEPA-SimplerEnv` | OXE Bridge / RT-1 | 1 (view duplicated ×2)  | Enabled     | 7          |
+
+All checkpoints use `Qwen/Qwen3-VL-2B-Instruct` as the language backbone.
+
+---
+
+## Configuration
+
+Key parameters in `VLAJEPAConfig`:
+
+| Parameter                 | Default | Description                                                                                                                                                                     |
+| ------------------------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `chunk_size`              | 7       | Number of actions predicted per inference call                                                                                                                                  |
+| `n_action_steps`          | 7       | Steps executed from the predicted chunk before re-planning                                                                                                                      |
+| `num_video_frames`        | 8       | Video clip length fed to the world model                                                                                                                                        |
+| `enable_world_model`      | `True`  | Whether to load and train the V-JEPA2 predictor                                                                                                                                 |
+| `world_model_loss_weight` | 0.1     | Weight of the JEPA prediction loss relative to the action loss                                                                                                                  |
+| `num_inference_timesteps` | 4       | Euler integration steps for action denoising                                                                                                                                    |
+| `freeze_qwen`             | `False` | Freeze the Qwen3-VL backbone and only train the action head                                                                                                                     |
+| `reinit_modules`          | `None`  | Key prefixes allowed to be randomly re-initialised on load (for cross-embodiment transfer, see [Fine-tuning on a different embodiment](#fine-tuning-on-a-different-embodiment)) |
+| `gripper_dim`             | 6       | Index of the gripper dimension in the action vector (e.g. 6 for a 7-DoF arm with gripper as the last joint)                                                                     |
+| `gripper_threshold`       | 0.5     | Threshold used by `pre_snap_gripper_action` and `binarize_gripper_action` to binarize the gripper dimension                                                                     |
+| `pre_snap_gripper_action` | `True`  | Snap the gripper dim to {0, 1} before unnormalization. Set to `False` for robots without a binary gripper                                                                       |
+| `binarize_gripper_action` | `True`  | Binarize the gripper dim to {-1, 1} after unnormalization. Set to `False` for robots without a binary gripper                                                                   |
+
+---
+
+## Training
+
+Number of training steps may vary based on dataset size and compute budget. The original paper pretrained for 50k on ssv2 + droid jointly, then additional 30k steps for LIBERO, but fewer steps may still yield good performance when fine-tuning from the provided pretrained checkpoints.
+
+### Full training from scratch
+
+```bash
+lerobot-train \
+  policy.type=vla_jepa \
+  policy.repo_id=your_org/your_repo \
+  dataset.repo_id=your_org/your_dataset
+```
+
+### Fine-tuning from a pretrained checkpoint
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=your_org/your_dataset
+```
+
+If you want to freeze the Qwen backbone and only train the action head, set `policy.freeze_qwen=True`:
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --policy.freeze_qwen=true \
+  --dataset.repo_id=your_org/your_dataset
+```
+
+### Fine-tuning on a different embodiment
+
+When the target robot has a different action or state dimensionality than the pretrained checkpoint, the input/output projection layers of the action head will have mismatched shapes and cannot be loaded directly. `reinit_modules` lets you list the key prefixes that are allowed to mismatch — those layers are randomly re-initialised while every other weight is reused from the checkpoint. Any shape mismatch outside the listed prefixes raises an error.
+
+The layers that depend on `action_dim` and `state_dim` are:
+
+| Layer                                     | Key prefix                          |
+| ----------------------------------------- | ----------------------------------- |
+| Action encoder (action_dim → inner_dim)   | `model.action_model.action_encoder` |
+| Action decoder (hidden_size → action_dim) | `model.action_model.action_decoder` |
+| State encoder (state_dim → inner_dim)     | `model.action_model.state_encoder`  |
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --policy.freeze_qwen=true \
+  --policy.reinit_modules='["model.action_model.action_encoder", "model.action_model.action_decoder", "model.action_model.state_encoder"]' \
+  --dataset.repo_id=your_org/your_dataset
+```
+
+If your robot has no proprioceptive state, omit `model.action_model.state_encoder` from the list.
+
+### Reproducing the LIBERO results
+
+**Training on LIBERO:**
+starts the training from the Pretrain checkpoint, trains for 30k steps on the LIBERO dataset.
+Original paper mentions training across 8 GPUs with a batch size of 32, meaning global batch size of 256.
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=HuggingFaceVLA/libero \
+  --steps=30000
+```
+
+**Evaluating the pretrained LIBERO-10 checkpoint:**
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/VLA-JEPA-LIBERO \
+  --env.type=libero \
+  --env.task=libero_spatial,libero_object,libero_goal,libero_10 \
+  --eval.n_episodes=10 \
+  --eval.batch_size=5
+```
+
+To evaluate a subset of tasks only:
+
+```bash
+lerobot-eval \
+  --policy.path=lerobot/VLA-JEPA-LIBERO \
+  --env.type=libero \
+  --env.task=libero_10 \
+  --env.task_ids='[0,1,2]' \
+  --eval.n_episodes=10 \
+  --eval.batch_size=5
+```
+
+**Expected results:**
+
+| Suite          | Episodes | Successes | Success Rate |
+| -------------- | -------- | --------- | ------------ |
+| libero_spatial | 100      | 93        | **95.0%**    |
+| libero_object  | 100      | 100       | **100.0%**   |
+| libero_goal    | 100      | 98        | **98.0%**    |
+| libero_10      | 100      | 96        | **93.0%**    |
+| **Overall**    | **400**  | **387**   | **96.5%**    |
+
+---
+
+## Fine-tuning on datasets with a different number of cameras
+
+The pretrained world model predictor was trained with `embed_dim = jepa_tubelet_size × 1024` (default `jepa_tubelet_size=2`).
+
+**Default behaviour — view padding / trimming (no action required)**
+
+When fine-tuning from `VLA-JEPA-Pretrain` the model automatically adjusts the number of views fed to the world model to match `jepa_tubelet_size`:
+
+- **Single-view datasets (e.g. BridgeV2):** the single-view latent is duplicated to produce a two-view world-model input, preserving the JEPA self-supervised signal without any weight mismatch.
+- **>2-view datasets (e.g. DROID with 3 views):** all views are passed to the Qwen backbone (for richer context), but only the first `jepa_tubelet_size` views (one wrist + one third-person, following the configured view order) are used for the world model.
+
+**Option 1 — Disable the world model**
+
+Set `enable_world_model=False` to skip the JEPA loss entirely. Only the Qwen backbone and action head are loaded and trained. This is sufficient for good action performance.
+
+```bash
+lerobot-train \
+  --policy.path=lerobot/VLA-JEPA-Pretrain \
+  --policy.enable_world_model=false \
+  --policy.repo_id=your_org/your_repo \
+  --dataset.repo_id=your_org/single_camera_dataset
+```
+
+**Option 2 — Reinitialize the predictor input projection**
+
+If you want to change `jepa_tubelet_size` to a value other than 2, load the checkpoint with `strict=False` and reinitialize `model.video_predictor.predictor_embed` for the new `embed_dim`. All other predictor block weights (attention, MLP, norm, output projection) are camera-count-agnostic and can be reused from the pretrained checkpoint.
+
+---
+
+## Citation
+
+```bibtex
+@misc{sun2026vlajepaenhancingvisionlanguageactionmodel,
+  title         = {VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model},
+  author        = {Jingwen Sun and Wenyao Zhang and Zekun Qi and Shaojie Ren and Zezhi Liu and Hanxin Zhu and Guangzhong Sun and Xin Jin and Zhibo Chen},
+  year          = {2026},
+  eprint        = {2602.10098},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.RO},
+  url           = {https://arxiv.org/abs/2602.10098},
+}
+```
+
+---
+
+## License
+
+Weights are distributed under the license terms of the original [ginwind/VLA-JEPA](https://huggingface.co/ginwind/VLA-JEPA) repository (**Apache 2.0 License**). The LeRobot integration code follows the **Apache 2.0 License**.
@@ -15,10 +15,12 @@
 # limitations under the License.

 """
-Create MP4 (or GIF) videos with sarm_progress overlay for specified episodes.
+Create MP4 (or GIF) videos with per-frame progress overlay for specified episodes.

 Downloads datasets from HuggingFace, seeks directly into the episode segment
 of the source video, draws a progress line on each frame, and writes the result.
+The progress data is read from a parquet file that lives alongside the dataset
+(configurable via ``--progress-file``).

 Usage:
    python examples/dataset/create_progress_videos.py \
@@ -56,22 +58,26 @@ SCORE_FONT_SCALE = 0.8
 TASK_FONT_SCALE = 0.55


-def download_episode_metadata(repo_id: str, episode: int) -> Path:
-    """Download only the metadata and sarm_progress files for a dataset.
+def download_episode_metadata(
+    repo_id: str, episode: int, progress_file: str = "sarm_progress.parquet"
+) -> Path:
+    """Download only the metadata and per-frame progress file for a dataset.

    Args:
        repo_id: HuggingFace dataset repository ID.
        episode: Episode index (used for logging only; all meta is fetched).
+        progress_file: Filename of the per-frame progress parquet inside the
+            dataset repo.

    Returns:
        Local cache path for the downloaded snapshot.
    """
-    logging.info("[1/4] Downloading metadata for %s (episode %d) ...", repo_id, episode)
+    logging.info("[1/4] Downloading metadata + %s for %s (episode %d) ...", progress_file, repo_id, episode)
    local_path = Path(
        snapshot_download(
            repo_id=repo_id,
            repo_type="dataset",
-            allow_patterns=["meta/**", "sarm_progress.parquet"],
+            allow_patterns=["meta/**", progress_file],
            ignore_patterns=["*.mp4"],
        )
    )
@@ -215,25 +221,28 @@ def download_video_file(repo_id: str, local_path: Path, video_rel: str) -> Path:
    return video_path


-def load_progress_data(local_path: Path, episode: int) -> np.ndarray | None:
-    """Load sarm_progress values for an episode.
+def load_progress_data(
+    local_path: Path, episode: int, progress_file: str = "sarm_progress.parquet"
+) -> np.ndarray | None:
+    """Load per-frame progress values for an episode.

    Args:
        local_path: Dataset cache root.
        episode: Episode index.
+        progress_file: Filename of the per-frame progress parquet.

    Returns:
        Sorted (N, 2) array of (frame_index, progress), or None if unavailable.
    """
-    parquet_path = local_path / "sarm_progress.parquet"
+    parquet_path = local_path / progress_file
    if not parquet_path.exists():
-        logging.warning("sarm_progress.parquet not found")
+        logging.warning("%s not found", progress_file)
        return None
    df = pd.read_parquet(parquet_path)
-    logging.info("   sarm_progress.parquet columns: %s", list(df.columns))
+    logging.info("   %s columns: %s", progress_file, list(df.columns))
    episode_df = df[df["episode_index"] == episode].copy()
    if episode_df.empty:
-        logging.warning("No sarm_progress rows for episode %d", episode)
+        logging.warning("No progress rows for episode %d in %s", episode, progress_file)
        return None
    episode_df = episode_df.sort_values("frame_index")

@@ -576,6 +585,7 @@ def process_dataset(
    camera_key: str | None,
    output_dir: Path,
    create_gif: bool = False,
+    progress_file: str = "sarm_progress.parquet",
 ) -> Path | None:
    """Full pipeline: download, extract metadata, composite progress, write output.

@@ -585,6 +595,8 @@ def process_dataset(
        camera_key: Camera key to use, or None for auto-selection.
        output_dir: Directory to write output files.
        create_gif: If True, also generate a GIF from the MP4.
+        progress_file: Filename of the per-frame progress parquet inside the
+            dataset repo.

    Returns:
        Path to the final output file, or None on failure.
@@ -592,7 +604,7 @@ def process_dataset(
    safe_name = repo_id.replace("/", "_")
    logging.info("Processing: %s  |  episode %d", repo_id, episode)

-    local_path = download_episode_metadata(repo_id, episode)
+    local_path = download_episode_metadata(repo_id, episode, progress_file)
    logging.info("   Local cache: %s", local_path)

    episode_meta = load_episode_meta(local_path, episode, camera_key)
@@ -600,9 +612,9 @@ def process_dataset(

    video_path = download_video_file(repo_id, local_path, episode_meta["video_rel"])

-    progress_data = load_progress_data(local_path, episode)
+    progress_data = load_progress_data(local_path, episode, progress_file)
    if progress_data is None:
-        logging.error("Could not load sarm_progress data. Skipping overlay.")
+        logging.error("Could not load progress data from %s. Skipping overlay.", progress_file)
        return None

    logging.info("   Progress frames: %d", len(progress_data))
@@ -627,7 +639,7 @@ def process_dataset(

 def main() -> None:
    parser = argparse.ArgumentParser(
-        description="Create MP4/GIF videos with sarm_progress overlay for dataset episodes."
+        description="Create MP4/GIF videos with per-frame progress overlay for dataset episodes."
    )
    parser.add_argument(
        "--repo-id",
@@ -658,6 +670,15 @@ def main() -> None:
        action="store_true",
        help="Also generate a GIF from the MP4 output.",
    )
+    parser.add_argument(
+        "--progress-file",
+        type=str,
+        default="sarm_progress.parquet",
+        help=(
+            "Filename of the per-frame progress parquet inside the dataset repo "
+            "(default: 'sarm_progress.parquet')."
+        ),
+    )
    args = parser.parse_args()

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
@@ -670,6 +691,7 @@ def main() -> None:
        camera_key=args.camera_key,
        output_dir=args.output_dir,
        create_gif=args.gif,
+        progress_file=args.progress_file,
    )

    if result:
@@ -138,7 +138,9 @@ dataset_viz = ["lerobot[dataset]", "lerobot[viz]"]
 # Common
 av-dep = ["av>=15.0.0,<16.0.0"]
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.9.17"]
+# NOTE: 0.9.16 links against liburdfdom_sensor.so.4, which is unavailable on Ubuntu 24.04
+# (noble ships urdfdom 3.x). Cap below 0.9.16 until system urdfdom 4.x is broadly available.
+placo-dep = ["placo>=0.9.6,<0.9.16"]
 transformers-dep = ["transformers>=5.4.0,<5.6.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
@@ -196,6 +198,7 @@ wallx = [
    "lerobot[qwen-vl-utils-dep]",
 ]
 pi = ["lerobot[transformers-dep]", "lerobot[scipy-dep]"]
+molmoact2 = ["lerobot[transformers-dep]", "lerobot[peft-dep]", "lerobot[scipy-dep]"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0"]
 multi_task_dit = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]"]
 groot = [
@@ -209,9 +212,12 @@ groot = [
    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
 sarm = ["lerobot[transformers-dep]", "pydantic>=2.0.0,<3.0.0", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
+robometer = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]", "lerobot[peft-dep]"]
+topreward = ["lerobot[transformers-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 eo1 = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-dep]"]
 hilserl = ["lerobot[transformers-dep]", "lerobot[dataset]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
+vla_jepa = ["lerobot[transformers-dep]", "lerobot[diffusers-dep]", "lerobot[qwen-vl-utils-dep]"]

 # Features
 async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
@@ -272,10 +278,12 @@ all = [
    "lerobot[multi_task_dit]",
    "lerobot[wallx]",
    "lerobot[pi]",
+    "lerobot[molmoact2]",
    "lerobot[smolvla]",
    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
    "lerobot[xvla]",
    "lerobot[hilserl]",
+    "lerobot[vla_jepa]",
    "lerobot[async]",
    "lerobot[dev]",
    "lerobot[test]",
@@ -286,6 +294,8 @@ all = [
    "lerobot[libero]; sys_platform == 'linux'",
    "lerobot[metaworld]",
    "lerobot[sarm]",
+    "lerobot[robometer]",
+    "lerobot[topreward]",
    "lerobot[peft]",
    # "lerobot[unitree_g1]", TODO: Unitree requires specific installation instructions for unitree_sdk2
 ]
@@ -401,8 +411,11 @@ default.extend-ignore-identifiers-re = [
    "ein",
    "thw",
    "inpt",
+    "arange",
+    "is_compileable",
    "ROBOTIS",
-    "OT_VALUE"
+    "OT_VALUE",
+    "VanderBilt"
 ]

 # TODO: Uncomment when ready to use
@@ -41,8 +41,8 @@ class DatasetRecordConfig:
    video: bool = True
    # Upload dataset to Hugging Face hub.
    push_to_hub: bool = True
-    # Upload on private repository on the Hugging Face hub.
-    private: bool = False
+    # If True, upload as private; if None, defer to the org default on the Hub (only affects orgs).
+    private: bool | None = None
    # Add tags to your dataset on the hub.
    tags: list[str] | None = None
    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
@@ -255,8 +255,7 @@ def extract_path_fields_from_config(config_path: str, path_fields: list[str]) ->
            remaining = config_data[field]
            if remaining:
                _config_yaml_overrides[field] = _flatten_to_cli_args(remaining)
-            else:
-                del config_data[field]
+            del config_data[field]
            modified = True

    if not modified:
@@ -311,7 +310,13 @@ def wrap(config_path: Path | None = None) -> Callable[[F], F]:
                    cli_args = filter_arg("config_path", cli_args)
                    cfg = argtype.from_pretrained(config_path_cli, cli_args=cli_args)
                else:
-                    cfg = draccus.parse(config_class=argtype, config_path=config_path, args=cli_args)
+                    if config_path_cli:
+                        cli_args = filter_arg("config_path", cli_args)
+                    cfg = draccus.parse(
+                        config_class=argtype,
+                        config_path=config_path_cli or config_path,
+                        args=cli_args,
+                    )
            response = fn(cfg, *args, **kwargs)
            return response

@@ -177,6 +177,12 @@ class TrainPipelineConfig(HubMixin):
            )

        active_cfg = self.trainable_config
+        if self.rename_map and active_cfg.pretrained_path is None:
+            raise ValueError(
+                "`rename_map` requires a pretrained policy checkpoint. "
+                "Fresh initialization derives feature names from the current dataset, so no rename is applied."
+            )
+
        if not self.job_name:
            if self.env is None:
                self.job_name = f"{active_cfg.type}"
@@ -524,7 +524,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
        license: str | None = "apache-2.0",
        tag_version: bool = True,
        push_videos: bool = True,
-        private: bool = False,
+        private: bool | None = None,
        allow_patterns: list[str] | str | None = None,
        upload_large_folder: bool = False,
        **card_kwargs,
@@ -543,7 +543,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
            tag_version: If ``True``, create a Git tag for the current codebase
                version.
            push_videos: If ``False``, skip uploading the ``videos/`` directory.
-            private: If ``True``, create a private repository.
+            private: If ``True``, create a private repository. If ``None``
+                (default), defer to the org default on the Hub (only affects orgs).
            allow_patterns: Glob pattern(s) restricting which files to upload.
            upload_large_folder: If ``True``, use ``upload_large_folder`` instead
                of ``upload_folder`` for very large datasets.
@@ -18,12 +18,25 @@ from typing import TYPE_CHECKING

 import numpy as np

-from lerobot.utils.import_utils import _placo_available, require_package
+from lerobot.utils.import_utils import require_package

-if TYPE_CHECKING or _placo_available:
+_placo_runtime_error: ImportError | None = None
+
+if TYPE_CHECKING:
    import placo  # type: ignore[import-not-found]
 else:
-    placo = None
+    try:
+        import placo  # type: ignore[import-not-found]
+    except ImportError as _placo_import_err:
+        placo = None
+        _placo_runtime_error = _placo_import_err
+
+
+def _raise_if_placo_unusable() -> None:
+    if placo is None and _placo_runtime_error is not None:
+        raise ImportError(
+            f"placo is installed but failed to import: {_placo_runtime_error!s}"
+        ) from _placo_runtime_error


 class RobotKinematics:
@@ -44,6 +57,7 @@ class RobotKinematics:
            joint_names (list[str] | None): List of joint names to use for the kinematics solver
        """
        require_package("placo", extra="placo-dep")
+        _raise_if_placo_unusable()

        self.robot = placo.RobotWrapper(urdf_path)
        self.solver = placo.KinematicsSolver(self.robot)
@@ -43,6 +43,7 @@ from .tables import (
    CAN_CMD_SET_ZERO,
    DEFAULT_BAUDRATE,
    DEFAULT_TIMEOUT_MS,
+    HANDSHAKE_TIMEOUT_S,
    MODEL_RESOLUTION,
    MOTOR_LIMIT_PARAMS,
    NORMALIZED_DATA,
@@ -215,14 +216,16 @@ class RobstrideMotorsBus(MotorsBusBase):
            self._is_connected = False
            raise ConnectionError(f"Failed to connect to CAN bus: {e}") from e

-    def _query_status_via_clear_fault(self, motor: NameOrID) -> tuple[bool, can.Message | None]:
+    def _query_status_via_clear_fault(
+        self, motor: NameOrID, timeout: float = RUNNING_TIMEOUT
+    ) -> tuple[bool, can.Message | None]:
        motor_name = self._get_motor_name(motor)
        motor_id = self._get_motor_id(motor_name)
        recv_id = self._get_motor_recv_id(motor_name)
        data = [0xFF] * 7 + [CAN_CMD_CLEAR_FAULT]
        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
        self._bus().send(msg)
-        return self._recv_status_via_clear_fault(expected_recv_id=recv_id)
+        return self._recv_status_via_clear_fault(expected_recv_id=recv_id, timeout=timeout)

    def _recv_status_via_clear_fault(
        self, expected_recv_id: int | None = None, timeout: float = RUNNING_TIMEOUT
@@ -280,7 +283,7 @@ class RobstrideMotorsBus(MotorsBusBase):
        faulted_motors = []

        for motor_name in self.motors:
-            has_fault, msg = self._query_status_via_clear_fault(motor_name)
+            has_fault, msg = self._query_status_via_clear_fault(motor_name, timeout=HANDSHAKE_TIMEOUT_S)
            if msg is None:
                missing_motors.append(motor_name)
            elif has_fault:
@@ -505,6 +508,87 @@ class RobstrideMotorsBus(MotorsBusBase):

        return responses

+    def _recv_all_messages_until_quiet(
+        self,
+        *,
+        timeout: float = RUNNING_TIMEOUT,
+        max_messages: int = 4096,
+    ) -> list[can.Message]:
+        """
+        Receive frames until the bus goes quiet.
+
+        Args:
+            timeout: Poll timeout used for each recv() call. Collection stops
+                when one recv() times out (quiet gap).
+            max_messages: Safety cap to prevent unbounded loops.
+        """
+        out: list[can.Message] = []
+        max_messages = max(1, max_messages)
+        timeout = max(0.0, timeout)
+
+        try:
+            while len(out) < max_messages:
+                msg = self._bus().recv(timeout=timeout)
+                if msg is None:
+                    break
+                out.append(msg)
+        except (can.CanError, OSError) as e:
+            logger.debug(f"Error draining CAN RX queue on {self.port}: {e}")
+
+        return out
+
+    def _process_feedback_messages(self, messages: list[can.Message]) -> set[int]:
+        """
+        Decode all received feedback frames and update cached motor states.
+
+        Returns:
+            Set of payload recv_ids that were successfully mapped to motors.
+        """
+        processed_recv_ids: set[int] = set()
+        for msg in messages:
+            if len(msg.data) < 1:
+                logger.debug(
+                    f"Dropping short CAN frame on {self.port} "
+                    f"(arb=0x{int(msg.arbitration_id):02X}, data={bytes(msg.data).hex()})"
+                )
+                continue
+
+            recv_id = int(msg.data[0])
+            motor_name = self._recv_id_to_motor.get(recv_id)
+            if motor_name is None:
+                logger.debug(
+                    f"Unmapped CAN frame on {self.port} "
+                    f"(arb=0x{int(msg.arbitration_id):02X}, recv_id=0x{recv_id:02X}, data={bytes(msg.data).hex()})"
+                )
+                continue
+
+            self._process_response(motor_name, msg)
+            processed_recv_ids.add(recv_id)
+
+        return processed_recv_ids
+
+    def flush_rx_queue(self, poll_timeout_s: float = 0.0005, max_messages: int = 4096) -> int:
+        """
+        Drain pending RX frames from the CAN interface.
+
+        This is used by higher-level controllers to drop stale feedback before issuing
+        a fresh read cycle, so subsequent state reads are based on most recent replies.
+        It should also be called once when a controller instance is created/connected,
+        to clear residual frames left on the interface from previous sessions.
+        """
+        drained = 0
+        poll_timeout_s = max(0.0, poll_timeout_s)
+        max_messages = max(1, max_messages)
+        try:
+            while drained < max_messages:
+                msg = self._bus().recv(timeout=poll_timeout_s)
+                if msg is None:
+                    break
+                drained += 1
+        except (can.CanError, OSError) as e:
+            logger.debug(f"Failed to flush CAN RX queue on {self.port}: {e}")
+        return drained
+
    def _speed_control(
        self,
        motor: NameOrID,
@@ -644,11 +728,14 @@ class RobstrideMotorsBus(MotorsBusBase):
            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
            self._bus().send(msg)
            recv_id_to_motor[self._get_motor_recv_id(motor)] = motor_name
+        # Read every feedback frame until RX goes quiet, then decode all of them.
+        # This avoids dropping useful frames when responses from different motors interleave.
+        messages = self._recv_all_messages_until_quiet()
+        processed_recv_ids = self._process_feedback_messages(messages)

-        responses = self._recv_all_responses(list(recv_id_to_motor.keys()), timeout=RUNNING_TIMEOUT)
        for recv_id, motor_name in recv_id_to_motor.items():
-            if msg := responses.get(recv_id):
-                self._process_response(motor_name, msg)
+            if recv_id not in processed_recv_ids:
+                logger.warning(f"Packet drop: {motor_name} (ID: 0x{recv_id:02X}). Using last known state.")

    def _float_to_uint(self, x: float, x_min: float, x_max: float, bits: int) -> int:
        """Convert float to unsigned integer for CAN transmission."""
@@ -711,7 +798,10 @@ class RobstrideMotorsBus(MotorsBusBase):
        try:
            self._decode_motor_state(msg.data)
        except Exception as e:
-            logger.warning(f"Failed to decode response from {motor}: {e}")
+            logger.warning(
+                f"Failed to decode response from {motor} "
+                f"(arb=0x{int(msg.arbitration_id):02X}, data={bytes(msg.data).hex()}): {e}"
+            )

    def _get_cached_value(self, motor: str, data_name: str) -> Value:
        """Retrieve a specific value from the state cache."""
@@ -848,20 +938,12 @@ class RobstrideMotorsBus(MotorsBusBase):
            self._bus().send(msg)
            updated_motors.append(motor)

-        expected_recv_ids = [self._get_motor_recv_id(motor) for motor in updated_motors]
-        responses = self._recv_all_responses(expected_recv_ids, timeout=RUNNING_TIMEOUT)
-
-        for response in responses.values():
-            payload_motor_name = self._recv_id_to_motor.get(response.data[0])
-            if payload_motor_name is not None:
-                self._process_response(payload_motor_name, response)
-            else:
-                # Fallback: still attempt to decode based on payload byte0 mapping.
-                self._decode_motor_state(response.data)
+        messages = self._recv_all_messages_until_quiet()
+        processed_recv_ids = self._process_feedback_messages(messages)

        for motor in updated_motors:
            recv_id = self._get_motor_recv_id(motor)
-            if recv_id not in responses:
+            if recv_id not in processed_recv_ids:
                logger.warning(f"Packet drop: {motor} (ID: 0x{recv_id:02X}). Using last known state.")

    def read_calibration(self) -> dict[str, MotorCalibration]:
@@ -114,7 +114,8 @@ CAN_CMD_SAVE_PARAM = 0xAA
 CAN_PARAM_ID = 0x7FF


-RUNNING_TIMEOUT = 0.001
+RUNNING_TIMEOUT = 0.003
+HANDSHAKE_TIMEOUT_S = 0.05
 PARAM_TIMEOUT = 0.01

 STATE_CACHE_TTL_S = 0.02
@@ -20,6 +20,7 @@ from .eo1.configuration_eo1 import EO1Config as EO1Config
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig as GaussianActorConfig
 from .groot.configuration_groot import GrootConfig as GrootConfig
+from .molmoact2.configuration_molmoact2 import MolmoAct2Config as MolmoAct2Config
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig as MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0_fast.configuration_pi0_fast import PI0FastConfig as PI0FastConfig
@@ -43,6 +44,7 @@ __all__ = [
    "EO1Config",
    "GaussianActorConfig",
    "GrootConfig",
+    "MolmoAct2Config",
    "MultiTaskDiTConfig",
    "PI0Config",
    "PI0FastConfig",
@@ -49,6 +49,7 @@ from .diffusion.configuration_diffusion import DiffusionConfig
 from .eo1.configuration_eo1 import EO1Config
 from .gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
 from .groot.configuration_groot import GrootConfig
+from .molmoact2.configuration_molmoact2 import MolmoAct2Config
 from .multi_task_dit.configuration_multi_task_dit import MultiTaskDiTConfig
 from .pi0.configuration_pi0 import PI0Config
 from .pi05.configuration_pi05 import PI05Config
@@ -56,6 +57,7 @@ from .pretrained import PreTrainedPolicy
 from .smolvla.configuration_smolvla import SmolVLAConfig
 from .tdmpc.configuration_tdmpc import TDMPCConfig
 from .utils import validate_visual_features_consistency
+from .vla_jepa.configuration_vla_jepa import VLAJEPAConfig
 from .vqbet.configuration_vqbet import VQBeTConfig
 from .wall_x.configuration_wall_x import WallXConfig
 from .xvla.configuration_xvla import XVLAConfig
@@ -88,7 +90,8 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:

    Args:
        name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-            "multi_task_dit", "vqbet", "pi0", "pi05", "gaussian_actor", "smolvla", "wall_x".
+            "multi_task_dit", "vqbet", "pi0", "pi05", "gaussian_actor", "smolvla", "wall_x",
+            "molmoact2".
    Returns:
        The policy class corresponding to the given name.

@@ -151,6 +154,14 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
        from .eo1.modeling_eo1 import EO1Policy

        return EO1Policy
+    elif name == "molmoact2":
+        from .molmoact2.modeling_molmoact2 import MolmoAct2Policy
+
+        return MolmoAct2Policy
+    elif name == "vla_jepa":
+        from .vla_jepa.modeling_vla_jepa import VLAJEPAPolicy
+
+        return VLAJEPAPolicy
    else:
        try:
            return _get_policy_cls_from_policy_name(name=name)
@@ -168,7 +179,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
    Args:
        policy_type: The type of the policy. Supported types include "tdmpc",
                     "multi_task_dit", "diffusion", "act", "vqbet", "pi0", "pi05", "gaussian_actor",
-                     "smolvla", "wall_x".
+                     "smolvla", "wall_x", "molmoact2".
        **kwargs: Keyword arguments to be passed to the configuration class constructor.

    Returns:
@@ -203,6 +214,10 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
        return WallXConfig(**kwargs)
    elif policy_type == "eo1":
        return EO1Config(**kwargs)
+    elif policy_type == "molmoact2":
+        return MolmoAct2Config(**kwargs)
+    elif policy_type == "vla_jepa":
+        return VLAJEPAConfig(**kwargs)
    else:
        try:
            config_cls = PreTrainedConfig.get_choice_class(policy_type)
@@ -231,6 +246,7 @@ class ProcessorConfigKwargs(TypedDict, total=False):
    preprocessor_overrides: dict[str, Any] | None
    postprocessor_overrides: dict[str, Any] | None
    dataset_stats: dict[str, dict[str, torch.Tensor]] | None
+    dataset_meta: Any | None


 def make_pre_post_processors(
@@ -264,26 +280,22 @@ def make_pre_post_processors(
            policy configuration type.
    """
    if pretrained_path:
-        # TODO(Steven): Temporary patch, implement correctly the processors for Gr00t
        if isinstance(policy_cfg, GrootConfig):
-            # GROOT handles normalization in groot_pack_inputs_v3 step
-            # Need to override both stats AND normalize_min_max since saved config might be empty
-            preprocessor_overrides = {}
-            postprocessor_overrides = {}
-            preprocessor_overrides["groot_pack_inputs_v3"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-            }
+            from .groot.processor_groot import make_groot_pre_post_processors_from_pretrained

-            # Also ensure postprocessing slices to env action dim and unnormalizes with dataset stats
-            env_action_dim = policy_cfg.output_features[ACTION].shape[0]
-            postprocessor_overrides["groot_action_unpack_unnormalize_v1"] = {
-                "stats": kwargs.get("dataset_stats"),
-                "normalize_min_max": True,
-                "env_action_dim": env_action_dim,
-            }
-            kwargs["preprocessor_overrides"] = preprocessor_overrides
-            kwargs["postprocessor_overrides"] = postprocessor_overrides
+            return make_groot_pre_post_processors_from_pretrained(
+                config=policy_cfg,
+                pretrained_path=pretrained_path,
+                dataset_stats=kwargs.get("dataset_stats"),
+                preprocessor_overrides=kwargs.get("preprocessor_overrides"),
+                postprocessor_overrides=kwargs.get("postprocessor_overrides"),
+                preprocessor_config_filename=kwargs.get(
+                    "preprocessor_config_filename", f"{POLICY_PREPROCESSOR_DEFAULT_NAME}.json"
+                ),
+                postprocessor_config_filename=kwargs.get(
+                    "postprocessor_config_filename", f"{POLICY_POSTPROCESSOR_DEFAULT_NAME}.json"
+                ),
+            )

        preprocessor = PolicyProcessorPipeline.from_pretrained(
            pretrained_model_name_or_path=pretrained_path,
@@ -406,6 +418,7 @@ def make_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )
+
    elif isinstance(policy_cfg, EO1Config):
        from .eo1.processor_eo1 import make_eo1_pre_post_processors

@@ -414,6 +427,23 @@ def make_pre_post_processors(
            dataset_stats=kwargs.get("dataset_stats"),
        )

+    elif isinstance(policy_cfg, MolmoAct2Config):
+        from .molmoact2.processor_molmoact2 import make_molmoact2_pre_post_processors
+
+        processors = make_molmoact2_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+            dataset_meta=kwargs.get("dataset_meta"),
+        )
+
+    elif isinstance(policy_cfg, VLAJEPAConfig):
+        from .vla_jepa.processor_vla_jepa import make_vla_jepa_pre_post_processors
+
+        processors = make_vla_jepa_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
+
    else:
        try:
            processors = _make_processors_from_policy_config(
@@ -499,6 +529,10 @@ def make_policy(
        action_names = ds_meta.features.get(ACTION, {}).get("names")
        if action_names is not None:
            cfg.action_feature_names = list(action_names)
+    if ds_meta is not None:
+        set_dataset_feature_metadata = getattr(cfg, "set_dataset_feature_metadata", None)
+        if callable(set_dataset_feature_metadata):
+            set_dataset_feature_metadata(ds_meta.features)

    kwargs["config"] = cfg

@@ -18,4 +18,12 @@ from .configuration_groot import GrootConfig
 from .modeling_groot import GrootPolicy
 from .processor_groot import make_groot_pre_post_processors

-__all__ = ["GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+__all__ = ["GR00TN17", "GR00TN17Config", "GrootConfig", "GrootPolicy", "make_groot_pre_post_processors"]
+
+
+def __getattr__(name: str):
+    if name in {"GR00TN17", "GR00TN17Config"}:
+        from .groot_n1_7 import GR00TN17, GR00TN17Config
+
+        return {"GR00TN17": GR00TN17, "GR00TN17Config": GR00TN17Config}[name]
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
@@ -1,54 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-import torch.nn as nn
-
-
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-class SinusoidalPositionalEncoding(nn.Module):
-    """
-    Produces a sinusoidal encoding of shape (B, T, w)
-    given timesteps of shape (B, T).
-    """
-
-    def __init__(self, embedding_dim):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-
-    def forward(self, timesteps):
-        # timesteps: shape (B, T)
-        # We'll compute sin/cos frequencies across dim T
-        timesteps = timesteps.float()  # ensure float
-
-        b, t = timesteps.shape
-        device = timesteps.device
-
-        half_dim = self.embedding_dim // 2
-        # typical log space frequencies for sinusoidal encoding
-        exponent = -torch.arange(half_dim, dtype=torch.float, device=device) * (
-            torch.log(torch.tensor(10000.0)) / half_dim
-        )
-        # Expand timesteps to (B, T, 1) then multiply
-        freqs = timesteps.unsqueeze(-1) * exponent.exp()  # (B, T, half_dim)
-
-        sin = torch.sin(freqs)
-        cos = torch.cos(freqs)
-        enc = torch.cat([sin, cos], dim=-1)  # (B, T, w)
-
-        return enc
@@ -14,6 +14,7 @@
 # limitations under the License.


+import logging
 from typing import TYPE_CHECKING

 import torch
@@ -42,6 +43,9 @@ else:
    Timesteps = None


+logger = logging.getLogger(__name__)
+
+
 class TimestepEncoder(nn.Module):
    def __init__(self, embedding_dim, compute_dtype=torch.float32):
        require_package("diffusers", extra="groot")
@@ -181,8 +185,7 @@ class BasicTransformerBlock(nn.Module):
        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states,
-            attention_mask=attention_mask,
-            # encoder_attention_mask=encoder_attention_mask,
+            attention_mask=encoder_attention_mask if encoder_hidden_states is not None else attention_mask,
        )
        if self.final_dropout:
            attn_output = self.final_dropout(attn_output)
@@ -266,8 +269,8 @@ class DiT(ModelMixin, ConfigMixin):
        self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim)
        self.proj_out_2 = nn.Linear(self.inner_dim, self.config.output_dim)
-        print(
-            "Total number of DiT parameters: ",
+        logger.debug(
+            "Total number of DiT parameters: %d",
            sum(p.numel() for p in self.parameters() if p.requires_grad),
        )

@@ -318,6 +321,71 @@ class DiT(ModelMixin, ConfigMixin):
            return self.proj_out_2(hidden_states)


+class AlternateVLDiT(DiT):
+    """N1.7 DiT variant that alternates cross-attention over image and text tokens."""
+
+    def __init__(self, *args, attend_text_every_n_blocks: int = 2, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.attend_text_every_n_blocks = attend_text_every_n_blocks
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.LongTensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        return_all_hidden_states: bool = False,
+        image_mask: torch.Tensor | None = None,
+        backbone_attention_mask: torch.Tensor | None = None,
+    ):
+        if image_mask is None:
+            raise ValueError("image_mask is required for AlternateVLDiT.")
+        if backbone_attention_mask is None:
+            raise ValueError("backbone_attention_mask is required for AlternateVLDiT.")
+
+        temb = self.timestep_encoder(timestep)
+        hidden_states = hidden_states.contiguous()
+        encoder_hidden_states = encoder_hidden_states.contiguous()
+
+        image_attention_mask = image_mask & backbone_attention_mask
+        non_image_attention_mask = (~image_mask) & backbone_attention_mask
+
+        all_hidden_states = [hidden_states]
+        if not self.config.interleave_self_attention:
+            raise ValueError("AlternateVLDiT requires interleave_self_attention=True.")
+
+        for idx, block in enumerate(self.transformer_blocks):
+            if idx % 2 == 1:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=None,
+                    encoder_attention_mask=None,
+                    temb=temb,
+                )
+            else:
+                curr_encoder_attention_mask = (
+                    non_image_attention_mask
+                    if idx % (2 * self.attend_text_every_n_blocks) == 0
+                    else image_attention_mask
+                )
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=None,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=curr_encoder_attention_mask,
+                    temb=temb,
+                )
+            all_hidden_states.append(hidden_states)
+
+        conditioning = temb
+        shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
+        if return_all_hidden_states:
+            return self.proj_out_2(hidden_states), all_hidden_states
+        return self.proj_out_2(hidden_states)
+
+
 class SelfAttentionTransformer(ModelMixin, ConfigMixin):
    _supports_gradient_checkpointing = True

@@ -362,8 +430,8 @@ class SelfAttentionTransformer(ModelMixin, ConfigMixin):
                for _ in range(self.config.num_layers)
            ]
        )
-        print(
-            "Total number of SelfAttentionTransformer parameters: ",
+        logger.debug(
+            "Total number of SelfAttentionTransformer parameters: %d",
            sum(p.numel() for p in self.parameters() if p.requires_grad),
        )

@@ -1,408 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import field
-from typing import TYPE_CHECKING
-
-import torch
-import torch.nn.functional as F  # noqa: N812
-from torch import nn
-from torch.distributions import Beta
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from transformers import PretrainedConfig
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-    PretrainedConfig = object
-    BatchFeature = None
-
-from .action_encoder import (
-    SinusoidalPositionalEncoding,
-    swish,
-)
-from .cross_attention_dit import DiT, SelfAttentionTransformer
-
-
-class CategorySpecificLinear(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        # For each category, we have separate weights and biases.
-        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
-        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
-
-    def forward(self, x, cat_ids):
-        selected_w = self.W[cat_ids]
-        selected_b = self.b[cat_ids]
-        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
-
-
-class CategorySpecificMLP(nn.Module):
-    def __init__(self, num_categories, input_dim, hidden_dim, output_dim):
-        super().__init__()
-        self.num_categories = num_categories
-        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
-        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
-
-    def forward(self, x, cat_ids):
-        hidden = F.relu(self.layer1(x, cat_ids))
-        return self.layer2(hidden, cat_ids)
-
-
-class MultiEmbodimentActionEncoder(nn.Module):
-    def __init__(self, action_dim, hidden_size, num_embodiments):
-        super().__init__()
-        self.hidden_size = hidden_size
-        self.num_embodiments = num_embodiments
-
-        # W1: R^{w x d}, W2: R^{w x 2w}, W3: R^{w x w}
-        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)  # (d -> w)
-        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)  # (2w -> w)
-        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)  # (w -> w)
-        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
-
-    def forward(self, actions, timesteps, cat_ids):
-        """
-        actions:   shape (B, T, action_dim)
-        timesteps: shape (B,)  -- a single scalar per batch item
-        cat_ids:   shape (B,)
-        returns:   shape (B, T, hidden_size)
-        """
-        b, t, _ = actions.shape
-
-        # 1) Expand each batch's single scalar time 'tau' across all T steps
-        #    so that shape => (B, T)
-        #    e.g. if timesteps is (B,), replicate across T
-        if timesteps.dim() == 1 and timesteps.shape[0] == b:
-            # shape (B,) => (B,T)
-            timesteps = timesteps.unsqueeze(1).expand(-1, t)
-        else:
-            raise ValueError("Expected `timesteps` to have shape (B,) so we can replicate across T.")
-
-        # 2) Standard action MLP step for shape => (B, T, w)
-        a_emb = self.W1(actions, cat_ids)
-
-        # 3) Get the sinusoidal encoding (B, T, w)
-        tau_emb = self.pos_encoding(timesteps).to(dtype=a_emb.dtype)
-
-        # 4) Concat along last dim => (B, T, 2w), then W2 => (B, T, w), swish
-        x = torch.cat([a_emb, tau_emb], dim=-1)
-        x = swish(self.W2(x, cat_ids))
-
-        # 5) Finally W3 => (B, T, w)
-        x = self.W3(x, cat_ids)
-        return x
-
-
-class FlowmatchingActionHeadConfig(PretrainedConfig):
-    """NOTE: N1.5 uses XEmbFlowmatchingPolicyHeadConfig as action head"""
-
-    add_pos_embed: bool = field(default=True, metadata={"help": "Whether to add positional embedding"})
-    model_dtype: str = field(default="float32", metadata={"help": "Model data type."})
-    diffusion_model_cfg: dict = field(default=None, metadata={"help": "Diffusion model configuration."})
-    input_embedding_dim: int = field(default=1536, metadata={"help": "Input embedding channel dimension."})
-    backbone_embedding_dim: int = field(
-        default=1536, metadata={"help": "Backbone embedding channel dimension."}
-    )
-
-    hidden_size: int = field(default=1024, metadata={"help": "Input embedding dimension."})
-    max_seq_len: int = field(default=1024, metadata={"help": "Maximum Sequence Length"})
-    action_dim: int = field(default=None, metadata={"help": "Action dimension."})
-    action_horizon: int = field(default=None, metadata={"help": "Action horizon."})
-    noise_beta_alpha: float = field(default=1.5, metadata={"help": ""})
-    noise_beta_beta: float = field(default=1.0, metadata={"help": ""})
-    noise_s: float = field(default=0.999, metadata={"help": "Flow matching noise Beta distribution s."})
-    num_timestep_buckets: int = field(
-        default=1000, metadata={"help": "Number of timestep discretization buckets."}
-    )
-    num_inference_timesteps: int = field(
-        default=None,
-        metadata={"help": "Number of inference steps for noise diffusion."},
-    )
-    max_num_embodiments: int = field(default=32, metadata={"help": "Number of embodiments."})
-    tune_projector: bool = field(default=True, metadata={"help": "Whether to tune the projector."})
-    tune_diffusion_model: bool = field(
-        default=True, metadata={"help": "Whether to tune the diffusion model."}
-    )
-    load_pretrained_det_decode_layer_path: str = field(
-        default=None, metadata={"help": "Path to pretrained detection model."}
-    )
-    detection_coeff: float = field(default=1.0, metadata={"help": "Detection coefficient."})
-
-    freeze_decode_layer: bool = field(default=False)
-    expand_batch: int = field(default=None)
-    use_vlln: bool = field(default=True)
-
-    vl_self_attention_cfg: dict = field(default=None)
-    num_target_vision_tokens: int = field(default=32, metadata={"help": "Number of target vision tokens."})
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-
-class FlowmatchingActionHead(nn.Module):
-    config_class = FlowmatchingActionHeadConfig
-    supports_gradient_checkpointing = True
-
-    def __init__(
-        self,
-        config: FlowmatchingActionHeadConfig,
-    ):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.input_embedding_dim = config.input_embedding_dim
-
-        self.model = DiT(**config.diffusion_model_cfg)
-        self.action_dim = config.action_dim
-        self.action_horizon = config.action_horizon
-        self.num_inference_timesteps = config.num_inference_timesteps
-
-        self.state_encoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=config.max_state_dim,
-            hidden_dim=self.hidden_size,
-            output_dim=self.input_embedding_dim,
-        )
-        self.action_encoder = MultiEmbodimentActionEncoder(
-            action_dim=config.action_dim,
-            hidden_size=self.input_embedding_dim,
-            num_embodiments=config.max_num_embodiments,
-        )
-        self.action_decoder = CategorySpecificMLP(
-            num_categories=config.max_num_embodiments,
-            input_dim=self.hidden_size,
-            hidden_dim=self.hidden_size,
-            output_dim=self.action_dim,
-        )
-        self.future_tokens = nn.Embedding(config.num_target_vision_tokens, self.input_embedding_dim)
-        nn.init.normal_(self.future_tokens.weight, mean=0.0, std=0.02)
-
-        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
-        self.vl_self_attention = (
-            SelfAttentionTransformer(**config.vl_self_attention_cfg) if config.use_vlln else nn.Identity()
-        )
-
-        if config.add_pos_embed:
-            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
-            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
-
-        self._noise_beta_alpha = config.noise_beta_alpha
-        self._noise_beta_beta = config.noise_beta_beta
-        self._beta_dist = None
-        self.num_timestep_buckets = config.num_timestep_buckets
-        self.config = config
-        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model)
-
-    def set_trainable_parameters(self, tune_projector: bool, tune_diffusion_model: bool):
-        self.tune_projector = tune_projector
-        self.tune_diffusion_model = tune_diffusion_model
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_projector:
-            self.state_encoder.requires_grad_(False)
-            self.action_encoder.requires_grad_(False)
-            self.action_decoder.requires_grad_(False)
-            if self.config.add_pos_embed:
-                self.position_embedding.requires_grad_(False)
-        if not tune_diffusion_model:
-            self.model.requires_grad_(False)
-        print(f"Tune action head projector: {self.tune_projector}")
-        print(f"Tune action head diffusion model: {self.tune_diffusion_model}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_projector and not tune_diffusion_model:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Action head trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No action head trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if not self.tune_projector:
-                self.state_encoder.eval()
-                self.action_encoder.eval()
-                self.action_decoder.eval()
-                if self.config.add_pos_embed:
-                    self.position_embedding.eval()
-            if not self.tune_diffusion_model:
-                self.model.eval()
-
-    def sample_time(self, batch_size, device, dtype):
-        if self._beta_dist is None:
-            self._beta_dist = Beta(self._noise_beta_alpha, self._noise_beta_beta, validate_args=False)
-        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
-        return (self.config.noise_s - sample) / self.config.noise_s
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
-        backbone_features = backbone_output["backbone_features"]
-        backbone_features = self.vlln(backbone_features)
-        backbone_features = self.vl_self_attention(backbone_features)
-        backbone_output["backbone_features"] = backbone_features
-        return backbone_output
-
-    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        # Set frozen modules to eval
-        self.set_frozen_modules_to_eval_mode()
-
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        if self.config.expand_batch is not None:
-            for k, v in backbone_output.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                backbone_output[k] = expanded
-
-            for k, v in action_input.items():
-                ndim = len(v.shape)
-                factors = [self.config.expand_batch]
-                while len(factors) < ndim:
-                    factors.append(1)
-                factors = tuple(factors)
-                expanded = v.repeat(*factors)
-                action_input[k] = expanded
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        device = vl_embs.device
-
-        # Get embodiment ID.
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Embed noised action trajectory.
-        actions = action_input.action
-        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
-        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
-        t = t[:, None, None]  # shape (B,1,1) for broadcast
-
-        noisy_trajectory = (1 - t) * noise + t * actions
-        velocity = actions - noise
-
-        # Convert (continuous) t -> discrete if needed
-        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
-        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
-
-        # Maybe add position embedding.
-        if self.config.add_pos_embed:
-            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-            pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-            action_features = action_features + pos_embs
-
-        # Join vision, language, state and action embedding along sequence dimension.
-        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-        sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-        vl_attn_mask = backbone_output.backbone_attention_mask
-
-        model_output = self.model(
-            hidden_states=sa_embs,
-            encoder_hidden_states=vl_embs,
-            encoder_attention_mask=vl_attn_mask,
-            timestep=t_discretized,
-            return_all_hidden_states=False,  # NOTE (YL): not using flare now
-        )
-        pred = self.action_decoder(model_output, embodiment_id)
-        pred_actions = pred[:, -actions.shape[1] :]
-
-        # Slice out only the action portion of pred and target.
-        action_mask = action_input.action_mask
-        loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
-        loss = loss.sum() / action_mask.sum()
-        output_dict = {
-            "loss": loss,
-        }
-        return BatchFeature(data=output_dict)
-
-    @torch.no_grad()
-    def get_action(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
-        backbone_output = self.process_backbone_output(backbone_output)
-
-        # Get vision and language embeddings.
-        vl_embs = backbone_output.backbone_features
-        embodiment_id = action_input.embodiment_id
-
-        # Embed state.
-        state_features = self.state_encoder(action_input.state, embodiment_id)
-
-        # Set initial actions as the sampled noise.
-        batch_size = vl_embs.shape[0]
-        device = vl_embs.device
-        actions = torch.randn(
-            size=(batch_size, self.config.action_horizon, self.config.action_dim),
-            dtype=vl_embs.dtype,
-            device=device,
-        )
-
-        num_steps = self.num_inference_timesteps
-        dt = 1.0 / num_steps
-
-        # Run denoising steps.
-        for t in range(num_steps):
-            t_cont = t / float(num_steps)  # e.g. goes 0, 1/N, 2/N, ...
-            t_discretized = int(t_cont * self.num_timestep_buckets)
-
-            # Embed noised action trajectory.
-            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
-            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
-            # Maybe add position embedding.
-            if self.config.add_pos_embed:
-                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
-                pos_embs = self.position_embedding(pos_ids).unsqueeze(0)
-                action_features = action_features + pos_embs
-
-            # Join vision, language, state and action embedding along sequence dimension.
-            future_tokens = self.future_tokens.weight.unsqueeze(0).expand(vl_embs.shape[0], -1, -1)
-            sa_embs = torch.cat((state_features, future_tokens, action_features), dim=1)
-
-            # Run model forward.
-            model_output = self.model(
-                hidden_states=sa_embs,
-                encoder_hidden_states=vl_embs,
-                timestep=timesteps_tensor,
-            )
-            pred = self.action_decoder(model_output, embodiment_id)
-
-            pred_velocity = pred[:, -self.action_horizon :]
-
-            # Update actions using euler integration.
-            actions = actions + dt * pred_velocity
-        return BatchFeature(data={"action_pred": actions})
-
-    @property
-    def device(self):
-        return next(iter(self.parameters())).device
-
-    @property
-    def dtype(self):
-        return next(iter(self.parameters())).dtype
@@ -14,12 +14,326 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+import json
+import logging
+import os
 from dataclasses import dataclass, field
+from pathlib import Path

 from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTrainedConfig
 from lerobot.optim import AdamWConfig, CosineDecayWithWarmupSchedulerConfig
 from lerobot.utils.constants import ACTION, OBS_STATE

+logger = logging.getLogger(__name__)
+
+GROOT_N1_7 = "n1.7"
+# Legacy GR00T N1.5 identifier. N1.5 is NOT a supported model_version (it is
+# intentionally absent from _GROOT_MODEL_VERSION_ALIASES so normalize_groot_model_version
+# still rejects it). It is retained only so that infer_groot_model_version can recognise
+# an N1.5 base path/checkpoint and the N1.7 config/loader can reject the mismatch.
+GROOT_N1_5 = "n1.5"
+# Canonical guidance appended to every error raised when an N1.5 checkpoint, config,
+# or processor pipeline is detected. Keep this message in sync with docs/source/groot.mdx.
+GROOT_N1_5_REMOVAL_GUIDANCE = (
+    "GR00T N1.5 support was removed from LeRobot. "
+    "To keep using an N1.5 checkpoint, pin the last release that supports it: "
+    "`pip install 'lerobot==0.5.1'`. To use the current release, migrate to GR00T N1.7 "
+    "(model_version='n1.7', base model nvidia/GR00T-N1.7-3B)."
+)
+GROOT_N1_7_BASE_MODEL = "nvidia/GR00T-N1.7-3B"
+GROOT_N1_7_BACKBONE_MODEL = "nvidia/Cosmos-Reason2-2B"
+GROOT_ACTION_DECODE_TRANSFORM_LIBERO = "libero"
+# Sentinel meaning "the user did not pick an action decode transform": __post_init__ resolves it
+# to the embodiment default ('libero' for 'libero_sim', otherwise None). It is distinct from an
+# explicit 'none' (resolved to None) so an opt-out survives a draccus save/load round-trip.
+GROOT_ACTION_DECODE_TRANSFORM_AUTO = "auto"
+
+_GROOT_MODEL_VERSION_ALIASES = {
+    "n1.7": GROOT_N1_7,
+    "n1_7": GROOT_N1_7,
+    "n1d7": GROOT_N1_7,
+    "n17": GROOT_N1_7,
+    "1.7": GROOT_N1_7,
+}
+
+# Legacy N1.5 spellings, kept ONLY so they can be detected and rejected with
+# GROOT_N1_5_REMOVAL_GUIDANCE (see GROOT_N1_5 above). Never map these to a supported version.
+_GROOT_N1_5_VERSION_ALIASES = {"n1.5", "n1_5", "n1d5", "n15", "1.5"}
+
+_GROOT_ACTION_DECODE_TRANSFORM_ALIASES = {
+    GROOT_ACTION_DECODE_TRANSFORM_AUTO: GROOT_ACTION_DECODE_TRANSFORM_AUTO,
+    "none": None,
+    "": None,
+    GROOT_ACTION_DECODE_TRANSFORM_LIBERO: GROOT_ACTION_DECODE_TRANSFORM_LIBERO,
+}
+
+
+def normalize_groot_model_version(model_version: str) -> str:
+    normalized = _GROOT_MODEL_VERSION_ALIASES.get(model_version.lower())
+    if normalized is None:
+        supported = GROOT_N1_7
+        message = f"Unsupported GR00T model_version '{model_version}'. Supported versions: {supported}."
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+        raise ValueError(message)
+    return normalized
+
+
+def normalize_groot_action_decode_transform(transform: str | None) -> str | None:
+    if transform is None:
+        return None
+    normalized = _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.get(transform.lower())
+    if normalized is None and transform.lower() not in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES:
+        supported = ", ".join(
+            sorted(key for key, value in _GROOT_ACTION_DECODE_TRANSFORM_ALIASES.items() if value is not None)
+        )
+        raise ValueError(
+            f"Unsupported GR00T N1.7 action decode transform '{transform}'. "
+            f"Supported transforms: none, {supported}."
+        )
+    return normalized
+
+
+def infer_groot_model_version(model_path: str | None) -> str | None:
+    if not model_path:
+        return None
+    model_path_lower = model_path.lower()
+    if "gr00t-n1.7" in model_path_lower or "gr00t_n1.7" in model_path_lower:
+        return GROOT_N1_7
+    # Detect legacy N1.5 paths so the N1.7 config/loader can reject the mismatch.
+    # N1.5 is unsupported, but it must still be recognised here to fail loudly
+    # rather than silently treating an N1.5 checkpoint as N1.7.
+    if "gr00t-n1.5" in model_path_lower or "gr00t_n1.5" in model_path_lower:
+        return GROOT_N1_5
+    config_version = _infer_groot_model_version_from_local_config(model_path)
+    if config_version is not None:
+        return config_version
+    return None
+
+
+def is_raw_groot_n1_7_checkpoint(model_path: str | Path | None) -> bool:
+    if model_path is None:
+        return False
+
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return False
+
+    try:
+        with config_path.open() as f:
+            config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return False
+
+    return "type" not in config and _infer_groot_model_version_from_config(config) == GROOT_N1_7
+
+
+def infer_groot_n1_7_embodiment_tag(model_path: str | Path | None) -> str | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    try:
+        with processor_config_path.open() as f:
+            processor_config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
+
+    modality_configs = processor_config.get("processor_kwargs", {}).get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+    if "libero_sim" in modality_configs:
+        return "libero_sim"
+    if len(modality_configs) == 1:
+        return next(iter(modality_configs))
+    return None
+
+
+def infer_groot_n1_7_action_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    if model_path is None:
+        return None
+
+    processor_config_path = Path(model_path).expanduser() / "processor_config.json"
+    try:
+        with processor_config_path.open() as f:
+            processor_config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
+
+    processor_kwargs = processor_config.get("processor_kwargs", {})
+    if not isinstance(processor_kwargs, dict):
+        return None
+    modality_configs = processor_kwargs.get("modality_configs", {})
+    if not isinstance(modality_configs, dict):
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag is None:
+        return None
+
+    embodiment_config = modality_configs.get(embodiment_tag, {})
+    if not isinstance(embodiment_config, dict):
+        return None
+    action_config = embodiment_config.get("action", {})
+    if not isinstance(action_config, dict):
+        return None
+    delta_indices = action_config.get("delta_indices", [])
+    if not isinstance(delta_indices, list):
+        return None
+    return len(delta_indices) or None
+
+
+def infer_groot_n1_7_action_execution_horizon(
+    model_path: str | Path | None, embodiment_tag: str | None = None
+) -> int | None:
+    action_horizon = infer_groot_n1_7_action_horizon(model_path, embodiment_tag)
+    if action_horizon is None:
+        return None
+
+    if embodiment_tag is None:
+        embodiment_tag = infer_groot_n1_7_embodiment_tag(model_path)
+    if embodiment_tag == "libero_sim":
+        # NVIDIA's N1.7 LIBERO rollout wrapper replans after 8 of the 16 decoded
+        # actions. Keeping that execution cadence avoids stale open-loop chunks.
+        return min(action_horizon, 8)
+    return action_horizon
+
+
+def resolve_groot_n1_7_backbone_model(model_name: str, cache_dir: str | Path | None = None) -> str:
+    model_path = Path(model_name).expanduser()
+    if model_path.exists():
+        return str(model_path)
+
+    cached_snapshot = _find_cached_hf_snapshot(model_name, cache_dir=cache_dir)
+    return str(cached_snapshot) if cached_snapshot is not None else model_name
+
+
+def _find_cached_hf_snapshot(repo_id: str, cache_dir: str | Path | None = None) -> Path | None:
+    repo_cache_name = f"models--{repo_id.replace('/', '--')}"
+    required_files = (
+        "config.json",
+        "tokenizer_config.json",
+        "preprocessor_config.json",
+        "video_preprocessor_config.json",
+    )
+
+    for hub_cache in _candidate_hf_hub_caches(cache_dir):
+        repo_cache = hub_cache / repo_cache_name
+        snapshots_dir = repo_cache / "snapshots"
+        if not snapshots_dir.is_dir():
+            continue
+
+        candidates: list[Path] = []
+        ref_path = repo_cache / "refs" / "main"
+        try:
+            ref = ref_path.read_text().strip()
+        except OSError:
+            ref = ""
+        if ref:
+            candidates.append(snapshots_dir / ref)
+        candidates.extend(
+            sorted(
+                (path for path in snapshots_dir.iterdir() if path.is_dir()),
+                key=lambda path: path.stat().st_mtime,
+                reverse=True,
+            )
+        )
+
+        seen: set[Path] = set()
+        for snapshot in candidates:
+            if snapshot in seen:
+                continue
+            seen.add(snapshot)
+            if all((snapshot / filename).exists() for filename in required_files):
+                return snapshot
+    return None
+
+
+def _candidate_hf_hub_caches(cache_dir: str | Path | None) -> list[Path]:
+    candidates: list[Path] = []
+    if cache_dir is not None:
+        cache_path = Path(cache_dir).expanduser()
+        candidates.append(cache_path)
+        candidates.append(cache_path / "hub")
+
+    hub_cache = os.environ.get("HUGGINGFACE_HUB_CACHE")
+    if hub_cache:
+        candidates.append(Path(hub_cache).expanduser())
+
+    hf_home = os.environ.get("HF_HOME")
+    if hf_home:
+        candidates.append(Path(hf_home).expanduser() / "hub")
+
+    candidates.append(Path.home() / ".cache" / "huggingface" / "hub")
+
+    deduped: list[Path] = []
+    seen: set[Path] = set()
+    for candidate in candidates:
+        resolved = candidate.resolve() if candidate.exists() else candidate
+        if resolved not in seen:
+            seen.add(resolved)
+            deduped.append(candidate)
+    return deduped
+
+
+def _infer_groot_model_version_from_local_config(model_path: str) -> str | None:
+    path = Path(model_path).expanduser()
+    if path.is_dir():
+        config_path = path / "config.json"
+    elif path.name == "config.json":
+        config_path = path
+    else:
+        return None
+
+    if not config_path.exists():
+        return None
+
+    try:
+        with config_path.open() as f:
+            config = json.load(f)
+    except (OSError, json.JSONDecodeError):
+        return None
+
+    return _infer_groot_model_version_from_config(config)
+
+
+def _infer_groot_model_version_from_config(config: dict) -> str | None:
+    model_version = config.get("model_version")
+    if isinstance(model_version, str):
+        if model_version.lower() in _GROOT_N1_5_VERSION_ALIASES:
+            return GROOT_N1_5
+        try:
+            return normalize_groot_model_version(model_version)
+        except ValueError:
+            return None
+
+    candidates = [config.get("model_type"), *(config.get("architectures") or [])]
+    for candidate in candidates:
+        if not isinstance(candidate, str):
+            continue
+        normalized = candidate.lower().replace("-", "_")
+        if normalized in {"gr00tn1d7", "gr00t_n1d7", "gr00t_n1_7"}:
+            return GROOT_N1_7
+        # nvidia/GR00T-N1.5-3B ships model_type 'gr00t_n1_5' and architectures ['GR00T_N1_5'].
+        # Recognise them so N1.5 checkpoints at generic local paths are rejected loudly
+        # instead of being silently treated as N1.7 (see infer_groot_model_version).
+        if normalized in {"gr00t_n1_5", "gr00tn1_5", "gr00t_n15", "gr00t_n1d5", "gr00tn1d5"}:
+            return GROOT_N1_5
+    if config.get("model_name") == GROOT_N1_7_BACKBONE_MODEL:
+        return GROOT_N1_7
+    # The Eagle VLM backbone is specific to pre-N1.7 GR00T checkpoints (N1.7 uses Cosmos/Qwen3-VL).
+    backbone_cfg = config.get("backbone_cfg")
+    if isinstance(backbone_cfg, dict) and "eagle_path" in backbone_cfg:
+        return GROOT_N1_5
+    return None
+

@PreTrainedConfig.register_subclass("groot")
@dataclass
@@ -28,35 +342,53 @@ class GrootConfig(PreTrainedConfig):

    # Basic policy settings
    n_obs_steps: int = 1
-    chunk_size: int = 50
-    n_action_steps: int = 50
+    chunk_size: int = 40
+    n_action_steps: int = 40

    # Dimension settings (must match pretrained GR00T model expectations)
    # Maximum state dimension. Shorter states will be zero-padded.
-    max_state_dim: int = 64
+    max_state_dim: int = 132

    # Maximum action dimension. Shorter actions will be zero-padded.
-    max_action_dim: int = 32
+    max_action_dim: int = 132

-    # Normalization (start with identity, adjust as needed)
+    # GR00T normalizes state/action internally in its processor steps (min/max with
+    # q01/q99 percentiles, per embodiment), and the Qwen3-VL backbone's image processor
+    # handles image normalization. The policy therefore does NOT use LeRobot's
+    # NormalizerProcessorStep/UnnormalizerProcessorStep, so this mapping is intentionally
+    # IDENTITY for every feature and is not consulted by make_groot_pre_post_processors.
    normalization_mapping: dict[str, NormalizationMode] = field(
        default_factory=lambda: {
            "VISUAL": NormalizationMode.IDENTITY,
-            "STATE": NormalizationMode.MEAN_STD,
-            "ACTION": NormalizationMode.MEAN_STD,
+            "STATE": NormalizationMode.IDENTITY,
+            "ACTION": NormalizationMode.IDENTITY,
        }
    )

-    # Image preprocessing (adjust to match Groot's expected input)
-    image_size: tuple[int, int] = (224, 224)
+    # Deprecated and unused: image sizing is handled by the backbone's image processor.
+    # Kept only so config.json files saved with earlier versions still parse.
+    image_size: tuple[int, int] = (256, 256)

    # Groot-specific model parameters (from groot_finetune_script.py)

-    # Path or HuggingFace model ID for the base Groot model
-    base_model_path: str = "nvidia/GR00T-N1.5-3B"
+    # Explicit GR00T model family selection. LeRobot supports GR00T N1.7 only.
+    model_version: str = GROOT_N1_7

-    # HF repo ID (or local path) that hosts vocab.json and merges.txt for Eagle tokenizer.
-    tokenizer_assets_repo: str = "lerobot/eagle2hg-processor-groot-n1p5"
+    # Path or HuggingFace model ID for the base Groot model
+    base_model_path: str | None = None
+
+    # HF repo ID (or local path) for the GR00T N1.7 Cosmos/Qwen3-VL backbone processor.
+    n1_7_backbone_model: str = GROOT_N1_7_BACKBONE_MODEL
+
+    # Optional named action transform applied after raw N1.7 checkpoint decoding and before env.step().
+    # 'auto' (default) resolves to the embodiment default ('libero' for 'libero_sim', otherwise no
+    # transform). Pass 'none' to explicitly disable the transform, including for 'libero_sim'.
+    action_decode_transform: str | None = GROOT_ACTION_DECODE_TRANSFORM_AUTO
+
+    # Deprecated, GR00T N1.5 only — do not set. Kept so config.json files saved by lerobot<=0.5.1
+    # still parse (draccus rejects unknown fields) and can be rejected in __post_init__ with a
+    # clear error pointing at GROOT_N1_5_REMOVAL_GUIDANCE instead of a cryptic DecodingError.
+    tokenizer_assets_repo: str | None = None

    # Embodiment tag to use for training (e.g. 'new_embodiment', 'gr1')
    embodiment_tag: str = "new_embodiment"
@@ -96,17 +428,13 @@ class GrootConfig(PreTrainedConfig):
    warmup_ratio: float = 0.05
    use_bf16: bool = True

-    # Dataset parameters
-    # Video backend to use for training ('decord' or 'torchvision_av')
+    # Deprecated Isaac-GR00T runner fields below — unused by the LeRobot N1.7 implementation
+    # (nothing in src/lerobot reads them). They are kept only so config.json files saved by
+    # earlier lerobot releases still parse: draccus rejects unknown fields, so removing them
+    # would break every previously saved groot checkpoint at config-load time.
    video_backend: str = "decord"
-
-    # Whether to balance dataset weights in mixture datasets
    balance_dataset_weights: bool = True
-
-    # Whether to sample trajectories weighted by their length
    balance_trajectory_weights: bool = True
-
-    # Optional dataset paths for delegating training to Isaac-GR00T runner
    dataset_paths: list[str] | None = None
    output_dir: str = "./tmp/gr00t"
    save_steps: int = 1000
@@ -117,6 +445,69 @@ class GrootConfig(PreTrainedConfig):
    resume: bool = False

    def __post_init__(self):
+        # 'tokenizer_assets_repo' only ever existed for GR00T N1.5 (lerobot<=0.5.1) and was
+        # serialized into every groot checkpoint config.json, so a value here means a legacy
+        # N1.5 checkpoint or config is being loaded.
+        if self.tokenizer_assets_repo is not None:
+            raise ValueError(
+                "Config sets 'tokenizer_assets_repo', which only existed for GR00T N1.5; this looks "
+                f"like a legacy GR00T N1.5 checkpoint or config. {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            )
+
+        self.model_version = normalize_groot_model_version(self.model_version)
+        self.action_decode_transform = normalize_groot_action_decode_transform(self.action_decode_transform)
+        if self.base_model_path is None:
+            self.base_model_path = GROOT_N1_7_BASE_MODEL
+
+        # The N1.7 LIBERO checkpoints emit a [0, 1] gripper action, but the LIBERO
+        # simulator expects the OpenVLA/[-1, 1] sign convention. NVIDIA's rollout
+        # wrapper applies this conversion; mirror it here so eval on the
+        # 'libero_sim' embodiment grasps correctly instead of scoring 0% success.
+        # This matches the embodiment-specific handling already done for the
+        # action execution horizon (see infer_groot_n1_7_action_execution_horizon).
+        # Only the 'auto' sentinel resolves to the embodiment default; an explicit
+        # 'none' (normalized to None above) keeps the transform disabled.
+        if self.action_decode_transform == GROOT_ACTION_DECODE_TRANSFORM_AUTO:
+            self.action_decode_transform = (
+                GROOT_ACTION_DECODE_TRANSFORM_LIBERO if self.embodiment_tag == "libero_sim" else None
+            )
+
+        # GR00T N1.5-era default values (e.g. --policy.chunk_size=50 from old commands or
+        # stale configs) are migrated to the values the N1.7 checkpoints expect, with a
+        # warning. The dataclass defaults are already the N1.7 values, so a plain
+        # GrootConfig() never triggers this.
+        legacy_default_remaps = (
+            ("max_state_dim", 64, 132),
+            ("max_action_dim", 32, 132),
+            ("chunk_size", 50, 40),
+            ("n_action_steps", 50, 40),
+            ("image_size", (224, 224), (256, 256)),
+        )
+        for field_name, legacy_value, n1_7_value in legacy_default_remaps:
+            current_value = getattr(self, field_name)
+            if isinstance(legacy_value, tuple):
+                current_value = tuple(current_value)
+            if current_value == legacy_value:
+                logger.warning(
+                    "GrootConfig.%s=%s matches a legacy GR00T N1.5-era default; remapping it to %s, "
+                    "the value expected by GR00T N1.7 checkpoints. Set a different value explicitly "
+                    "if this is not what you want.",
+                    field_name,
+                    legacy_value,
+                    n1_7_value,
+                )
+                setattr(self, field_name, n1_7_value)
+
+        inferred_version = infer_groot_model_version(self.base_model_path)
+        if inferred_version is not None and inferred_version != self.model_version:
+            message = (
+                f"GR00T model_version '{self.model_version}' does not match base_model_path "
+                f"'{self.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
+
        super().__post_init__()

        if self.n_action_steps > self.chunk_size:
@@ -191,8 +582,22 @@ class GrootConfig(PreTrainedConfig):

    @property
    def action_delta_indices(self) -> list[int]:
-        """Return indices for delta actions."""
-        return list(range(min(self.chunk_size, 16)))
+        """Return indices for delta actions.
+
+        The model action horizon is read from the checkpoint's processor_config.json
+        when available; the result is cached (keyed on the inputs that determine it) so
+        repeated access during dataset/training setup does not re-read from disk.
+        """
+        cache_key = (self.base_model_path, self.embodiment_tag, self.chunk_size)
+        cached = getattr(self, "_action_delta_indices_cache", None)
+        if cached is not None and cached[0] == cache_key:
+            return cached[1]
+        model_action_horizon = (
+            infer_groot_n1_7_action_horizon(self.base_model_path, self.embodiment_tag) or 40
+        )
+        indices = list(range(min(self.chunk_size, model_action_horizon)))
+        object.__setattr__(self, "_action_delta_indices_cache", (cache_key, indices))
+        return indices

    @property
    def reward_delta_indices(self) -> None:
@@ -1,135 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import copy
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.models.llama.configuration_llama import LlamaConfig
-from transformers.models.qwen2.configuration_qwen2 import Qwen2Config
-from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
-from transformers.models.siglip.configuration_siglip import SiglipVisionConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class Eagle25VLConfig(PretrainedConfig):
-    model_type = "eagle_2_5_vl"
-    is_composition = True
-    sub_configs = {"vision_config": SiglipVisionConfig, "text_config": Qwen2Config}
-
-    def __init__(
-        self,
-        vision_config=None,
-        text_config=None,
-        use_backbone_lora=0,
-        use_llm_lora=0,
-        pad2square=False,
-        select_layer=-4,
-        force_image_size=None,
-        downsample_ratio=0.5,
-        template=None,
-        dynamic_image_size=False,
-        use_thumbnail=False,
-        loss_version="v1",
-        min_dynamic_tiles=1,
-        max_dynamic_tiles=6,
-        mlp_checkpoint=False,
-        initializer_range=0.02,
-        _attn_implementation="flash_attention_2",
-        _attn_implementation_autoset=False,
-        llm_config=None,
-        image_token_index=None,
-        use_pixel_shuffle=True,
-        mlp_connector_layers=2,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-
-        if vision_config is None:
-            vision_config = {"model_type": "siglip_vision_model"}
-            logger.info("vision_config is None. Initializing the InternVisionConfig with default values.")
-
-        if text_config is None:
-            text_config = {"architectures": ["Qwen2ForCausalLM"]}
-            logger.info(
-                "text_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`)."
-            )
-
-        if vision_config["model_type"] == "siglip_vision_model":
-            self.vision_config = SiglipVisionConfig(**vision_config)
-        else:
-            raise ValueError("Unsupported model_type: {}".format(vision_config["model_type"]))
-
-        if text_config["architectures"][0] == "LlamaForCausalLM":
-            self.text_config = LlamaConfig(**text_config)
-        elif text_config["architectures"][0] == "Qwen2ForCausalLM":
-            self.text_config = Qwen2Config(**text_config)
-        elif text_config["architectures"][0] == "Qwen3ForCausalLM":
-            self.text_config = Qwen3Config(**text_config)
-        else:
-            raise ValueError("Unsupported architecture: {}".format(text_config["architectures"][0]))
-        self.use_backbone_lora = use_backbone_lora
-        self.use_llm_lora = use_llm_lora
-        self.mlp_checkpoint = mlp_checkpoint
-        self.pad2square = pad2square
-        self.select_layer = select_layer
-        self.force_image_size = force_image_size
-        self.downsample_ratio = downsample_ratio
-        self.template = template
-        self.dynamic_image_size = dynamic_image_size
-        self.use_thumbnail = use_thumbnail
-        self.loss_version = loss_version
-        self.initializer_range = initializer_range
-        self.min_dynamic_tiles = min_dynamic_tiles
-        self.max_dynamic_tiles = max_dynamic_tiles
-        self.tie_word_embeddings = self.text_config.tie_word_embeddings
-        self._attn_implementation = _attn_implementation
-        self._attn_implementation_autoset = _attn_implementation_autoset
-        self.image_token_index = image_token_index
-        self.use_pixel_shuffle = use_pixel_shuffle
-        self.mlp_connector_layers = mlp_connector_layers
-        logger.info(f"min_dynamic_tiles: {self.min_dynamic_tiles}")
-        logger.info(f"max_dynamic_tiles: {self.max_dynamic_tiles}")
-
-    def to_dict(self):
-        """
-        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
-
-        Returns:
-            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
-        """
-        output = copy.deepcopy(self.__dict__)
-        output["vision_config"] = self.vision_config.to_dict()
-        output["text_config"] = self.text_config.to_dict()
-        output["model_type"] = self.__class__.model_type
-        output["use_backbone_lora"] = self.use_backbone_lora
-        output["use_llm_lora"] = self.use_llm_lora
-        output["pad2square"] = self.pad2square
-        output["select_layer"] = self.select_layer
-        output["force_image_size"] = self.force_image_size
-        output["downsample_ratio"] = self.downsample_ratio
-        output["template"] = self.template
-        output["dynamic_image_size"] = self.dynamic_image_size
-        output["use_thumbnail"] = self.use_thumbnail
-        output["min_dynamic_tiles"] = self.min_dynamic_tiles
-        output["max_dynamic_tiles"] = self.max_dynamic_tiles
-        output["tie_word_embeddings"] = self.tie_word_embeddings
-        output["_attn_implementation"] = self._attn_implementation
-        output["_attn_implementation_autoset"] = self._attn_implementation_autoset
-        output["use_pixel_shuffle"] = self.use_pixel_shuffle
-        output["mlp_connector_layers"] = self.mlp_connector_layers
-        return output
@@ -1,503 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-from __future__ import annotations
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
-from transformers.image_processing_utils import (
-    BatchFeature,
-    get_patch_output_size,
-)
-from transformers.image_processing_utils_fast import (
-    BaseImageProcessorFast,
-    ImagesKwargs,
-    group_images_by_shape,
-    reorder_images,
-)
-from transformers.image_utils import (
-    IMAGENET_STANDARD_MEAN,  # 0.5, 0.5, 0.5
-    IMAGENET_STANDARD_STD,  # 0.5, 0.5, 0.5
-    ChannelDimension,
-    ImageInput,
-    PILImageResampling,
-    SizeDict,
-    get_image_size,
-    make_flat_list_of_images,
-    validate_kwargs,
-)
-from transformers.processing_utils import Unpack
-from transformers.utils import (
-    TensorType,
-    add_start_docstrings,
-    is_torch_available,
-    is_torchvision_v2_available,
-)
-from transformers.video_utils import VideoInput
-
-if is_torch_available():
-    import torch
-if is_torchvision_v2_available():
-    from torchvision.transforms.v2 import functional as F  # noqa: N812
-    from transformers.image_utils import pil_torch_interpolation_mapping
-else:
-    from torchvision.transforms import functional as F  # noqa: N812
-
-
-def crop(img: torch.Tensor, left: int, top: int, right: int, bottom: int) -> torch.Tensor:
-    """Crop the given numpy array.
-
-    Args:
-        img (torch.Tensor): Image to be cropped. Format should be (C, H, W).
-        left (int): The left coordinate of the crop box.
-        top (int): The top coordinate of the crop box.
-        right (int): The right coordinate of the crop box.
-        bottom (int): The bottom coordinate of the crop box.
-
-    Returns:
-        torch.Tensor: Cropped image.
-    """
-    if not isinstance(img, torch.Tensor):
-        raise TypeError(f"img should be torch.Tensor. Got {type(img)}")
-
-    if img.ndim not in [2, 3]:
-        raise ValueError(f"Image should have 2 or 3 dimensions. Got {img.ndim}")
-
-    img_height = img.shape[1]
-    img_width = img.shape[2]
-    if top < 0 or left < 0 or bottom > img_height or right > img_width:
-        raise ValueError("Crop coordinates out of bounds")
-
-    if top >= bottom or left >= right:
-        raise ValueError("Invalid crop coordinates")
-
-    return img[:, top:bottom, left:right]
-
-
-class Eagle25VLFastImageProcessorKwargs(ImagesKwargs):
-    max_dynamic_tiles: int | None
-    min_dynamic_tiles: int | None
-    use_thumbnail: bool | None
-    pad_during_tiling: bool | None
-    do_pad: bool | None
-
-
-@add_start_docstrings(
-    "Constructs a fast ConvNeXT image processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.",
-    # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING, TODO: this was depreciated from transformers remove!
-    """
-        image_grid_pinpoints (`List[List[int]]`, *optional*):
-            A list of possible resolutions to use for processing high resolution images. The best resolution is selected
-            based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
-            method. Not used for processing videos.
-        do_pad (`bool`, *optional*):
-            Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-            number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-    """,
-)
-class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
-    resample = PILImageResampling.BICUBIC
-    image_mean = IMAGENET_STANDARD_MEAN
-    image_std = IMAGENET_STANDARD_STD
-    size = {"height": 448, "width": 448}
-    default_to_square = False
-    crop_size = None
-    do_resize = True
-    do_center_crop = None
-    do_rescale = True
-    do_normalize = True
-    do_convert_rgb = True
-    do_pad = True
-    max_dynamic_tiles = 12
-    min_dynamic_tiles = 1
-    use_thumbnail = True
-    pad_during_tiling = False
-    valid_kwargs = Eagle25VLFastImageProcessorKwargs
-    model_input_names = ["pixel_values_videos"]
-
-    def __init__(self, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]):
-        super().__init__(**kwargs)
-
-    @add_start_docstrings(
-        # BASE_IMAGE_PROCESSOR_FAST_DOCSTRING_PREPROCESS, TODO: this was depreciated from transformers remove!
-        """
-            max_dynamic_tiles (`int`, *optional*):
-                The maximum number of dynamic tiles to use for processing high resolution images.
-            min_dynamic_tiles (`int`, *optional*):
-                The minimum number of dynamic tiles to use for processing high resolution images.
-            use_thumbnail (`bool`, *optional*):
-                Whether to use a thumbnail for processing high resolution images.
-            pad_during_tiling (`bool`, *optional*):
-                Whether to pad the image during tiling.
-            do_pad (`bool`, *optional*):
-                    Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
-                    number of patches in the batch. Padding will be applied to the bottom and right with zeros.
-        """,
-    )
-
-    # NOTE(YL): we will overload the preprocess method to add the image_flags
-    # def preprocess(
-    #     self, images: ImageInput, **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs]
-    # ) -> BatchFeature:
-    #     return super().preprocess(images, **kwargs)
-
-    def _prepare_images_structure(
-        self,
-        images: ImageInput,
-        expected_ndims: int = 3,
-    ) -> ImageInput:
-        """
-        Prepare the images structure for processing.
-
-        Args:
-            images (`ImageInput`):
-                The input images to process.
-            expected_ndims (`int`, *optional*, defaults to 3):
-                Expected number of dimensions for the images (added for transformers >=4.53.0 compatibility).
-
-        Returns:
-            `ImageInput`: The images with a valid nesting.
-        """
-        return make_flat_list_of_images(images)
-
-    def _resize_for_patching(
-        self,
-        image: torch.Tensor,
-        target_resolution: tuple,
-        interpolation: F.InterpolationMode,
-        input_data_format: ChannelDimension,
-    ) -> torch.Tensor:
-        """
-        Resizes an image to a target resolution while maintaining aspect ratio.
-
-        Args:
-            image ("torch.Tensor"):
-                The input image.
-            target_resolution (tuple):
-                The target resolution (height, width) of the image.
-            interpolation (`InterpolationMode`):
-                Resampling filter to use if resizing the image.
-            input_data_format (`ChannelDimension` or `str`):
-                The channel dimension format of the input image.
-
-        Returns:
-            "torch.Tensor": The resized and padded image.
-        """
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        # Resize the image
-        resized_image = F.resize(image, (new_height, new_width), interpolation=interpolation)
-
-        return resized_image
-
-    def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size):
-        """
-        previous version mainly focus on ratio.
-        We also consider area ratio here.
-        """
-        best_factor = float("-inf")
-        best_ratio = (1, 1)
-        area = width * height
-        for ratio in target_ratios:
-            target_aspect_ratio = ratio[0] / ratio[1]
-            # ratio_diff = abs(aspect_ratio - target_aspect_ratio)
-            # area_ratio = (ratio[0] * ratio[1] * image_size * image_size) / area
-            """
-            new area > 60% of original image area is enough.
-            """
-            factor_based_on_area_n_ratio = min(
-                (ratio[0] * ratio[1] * image_size * image_size) / area, 0.6
-            ) * min(target_aspect_ratio / aspect_ratio, aspect_ratio / target_aspect_ratio)
-
-            if factor_based_on_area_n_ratio > best_factor:
-                best_factor = factor_based_on_area_n_ratio
-                best_ratio = ratio
-
-        return best_ratio
-
-    def _pad_for_patching(
-        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> torch.Tensor:
-        """
-        Pad an image to a target resolution while maintaining aspect ratio.
-        """
-        target_height, target_width = target_resolution
-        new_height, new_width = get_patch_output_size(image, target_resolution, input_data_format)
-
-        paste_x = (target_width - new_width) // 2
-        paste_y = (target_height - new_height) // 2
-
-        padded_image = F.pad(image, padding=[paste_x, paste_y, paste_x, paste_y])
-
-        return padded_image
-
-    def _get_image_patches(
-        self,
-        image: torch.Tensor,
-        min_num: int,
-        max_num: int,
-        size: tuple,
-        tile_size: int,
-        use_thumbnail: bool,
-        interpolation: F.InterpolationMode,
-        pad_during_tiling: bool,
-    ) -> list[torch.Tensor]:
-        image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-
-        # calculate the target width and height
-        target_width = tile_size * target_aspect_ratio[0]
-        target_height = tile_size * target_aspect_ratio[1]
-        blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if pad_during_tiling:
-            resized_image = self._resize_for_patching(
-                image,
-                (target_height, target_width),
-                interpolation=interpolation,
-                input_data_format=ChannelDimension.FIRST,
-            )
-            padded_image = self._pad_for_patching(
-                resized_image,
-                (target_height, target_width),
-                input_data_format=ChannelDimension.FIRST,
-            )
-            image_used_to_split = padded_image
-        else:
-            image_used_to_split = F.resize(image, (target_height, target_width), interpolation=interpolation)
-
-        processed_tiles = []
-        for i in range(blocks):
-            box = (
-                (i % (target_width // tile_size)) * tile_size,
-                (i // (target_width // tile_size)) * tile_size,
-                ((i % (target_width // tile_size)) + 1) * tile_size,
-                ((i // (target_width // tile_size)) + 1) * tile_size,
-            )
-            # split the image
-            split_img = crop(image_used_to_split, box[0], box[1], box[2], box[3])
-            processed_tiles.append(split_img)
-        assert len(processed_tiles) == blocks
-
-        if use_thumbnail and len(processed_tiles) != 1:
-            thumbnail_img = F.resize(image, (tile_size, tile_size), interpolation=interpolation)
-            processed_tiles.append(thumbnail_img)
-
-        return processed_tiles
-
-    def _pad_for_batching(
-        self,
-        pixel_values: list[torch.Tensor],
-    ) -> list[torch.Tensor]:
-        """
-        Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
-
-        Args:
-            pixel_values (`List[torch.Tensor]`):
-                An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
-
-        Returns:
-            List[`torch.Tensor`]: The padded images.
-        """
-        max_patch = max(len(x) for x in pixel_values)
-        pixel_values = [
-            torch.nn.functional.pad(image, pad=[0, 0, 0, 0, 0, 0, 0, max_patch - image.shape[0]])
-            for image in pixel_values
-        ]
-
-        return pixel_values
-
-    def _preprocess(
-        self,
-        images: list[torch.Tensor],
-        do_resize: bool,
-        size: SizeDict,
-        max_dynamic_tiles: int,
-        min_dynamic_tiles: int,
-        use_thumbnail: bool,
-        pad_during_tiling: bool,
-        interpolation: F.InterpolationMode | None,
-        do_center_crop: bool,
-        crop_size: SizeDict,
-        do_rescale: bool,
-        rescale_factor: float,
-        do_normalize: bool,
-        image_mean: float | list[float] | None,
-        image_std: float | list[float] | None,
-        do_pad: bool,
-        return_tensors: str | TensorType | None,
-        pad_size: SizeDict | None = None,  # Added for transformers >=4.53.0 compatibility
-        disable_grouping: bool | None = None,  # Added for transformers >=4.53.0 compatibility
-    ) -> BatchFeature:
-        processed_images = []
-        image_sizes = []
-        # Determine the size tuple
-        if size and size.height and size.width:
-            size_tuple = (size.height, size.width)
-        else:
-            size_tuple = (size.shortest_edge, size.shortest_edge)
-
-        # Determine the patch size
-        if crop_size and crop_size.height:
-            tile_size = crop_size.height
-        elif size and size.height:
-            tile_size = size.height
-        else:
-            tile_size = size.shortest_edge
-
-        for image in images:
-            image_patches = self._get_image_patches(
-                image,
-                min_num=min_dynamic_tiles,
-                max_num=max_dynamic_tiles,
-                size=size_tuple,
-                tile_size=tile_size,
-                use_thumbnail=use_thumbnail,
-                interpolation=interpolation,
-                pad_during_tiling=pad_during_tiling,
-            )
-
-            # Group images by size for batched processing
-            processed_image_patches_grouped = {}
-            # Added for transformers >=4.53.0 compatibility
-            grouped_image_patches, grouped_image_patches_index = group_images_by_shape(
-                image_patches,
-                disable_grouping=disable_grouping,
-            )
-
-            for shape, stacked_image_patches in grouped_image_patches.items():
-                if do_resize:
-                    stacked_image_patches = self.resize(
-                        image=stacked_image_patches,
-                        size=size,
-                        interpolation=interpolation,
-                    )
-                if do_center_crop:
-                    stacked_image_patches = self.center_crop(stacked_image_patches, crop_size)
-                # Fused rescale and normalize
-                stacked_image_patches = self.rescale_and_normalize(
-                    stacked_image_patches,
-                    do_rescale,
-                    rescale_factor,
-                    do_normalize,
-                    image_mean,
-                    image_std,
-                )
-                processed_image_patches_grouped[shape] = stacked_image_patches
-            processed_image_patches = reorder_images(
-                processed_image_patches_grouped, grouped_image_patches_index
-            )
-            processed_image_patches = (
-                torch.stack(processed_image_patches, dim=0) if return_tensors else processed_image_patches
-            )
-            processed_images.append(processed_image_patches)
-            image_sizes.append(get_image_size(image, ChannelDimension.FIRST))
-
-        if do_pad:
-            processed_images = self._pad_for_batching(processed_images)
-
-        # processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
-        processed_images = torch.cat(processed_images, dim=0) if return_tensors else processed_images
-        return BatchFeature(
-            data={"pixel_values": processed_images, "image_sizes": image_sizes},
-            tensor_type=return_tensors,
-        )
-
-    def preprocess(
-        self,
-        images: ImageInput,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLFastImageProcessorKwargs],
-    ) -> BatchFeature:
-        validate_kwargs(
-            captured_kwargs=kwargs.keys(),
-            valid_processor_keys=self.valid_kwargs.__annotations__.keys(),
-        )
-        # Set default kwargs from self. This ensures that if a kwarg is not provided
-        # by the user, it gets its default value from the instance, or is set to None.
-        for kwarg_name in self.valid_kwargs.__annotations__:
-            kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
-
-        # Extract parameters that are only used for preparing the input images
-        do_convert_rgb = kwargs.pop("do_convert_rgb")
-        input_data_format = kwargs.pop("input_data_format")
-        device = kwargs.pop("device")
-        # Prepare input images
-        # transformers >= 4.53.0: uses _prepare_image_like_inputs instead of _prepare_input_images
-        if images is not None:
-            images = self._prepare_image_like_inputs(
-                images=images,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        if videos is not None:
-            videos = self._prepare_image_like_inputs(
-                images=videos,
-                do_convert_rgb=do_convert_rgb,
-                input_data_format=input_data_format,
-                device=device,
-            )
-
-        # Update kwargs that need further processing before being validated
-        kwargs = self._further_process_kwargs(**kwargs)
-
-        # Validate kwargs
-        self._validate_preprocess_kwargs(**kwargs)
-
-        # torch resize uses interpolation instead of resample
-        # Added for transformers >=4.53.0 compatibility
-        resample = kwargs.pop("resample", self.resample)
-        kwargs["interpolation"] = (
-            pil_torch_interpolation_mapping[resample]
-            if isinstance(resample, PILImageResampling | int)
-            else resample
-        )
-
-        # Filter kwargs to only include those accepted by _preprocess
-        valid_preprocess_kwargs = {
-            "do_resize",
-            "size",
-            "max_dynamic_tiles",
-            "min_dynamic_tiles",
-            "use_thumbnail",
-            "pad_during_tiling",
-            "interpolation",
-            "do_center_crop",
-            "crop_size",
-            "do_rescale",
-            "rescale_factor",
-            "do_normalize",
-            "image_mean",
-            "image_std",
-            "do_pad",
-            "return_tensors",
-            "pad_size",
-            "disable_grouping",
-        }
-        filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_preprocess_kwargs}
-        if images is not None:
-            return self._preprocess(images, **filtered_kwargs)
-        elif videos is not None:
-            return self._preprocess(videos, **filtered_kwargs)
-
-
-__all__ = ["Eagle25VLImageProcessorFast"]
@@ -1,395 +0,0 @@
-# --------------------------------------------------------
-# NVIDIA
-# Copyright (c) 2025 NVIDIA
-# Licensed under The MIT License [see LICENSE for details]
-# --------------------------------------------------------
-
-import inspect
-
-import torch
-import torch.utils.checkpoint as cp
-from peft import LoraConfig, get_peft_model
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from transformers import GenerationConfig
-from transformers.generation import GenerationMixin
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.modeling_utils import PreTrainedModel
-from transformers.models.llama.modeling_llama import LlamaForCausalLM
-from transformers.models.qwen2.modeling_qwen2 import Qwen2ForCausalLM
-from transformers.models.qwen3.modeling_qwen3 import Qwen3ForCausalLM
-from transformers.models.siglip.modeling_siglip import SiglipVisionModel
-from transformers.utils import add_start_docstrings, logging
-
-from .configuration_eagle2_5_vl import Eagle25VLConfig
-
-logger = logging.get_logger(__name__)
-
-
-# copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/modeling_llava_onevision.py#L241C1-L280C1
-EAGLE2_5_VL_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`Eagle25VLConfig`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare Eagle2_5_VL Model outputting raw hidden-states without any specific head on top.",
-    EAGLE2_5_VL_START_DOCSTRING,
-)
-class Eagle25VLPreTrainedModel(PreTrainedModel):
-    config_class = Eagle25VLConfig
-    base_model_prefix = "model"
-    main_input_name = "input_ids"
-    supports_gradient_checkpointing = True
-    _no_split_modules = [
-        "Qwen2DecoderLayer",
-        "LlamaDecoderLayer",
-        "Siglip2EncoderLayer",
-        "SiglipEncoderLayer",
-    ]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_cache_class = True
-    _supports_static_cache = True
-    _supports_quantized_cache = True
-    _supports_sdpa = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear | nn.Conv2d):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-
-class Eagle25VLForConditionalGeneration(Eagle25VLPreTrainedModel, GenerationMixin):
-    config_class = Eagle25VLConfig
-
-    def __init__(self, config: Eagle25VLConfig, vision_model=None, language_model=None):
-        super().__init__(config)
-
-        image_size = config.force_image_size or config.vision_config.image_size
-        patch_size = config.vision_config.patch_size
-        self.patch_size = patch_size
-        if config.use_pixel_shuffle:
-            self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio**2))
-        else:
-            self.num_image_token = int((image_size // patch_size) ** 2)
-
-        self.select_layer = config.select_layer
-        self.downsample_ratio = config.downsample_ratio
-        self.loss_version = config.loss_version
-        self.mlp_checkpoint = config.mlp_checkpoint
-        self.use_pixel_shuffle = config.use_pixel_shuffle
-        self.mlp_connector_layers = config.mlp_connector_layers
-        logger.info(f"num_image_token: {self.num_image_token}")
-        logger.info(f"mlp_checkpoint: {self.mlp_checkpoint}")
-        if vision_model is not None:
-            self.vision_model = vision_model
-        else:
-            if config.vision_config.model_type == "siglip_vision_model":
-                config.vision_config._attn_implementation = "flash_attention_2"
-                self.vision_model = SiglipVisionModel(config.vision_config)
-            else:
-                raise NotImplementedError(f"{config.vision_config.model_type} is not implemented.")
-
-        if language_model is not None:
-            self.language_model = language_model
-        else:
-            if config.text_config.architectures[0] == "LlamaForCausalLM":
-                self.language_model = LlamaForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Phi3ForCausalLM":
-                raise NotImplementedError("Phi3 is not implemented.")
-                # self.language_model = Phi3ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen2ForCausalLM":
-                assert config.text_config._attn_implementation == "flash_attention_2", (
-                    f"Qwen2 must use flash_attention_2 but got {config.text_config._attn_implementation}"
-                )
-                self.language_model = Qwen2ForCausalLM(config.text_config)
-            elif config.text_config.architectures[0] == "Qwen3ForCausalLM":
-                self.language_model = Qwen3ForCausalLM(config.text_config)
-            else:
-                raise NotImplementedError(f"{config.text_config.architectures[0]} is not implemented.")
-
-        vit_hidden_size = config.vision_config.hidden_size
-        llm_hidden_size = config.text_config.hidden_size
-
-        if config.mlp_connector_layers == 2:
-            self.mlp1 = nn.Sequential(
-                nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-                nn.GELU(),
-                nn.Linear(llm_hidden_size, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
-            )
-        elif config.mlp_connector_layers == 1 and not config.use_pixel_shuffle:
-            self.mlp1 = nn.Sequential(
-                nn.Linear(vit_hidden_size, llm_hidden_size),
-            )
-        else:
-            raise NotImplementedError(f"{config.mlp_connector_layers} is not implemented.")
-
-        self.image_token_index = config.image_token_index
-        self.neftune_alpha = None
-
-        if config.use_backbone_lora:
-            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
-
-        self.use_llm_lora = config.use_llm_lora
-        if config.use_llm_lora:
-            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
-
-        self.check_forward_kwargs()
-
-    def check_forward_kwargs(self):
-        # We intentionally avoid using **kwargs in forward because Hugging Face Transformers
-        # has special handling for functions with **kwargs parameters that would affect
-        # how our model is processed during training and inference.
-        forward_params = inspect.signature(self.forward).parameters
-        assert not any(k.kind == inspect.Parameter.VAR_KEYWORD for k in forward_params.values())
-
-    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.out_proj",
-                "mlp.fc1",
-                "mlp.fc2",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-        )
-        self.vision_model = get_peft_model(self.vision_model, lora_config)
-        self.vision_model.print_trainable_parameters()
-
-    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
-        lora_config = LoraConfig(
-            r=r,
-            target_modules=[
-                "self_attn.q_proj",
-                "self_attn.k_proj",
-                "self_attn.v_proj",
-                "self_attn.o_proj",
-                "mlp.gate_proj",
-                "mlp.down_proj",
-                "mlp.up_proj",
-            ],
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-            task_type="CAUSAL_LM",
-        )
-        self.language_model = get_peft_model(self.language_model, lora_config)
-        self.language_model.enable_input_require_grads()
-        self.language_model.print_trainable_parameters()
-        self.use_llm_lora = True
-
-    def forward(
-        self,
-        pixel_values: torch.FloatTensor,
-        input_ids: torch.LongTensor = None,
-        attention_mask: torch.Tensor | None = None,
-        position_ids: torch.LongTensor | None = None,
-        image_flags: torch.LongTensor | None = None,
-        past_key_values: list[torch.FloatTensor] | None = None,
-        labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        num_tiles_list: list[torch.Tensor] | None = None,
-    ) -> tuple | CausalLMOutputWithPast:
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        vit_embeds = self.extract_feature(pixel_values)
-
-        if image_flags is not None:
-            image_flags = image_flags.view(-1)
-            vit_embeds = vit_embeds[image_flags == 1]
-
-        b, n, c = input_embeds.shape
-        input_embeds = input_embeds.reshape(b * n, c)
-
-        input_ids = input_ids.reshape(b * n)
-        selected = input_ids == self.image_token_index
-        try:
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, c)
-        except Exception as e:
-            vit_embeds = vit_embeds.reshape(-1, c)
-            print(
-                f"warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, "
-                f"vit_embeds.shape={vit_embeds.shape}"
-            )
-            n_token = selected.sum()
-            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
-
-        input_embeds = input_embeds.reshape(b, n, c)
-
-        outputs = self.language_model(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        logits = outputs.logits
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def pixel_shuffle(self, x, scale_factor=0.5):
-        n, w, h, c = x.size()
-        # N, W, H, C --> N, W, H * scale, C // scale
-        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
-        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
-        x = x.permute(0, 2, 1, 3).contiguous()
-        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
-        x = x.view(n, int(h * scale_factor), int(w * scale_factor), int(c / (scale_factor * scale_factor)))
-
-        x = x.permute(0, 2, 1, 3).contiguous()
-        return x
-
-    def extract_feature(self, pixel_values):
-        if self.select_layer == -1:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=False, return_dict=True
-            )
-            if hasattr(vit_embeds, "last_hidden_state"):
-                vit_embeds = vit_embeds.last_hidden_state
-
-        else:
-            vit_embeds = self.vision_model(
-                pixel_values=pixel_values, output_hidden_states=True, return_dict=True
-            ).hidden_states[self.select_layer]
-
-        if self.use_pixel_shuffle:
-            h = w = int(vit_embeds.shape[1] ** 0.5)
-            vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
-            vit_embeds = self.pixel_shuffle(
-                vit_embeds, scale_factor=self.downsample_ratio
-            )  # torch.Size([B, 1024, 1024]) -> torch.Size([B, 16, 16, 4096])
-            vit_embeds = vit_embeds.reshape(
-                vit_embeds.shape[0], -1, vit_embeds.shape[-1]
-            )  # torch.Size([B, 16, 16, 4096]) -> torch.Size([B, 256, 4096])
-
-        if self.mlp_checkpoint and vit_embeds.requires_grad:
-            vit_embeds = cp.checkpoint(self.mlp1, vit_embeds)
-        else:
-            vit_embeds = self.mlp1(vit_embeds)
-
-        return vit_embeds
-
-    @torch.no_grad()
-    def generate(
-        self,
-        pixel_values: torch.FloatTensor | None = None,
-        input_ids: torch.FloatTensor | None = None,
-        attention_mask: torch.LongTensor | None = None,
-        visual_features: torch.FloatTensor | None = None,
-        generation_config: GenerationConfig | None = None,
-        output_hidden_states: bool | None = None,
-        image_sizes: list[tuple[int, int]] | None = None,
-        **generate_kwargs,
-    ) -> torch.LongTensor:
-        if pixel_values is not None:
-            if visual_features is not None:
-                vit_embeds = visual_features
-            else:
-                vit_embeds = self.extract_feature(pixel_values)
-
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-            b, n, c = input_embeds.shape
-            input_embeds = input_embeds.reshape(b * n, c)
-
-            input_ids = input_ids.reshape(b * n)
-            selected = input_ids == self.config.image_token_index
-            assert selected.sum() != 0
-            input_embeds[selected] = vit_embeds.reshape(-1, c).to(input_embeds.device)
-
-            input_embeds = input_embeds.reshape(b, n, c)
-        else:
-            input_embeds = self.language_model.get_input_embeddings()(input_ids)
-
-        if "use_cache" not in generate_kwargs:
-            generate_kwargs["use_cache"] = True
-
-        outputs = self.language_model.generate(
-            inputs_embeds=input_embeds,
-            attention_mask=attention_mask,
-            generation_config=generation_config,
-            output_hidden_states=output_hidden_states,
-            **generate_kwargs,
-        )
-
-        return outputs
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings
-    def get_input_embeddings(self):
-        return self.language_model.get_input_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_input_embeddings
-    def set_input_embeddings(self, value):
-        self.language_model.set_input_embeddings(value)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_output_embeddings
-    def get_output_embeddings(self):
-        return self.language_model.get_output_embeddings()
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_output_embeddings
-    def set_output_embeddings(self, new_embeddings):
-        self.language_model.set_output_embeddings(new_embeddings)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder
-    def set_decoder(self, decoder):
-        self.language_model.set_decoder(decoder)
-
-    # Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder
-    def get_decoder(self):
-        return self.language_model.get_decoder()
@@ -1,542 +0,0 @@
-# Copyright 2024 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Processor class for Eagle25VL.
-copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/processing_llava_onevision.py
-"""
-
-import base64
-import os
-import re
-from io import BytesIO
-
-import requests
-import torch
-from PIL import Image
-from transformers.feature_extraction_utils import BatchFeature
-from transformers.image_utils import ImageInput
-from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-from transformers.utils import logging
-from transformers.video_utils import VideoInput
-
-logger = logging.get_logger(__name__)
-
-
-FRAME_FACTOR = 2
-FPS = 2.0
-FPS_MIN_FRAMES = 4
-FPS_MAX_FRAMES = 256
-
-
-def to_rgb(pil_image: Image.Image) -> Image.Image:
-    if pil_image.mode == "RGBA":
-        white_background = Image.new("RGB", pil_image.size, (255, 255, 255))
-        white_background.paste(pil_image, mask=pil_image.split()[3])  # Use alpha channel as mask
-        return white_background
-    else:
-        return pil_image.convert("RGB")
-
-
-def fetch_image(ele: dict[str, str | Image.Image]) -> Image.Image:
-    image = ele["image"] if "image" in ele else ele["image_url"]
-    image_obj = None
-    if isinstance(image, Image.Image):
-        image_obj = image
-    elif image.startswith("http://") or image.startswith("https://"):
-        response = requests.get(image, stream=True, timeout=10)
-        image_obj = Image.open(BytesIO(response.content))
-    elif image.startswith("file://"):
-        image_obj = Image.open(image[7:])
-    elif image.startswith("data:image"):
-        if "base64," in image:
-            _, base64_data = image.split("base64,", 1)
-            data = base64.b64decode(base64_data)
-            image_obj = Image.open(BytesIO(data))
-    else:
-        image_obj = Image.open(image)
-    if image_obj is None:
-        raise ValueError(
-            f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}"
-        )
-    image = to_rgb(image_obj)
-    if "scale_factor" in ele:
-        scale_factor = ele["scale_factor"]
-        image = image.resize((image.width * scale_factor, image.height * scale_factor), Image.BILINEAR)
-    return image
-
-
-class Eagle25VLProcessorKwargs(ProcessingKwargs, total=False):
-    # see processing_utils.ProcessingKwargs documentation for usage.
-    _defaults = {
-        "text_kwargs": {
-            "padding": False,
-        },
-        "images_kwargs": {},
-        "videos_kwargs": {"max_dynamic_tiles": 1},
-    }
-
-
-class Eagle25VLProcessor(ProcessorMixin):
-    r"""
-    Constructs a Eagle25VL processor which wraps a Eagle25VL video processor, Eagle25VL image processor and a Eagle25VL tokenizer into a single processor.
-
-    [`Eagle25VLProcessor`] offers all the functionalities of [`Eagle25VLVideoProcessor`], [`Eagle25VLImageProcessor`] and [`Eagle25VLTokenizer`]. See the
-    [`~Eagle25VLVideoProcessor.__call__`], [`~Eagle25VLProcessor.__call__`] and [`~Eagle25VLProcessor.decode`] for more information.
-
-    Args:
-        image_processor ([`LlavaOnevisionImageProcessor`], *optional*):
-            The image processor is a required input.
-        tokenizer ([`LlamaTokenizerFast`], *optional*):
-            The tokenizer is a required input.
-        num_image_tokens (`int`, *optional*):
-            Number of image tokens for one imagethat will be returned by vision tower.
-        vision_feature_select_strategy (`str`, *optional*):
-            The feature selection strategy used to select the vision feature from the vision backbone.
-            Should be same as in model's config
-        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
-            in a chat into a tokenizable string.
-        image_token (`str`, *optional*, defaults to `"<image>"`):
-            Special token used to denote image location.
-        video_token (`str`, *optional*, defaults to `"<video>"`):
-            Special token used to denote video location.
-    """
-
-    attributes = ["image_processor", "tokenizer"]
-    valid_kwargs = [
-        "chat_template",
-        "num_image_tokens",
-        "vision_feature_select_strategy",
-        "image_token",
-        "video_token",
-        "images_kwargs",
-        "videos_kwargs",
-        "text_kwargs",
-    ]
-    image_processor_class = "AutoImageProcessor"
-    tokenizer_class = "AutoTokenizer"
-
-    def __init__(
-        self,
-        image_processor=None,
-        tokenizer=None,
-        vision_feature_select_strategy=None,
-        chat_template=None,
-        image_token="<IMG_CONTEXT>",  # nosec: B107
-        video_token="<IMG_CONTEXT>",  # nosec: B107
-        tokens_per_tile=256,
-        image_placeholder="image",
-        video_placeholder="video",
-        image_start_token="<img>",
-        image_end_token="</img>",
-        **kwargs,
-    ):
-        self.vision_feature_select_strategy = vision_feature_select_strategy
-        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
-        self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
-        self.image_token_id = (
-            tokenizer.image_token_id
-            if getattr(tokenizer, "image_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.image_token)
-        )
-        self.video_token_id = (
-            tokenizer.video_token_id
-            if getattr(tokenizer, "video_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.video_token)
-        )
-        self.image_placeholder = image_placeholder
-        self.video_placeholder = video_placeholder
-        self.tokens_per_tile = tokens_per_tile
-        self.image_start_token = image_start_token
-        self.image_end_token = image_end_token
-        if "auto_map" in kwargs:
-            self.auto_map = kwargs["auto_map"]
-        super().__init__(image_processor, tokenizer, chat_template=chat_template)
-
-    def replace_media_placeholder(
-        self, text, image_list, video_list, timestamps_list, fps_list, **output_kwargs
-    ):
-        num_of_images_in_this_sample = 0
-        num_of_videos_in_this_sample = 0
-        # Regular expression pattern to match formats like <image-1> or <video-2>
-        pattern = re.compile(rf"<({self.image_placeholder}|{self.video_placeholder})-(\d+)>")
-        unified_frame_list = []
-
-        # image_min_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        # )
-        # image_max_dynamic_tiles = output_kwargs["images_kwargs"].get(
-        #     "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        # )
-        # image_use_thumbnail = output_kwargs["images_kwargs"].get(
-        #     "use_thumbnail", self.image_processor.use_thumbnail
-        # )
-        video_min_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "min_dynamic_tiles", self.image_processor.min_dynamic_tiles
-        )
-        video_max_dynamic_tiles = output_kwargs["videos_kwargs"].get(
-            "max_dynamic_tiles", self.image_processor.max_dynamic_tiles
-        )
-        video_use_thumbnail = output_kwargs["videos_kwargs"].get(
-            "use_thumbnail", self.image_processor.use_thumbnail
-        )
-
-        tile_size = self.image_processor.size.get("height", 448)
-
-        # Function to replace tags in a single text
-        def replace_in_text(text):
-            # repl callback function for each match replacement operation
-            def repl(match):
-                nonlocal unified_frame_list
-                nonlocal num_of_images_in_this_sample
-                nonlocal num_of_videos_in_this_sample
-                media_type = match.group(1)  # 'image' or 'video'
-                idx_in_list = int(match.group(2)) - 1  # Convert to list index (0-based)
-                # Select the corresponding path based on media type
-                idx_mapper = {
-                    0: "first",
-                    1: "second",
-                    2: "third",
-                    3: "fourth",
-                    4: "fifth",
-                    5: "sixth",
-                    6: "seventh",
-                    7: "eighth",
-                    8: "ninth",
-                    9: "tenth",
-                }
-                if media_type == "image":
-                    image_inputs = self.image_processor(
-                        images=[image_list[idx_in_list]],
-                        videos=None,
-                        **output_kwargs["images_kwargs"],
-                    )
-                    if isinstance(image_inputs["pixel_values"], list):
-                        _pv = image_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        image_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = image_inputs["pixel_values"].shape[0]
-                    special_placeholder = f"<image {idx_in_list + 1}>{self.image_start_token}{self.image_token * num_all_tiles * self.tokens_per_tile}{self.image_end_token}"
-                    unified_frame_list.append(image_inputs)
-                    num_of_images_in_this_sample += 1
-
-                elif media_type == "video":
-                    video_inputs = self.image_processor(
-                        images=None,
-                        videos=[video_list[idx_in_list]],
-                        **output_kwargs["videos_kwargs"],
-                    )
-                    if isinstance(video_inputs["pixel_values"], list):
-                        _pv = video_inputs["pixel_values"]
-                        if _pv and isinstance(_pv[0], list):
-                            _pv = [t for sub in _pv for t in sub]
-                        video_inputs["pixel_values"] = torch.stack(
-                            [t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in _pv]
-                        )
-                    num_all_tiles = video_inputs["pixel_values"].shape[0]
-                    image_sizes = video_inputs["image_sizes"]
-                    if timestamps_list is not None and -1 not in timestamps_list:
-                        frame_timestamps = timestamps_list[idx_in_list]
-                    else:
-                        frame_timestamps = None
-                    sampled_fps = fps_list[idx_in_list] if fps_list is not None else None
-
-                    num_of_tiles_each_frame = [
-                        self.get_number_tiles_based_on_image_size(
-                            image_size,
-                            video_min_dynamic_tiles,
-                            video_max_dynamic_tiles,
-                            video_use_thumbnail,
-                            tile_size,
-                        )
-                        for image_size in image_sizes
-                    ]
-                    assert sum(num_of_tiles_each_frame) == num_all_tiles, (
-                        f"The number of tiles in each frame is not equal to the total number of tiles: {sum(num_of_tiles_each_frame)} != {num_all_tiles}"
-                    )
-
-                    if frame_timestamps is not None:
-                        assert len(frame_timestamps) == len(num_of_tiles_each_frame), (
-                            f"The number of timestamps is not equal to the number of frames: {len(frame_timestamps)} != {len(num_of_tiles_each_frame)}"
-                        )
-                        special_placeholder = [
-                            f"Frame {i + 1} sample at {frame_timestamps[i]:.2f}s: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-                    else:
-                        special_placeholder = [
-                            f"Frame {i + 1}: {self.image_start_token}{self.image_token * num_of_tiles * self.tokens_per_tile}{self.image_end_token}"
-                            for i, num_of_tiles in enumerate(num_of_tiles_each_frame)
-                        ]
-
-                    if sampled_fps is not None:
-                        special_placeholder = (
-                            f"The {idx_mapper[idx_in_list]} video sampled with {sampled_fps:.2f} fps: "
-                            + "".join(special_placeholder)
-                        )
-                    else:
-                        special_placeholder = f"The {idx_mapper[idx_in_list]} video: " + "".join(
-                            special_placeholder
-                        )
-                    unified_frame_list.append(video_inputs)
-                    num_of_videos_in_this_sample += 1
-                else:
-                    raise ValueError(f"Unknown media type: {media_type}")
-                return special_placeholder
-
-            return pattern.sub(repl, text)
-
-        text = replace_in_text(text)
-        if len(unified_frame_list) > 0:
-
-            def _to_tensor(v):
-                if isinstance(v, torch.Tensor):
-                    return v
-                if isinstance(v, list):
-                    if v and isinstance(v[0], list):
-                        v = [t for sub in v for t in sub]
-                    return torch.stack([t if isinstance(t, torch.Tensor) else torch.as_tensor(t) for t in v])
-                return torch.as_tensor(v)
-
-            pixel_values = torch.cat([_to_tensor(frame["pixel_values"]) for frame in unified_frame_list])
-            image_sizes = torch.cat([_to_tensor(frame["image_sizes"]) for frame in unified_frame_list])
-        else:
-            pixel_values = None
-            image_sizes = None
-        return (
-            text,
-            pixel_values,
-            image_sizes,
-            num_of_images_in_this_sample,
-            num_of_videos_in_this_sample,
-        )
-
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        audio=None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Eagle25VLProcessorKwargs],
-    ) -> BatchFeature:
-        """
-        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
-        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
-        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
-        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
-        of the above two methods for more information.
-
-        Args:
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. Both channels-first and channels-last formats are supported.
-            text (`str`, `List[str]`, `List[List[str]]`):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
-            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
-
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of a video input to be fed to a model. Returned when `videos` is not `None`.
-            - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`.
-        """
-
-        output_kwargs = self._merge_kwargs(
-            Eagle25VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if isinstance(text, str):
-            text_list = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-        elif isinstance(text, list) and isinstance(text[0], str):
-            text_list = text
-
-        if images is None:
-            images = []
-        if videos is None:
-            videos = []
-
-        pixel_values_list = []
-        image_sizes_list = []
-        new_sample_list = []
-        image_start_idx = 0
-        video_start_idx = 0
-        timestamps_batch = output_kwargs["videos_kwargs"].pop("timestamps", None)
-        fps_batch = output_kwargs["videos_kwargs"].pop("fps", None)
-        for sample in text_list:
-            timestamps_list = timestamps_batch[video_start_idx:] if timestamps_batch is not None else None
-            fps_list = fps_batch[video_start_idx:] if fps_batch is not None else None
-            (
-                sample,
-                pixel_values,
-                image_sizes,
-                num_of_images_in_this_sample,
-                num_of_videos_in_this_sample,
-            ) = self.replace_media_placeholder(
-                sample,
-                images[image_start_idx:],
-                videos[video_start_idx:],
-                timestamps_list,
-                fps_list,
-                **output_kwargs,
-            )
-            new_sample_list.append(sample)
-            if pixel_values is not None:
-                pixel_values_list.append(pixel_values)
-                image_sizes_list.append(image_sizes)
-            image_start_idx += num_of_images_in_this_sample
-            video_start_idx += num_of_videos_in_this_sample
-
-        if len(pixel_values_list) > 0:
-            image_inputs = {
-                "pixel_values": torch.cat(pixel_values_list),
-                "image_sizes": torch.cat(image_sizes_list),
-            }
-        else:
-            image_inputs = {}
-        video_inputs = {}
-        text_inputs = self.tokenizer(new_sample_list, **output_kwargs["text_kwargs"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs})
-
-    def get_number_tiles_based_on_image_size(
-        self, image_size: tuple, min_num: int, max_num: int, use_thumbnail: bool, tile_size: int
-    ) -> int:
-        """
-        Get the number of tiles based on the image size.
-        """
-        orig_height, orig_width = image_size
-        aspect_ratio = orig_width / orig_height
-        # calculate the existing image aspect ratio
-        target_ratios = {
-            (i, j)
-            for n in range(min_num, max_num + 1)
-            for i in range(1, n + 1)
-            for j in range(1, n + 1)
-            if i * j <= max_num and i * j >= min_num
-        }
-        target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
-
-        # find the closest aspect ratio to the target
-        target_aspect_ratio = self.image_processor.find_closest_aspect_ratio(
-            aspect_ratio, target_ratios, orig_width, orig_height, tile_size
-        )
-        tiles_num = target_aspect_ratio[0] * target_aspect_ratio[1]
-        if use_thumbnail and tiles_num > 1:
-            tiles_num += 1
-        return tiles_num
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
-    def batch_decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
-        refer to the docstring of this method for more information.
-        """
-        return self.tokenizer.batch_decode(*args, **kwargs)
-
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
-    def decode(self, *args, **kwargs):
-        """
-        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
-        the docstring of this method for more information.
-        """
-        return self.tokenizer.decode(*args, **kwargs)
-
-    @property
-    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
-
-    # override to save video-config in a separate config file
-    def save_pretrained(self, save_directory, **kwargs):
-        if os.path.isfile(save_directory):
-            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
-        os.makedirs(save_directory, exist_ok=True)
-
-        outputs = super().save_pretrained(save_directory, **kwargs)
-        return outputs
-
-    # override to load video-config from a separate config file
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
-        processor = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        # if return_unused_kwargs a tuple is returned where the second element is 'unused_kwargs'
-        if isinstance(processor, tuple):
-            processor = processor[0]
-        return processor
-
-    # Copy from https://github.com/QwenLM/Qwen2.5-VL/blob/main/qwen-vl-utils/src/qwen_vl_utils/vision_process.py
-    def process_vision_info(
-        self,
-        conversations: list[dict] | list[list[dict]],
-        return_video_kwargs: bool = False,
-    ) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None, dict | None]:
-        vision_infos = self.extract_vision_info(conversations)
-        ## Read images or videos
-        image_inputs = []
-        video_inputs = []
-        video_sample_fps_list = []
-        video_timestamps_list = []
-        for vision_info in vision_infos:
-            if "image" in vision_info or "image_url" in vision_info:
-                image_inputs.append(fetch_image(vision_info))
-            else:
-                raise ValueError("image, image_url or video should in content.")
-        if len(image_inputs) == 0:
-            image_inputs = None
-        if len(video_inputs) == 0:
-            video_inputs = None
-        if return_video_kwargs:
-            return (
-                image_inputs,
-                video_inputs,
-                {"fps": video_sample_fps_list, "timestamps": video_timestamps_list},
-            )
-        return image_inputs, video_inputs
-
-    def extract_vision_info(self, conversations: list[dict] | list[list[dict]]) -> list[dict]:
-        vision_infos = []
-        if isinstance(conversations[0], dict):
-            conversations = [conversations]
-        for conversation in conversations:
-            for message in conversation:
-                if isinstance(message["content"], list):
-                    for ele in message["content"]:
-                        if (
-                            "image" in ele
-                            or "image_url" in ele
-                            or "video" in ele
-                            or ele["type"] in ("image", "image_url", "video")
-                        ):
-                            vision_infos.append(ele)
-        return vision_infos
-
-
-__all__ = ["Eagle25VLProcessor"]
@@ -1,374 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import numpy as np
-import torch
-import torch.nn as nn
-from huggingface_hub import snapshot_download
-from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
-
-from lerobot.utils.import_utils import _transformers_available
-
-# Conditional import for type checking and lazy loading
-if TYPE_CHECKING or _transformers_available:
-    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
-    from transformers.feature_extraction_utils import BatchFeature
-else:
-    AutoConfig = None
-    AutoModel = None
-    PretrainedConfig = object
-    PreTrainedModel = object
-    BatchFeature = None
-
-try:
-    import tree
-except ImportError:
-    tree = None
-
-from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME
-
-from .action_head.flow_matching_action_head import (
-    FlowmatchingActionHead,
-    FlowmatchingActionHeadConfig,
-)
-from .utils import ensure_eagle_cache_ready
-
-DEFAULT_VENDOR_EAGLE_PATH = str((Path(__file__).resolve().parent / "eagle2_hg_model").resolve())
-DEFAULT_TOKENIZER_ASSETS_REPO = "lerobot/eagle2hg-processor-groot-n1p5"
-
-
-class EagleBackbone(nn.Module):
-    def __init__(
-        self,
-        tune_llm: bool = False,
-        tune_visual: bool = False,
-        select_layer: int = -1,
-        reproject_vision: bool = False,
-        use_flash_attention: bool = False,
-        load_bf16: bool = False,
-        eagle_path: str = DEFAULT_VENDOR_EAGLE_PATH,
-        tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS_REPO,
-        project_to_dim: int = 1536,
-    ):
-        """
-        Args:
-            tune_llm: whether to tune the LLM model (default: True)
-            tune_visual: whether to tune the visual model (default: False)
-        """
-        super().__init__()
-        assert not reproject_vision, "Reproject vision is not implemented here, set to False"
-
-        # Prefer loading Eagle model config from the cache directory where vendor files were copied.
-        vendor_dir = DEFAULT_VENDOR_EAGLE_PATH
-        cache_dir = HF_LEROBOT_HOME / tokenizer_assets_repo
-        try:
-            ensure_eagle_cache_ready(vendor_dir, cache_dir, tokenizer_assets_repo)
-        except Exception as exc:  # nosec: B110
-            print(f"[GROOT] Warning: failed to prepare Eagle cache for backbone: {exc}")
-
-        config = AutoConfig.from_pretrained(str(cache_dir), trust_remote_code=True)
-        self.eagle_model = AutoModel.from_config(config, trust_remote_code=True)
-
-        if project_to_dim is not None:
-            self.eagle_linear = torch.nn.Linear(2048, project_to_dim)
-        else:
-            self.eagle_linear = torch.nn.Identity()
-
-        # needed since we don't use these layers. Also saves compute
-        while len(self.eagle_model.language_model.model.layers) > select_layer:
-            self.eagle_model.language_model.model.layers.pop(-1)
-
-        self.select_layer = select_layer
-        self.set_trainable_parameters(tune_llm, tune_visual)
-
-    def set_trainable_parameters(self, tune_llm: bool, tune_visual: bool):
-        self.tune_llm = tune_llm
-        self.tune_visual = tune_visual
-        for p in self.parameters():
-            p.requires_grad = True
-        if not tune_llm:
-            self.eagle_model.language_model.requires_grad_(False)
-        if not tune_visual:
-            self.eagle_model.vision_model.requires_grad_(False)
-            self.eagle_model.mlp1.requires_grad_(False)
-        print(f"Tune backbone llm: {self.tune_llm}")
-        print(f"Tune backbone visual: {self.tune_visual}")
-        # Check if any parameters are still trainable. If not, print a warning.
-        if not tune_llm and not tune_visual:
-            for name, p in self.named_parameters():
-                if p.requires_grad:
-                    print(f"Backbone trainable parameter: {name}")
-        if not any(p.requires_grad for p in self.parameters()):
-            print("Warning: No backbone trainable parameters found.")
-
-    def set_frozen_modules_to_eval_mode(self):
-        """
-        Huggingface will call model.train() at each training_step. To ensure
-        the expected behaviors for modules like dropout, batchnorm, etc., we
-        need to call model.eval() for the frozen modules.
-        """
-        if self.training:
-            if self.eagle_model.language_model and not self.tune_llm:
-                self.eagle_model.language_model.eval()
-            if self.eagle_model.vision_model and not self.tune_visual:
-                self.eagle_model.vision_model.eval()
-
-    def prepare_input(self, batch: dict) -> BatchFeature:
-        return BatchFeature(data=batch)
-
-    def forward_eagle(self, vl_input: BatchFeature) -> BatchFeature:
-        eagle_prefix = "eagle_"
-        eagle_input = {
-            k.removeprefix(eagle_prefix): v for k, v in vl_input.items() if k.startswith(eagle_prefix)
-        }
-        del eagle_input["image_sizes"]
-
-        eagle_output = self.eagle_model(**eagle_input, output_hidden_states=True, return_dict=True)
-        eagle_features = eagle_output.hidden_states[self.select_layer]
-
-        eagle_features = self.eagle_linear(eagle_features)
-        return eagle_features, eagle_input["attention_mask"]
-
-    def forward(self, vl_input: BatchFeature) -> BatchFeature:
-        self.set_frozen_modules_to_eval_mode()
-
-        eagle_embeds, eagle_mask = self.forward_eagle(vl_input)
-
-        # YL (TODO HACK): to resolve DDP issue when tune_visual=True
-        # Ensure all trainable parameters in vision_model are used in the forward pass for DDP compatibility
-        if self.training and self.tune_visual:
-            dummy_term = torch.tensor(
-                0.0, device=eagle_embeds.device, dtype=eagle_embeds.dtype, requires_grad=True
-            )
-            for param in self.eagle_model.vision_model.parameters():
-                if param.requires_grad:
-                    dummy_term = dummy_term + 0.0 * param.sum()
-            eagle_embeds = eagle_embeds + dummy_term
-
-        return BatchFeature(
-            data={"backbone_features": eagle_embeds, "backbone_attention_mask": eagle_mask}
-        )  # [B, T2, hidden_size]
-
-
-BACKBONE_FEATURE_KEY = "backbone_features"
-ACTION_KEY = "action_pred"
-LOSS_KEY = "loss"
-ERROR_MSG = "Error: unexpected input/output"
-N_COLOR_CHANNELS = 3
-
-
-# config
-class GR00TN15Config(PretrainedConfig):
-    model_type = "gr00t_n1_5"
-
-    backbone_cfg: dict
-    action_head_cfg: dict
-    action_horizon: int
-    action_dim: int
-    compute_dtype: str = "float32"
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-
-# real model
-class GR00TN15(PreTrainedModel):
-    supports_gradient_checkpointing = True
-    config_class = GR00TN15Config
-    """
-    we expect the backbone output to have a key 'backbone_features' with shape (batch_size, n, hidden_size)
-    here n is variable and can be e.g. time, 1 or user specified
-    we expect the action head output to have a key 'action_pred' with shape (batch_size, time, action_dim) during inference time
-    we expect these to have type BatchFeature, and they can of course have many other user specified keys too
-    """
-
-    def __init__(
-        self,
-        config: GR00TN15Config,
-        local_model_path: str,
-    ):
-        assert isinstance(config.backbone_cfg, dict)
-        assert isinstance(config.action_head_cfg, dict)
-
-        super().__init__(config)
-        self.local_model_path = local_model_path
-
-        self.backbone = EagleBackbone(**config.backbone_cfg)
-        action_head_cfg = FlowmatchingActionHeadConfig(**config.action_head_cfg)
-        self.action_head = FlowmatchingActionHead(action_head_cfg)
-
-        self.action_horizon = config.action_horizon
-        self.action_dim = config.action_dim
-        self.compute_dtype = config.compute_dtype
-        self.post_init()
-
-    def validate_inputs(self, inputs):
-        # NOTE -- this should be handled internally by the model
-        # however, doing that will likely be breaking changes -- so we'll need to do it after the deadline
-
-        detected_error = False
-        error_msg = ERROR_MSG
-        if ACTION in inputs:
-            action = inputs[ACTION]
-            # In inference, action may be omitted or None; validate only when it's a tensor.
-            if action is None:
-                pass  # allow None during inference
-            elif isinstance(action, torch.Tensor):
-                shape_ok = (
-                    len(action.shape) == 3
-                    and action.shape[1] == self.action_horizon
-                    and action.shape[2] == self.action_dim
-                )
-                if not shape_ok:
-                    error_msg += f"\n{action.shape=}"
-                    detected_error = True
-            else:
-                # Unexpected non-tensor type provided for action
-                error_msg += f"\nInvalid type for action: {type(action)}"
-                detected_error = True
-
-        if "video" in inputs:
-            video = inputs["video"]
-            type_ok = isinstance(video, np.ndarray)
-            dtype_ok = video.dtype == np.uint8
-            shape_ok = len(video.shape) == 6 and video.shape[3] == N_COLOR_CHANNELS
-            if not type_ok:
-                error_msg += f"\n{type(video)=}"
-                detected_error = True
-            if not dtype_ok:
-                error_msg += f"\n{video.dtype=}"
-                detected_error = True
-            if not shape_ok:
-                error_msg += f"\n{video.shape=}"
-                detected_error = True
-
-        if detected_error:
-            raise ValueError(error_msg)
-
-    def validate_data(self, action_head_outputs, backbone_outputs, is_training):
-        fail_backbone = (
-            not isinstance(backbone_outputs, BatchFeature) or BACKBONE_FEATURE_KEY not in backbone_outputs
-        )
-
-        if fail_backbone:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(backbone_outputs, BatchFeature)=}"
-            error_msg += f"\n{BACKBONE_FEATURE_KEY in backbone_outputs=}"
-            error_msg += f"\n{backbone_outputs[BACKBONE_FEATURE_KEY].shape=}"
-            raise ValueError(error_msg)
-
-        fail_action_head = (not isinstance(action_head_outputs, BatchFeature)) or not (
-            (
-                LOSS_KEY in action_head_outputs and is_training
-            )  # there might not be an action prediction during training
-            or (
-                ACTION_KEY in action_head_outputs
-                and action_head_outputs[ACTION_KEY].shape[1] == self.action_horizon
-                and action_head_outputs[ACTION_KEY].shape[2] == self.action_dim
-            )
-        )
-
-        if fail_action_head:
-            error_msg = ERROR_MSG
-            error_msg += f"\n{isinstance(action_head_outputs, BatchFeature)=}"
-            error_msg += f"\n{LOSS_KEY in action_head_outputs=}"
-            error_msg += f"\n{action_head_outputs[ACTION_KEY].shape=}"
-            error_msg += f"\n{self.action_horizon=}"
-            error_msg += f"\n{self.action_dim=}"
-            raise ValueError(error_msg)
-
-    def forward(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=True)
-        return action_head_outputs
-
-    def get_action(
-        self,
-        inputs: dict,
-    ) -> BatchFeature:
-        backbone_inputs, action_inputs = self.prepare_input(inputs)
-        # Because the behavior of backbones remains the same for training and inference, we can use `forward` for backbones.
-        backbone_outputs = self.backbone(backbone_inputs)
-        action_head_outputs = self.action_head.get_action(backbone_outputs, action_inputs)
-        self.validate_data(action_head_outputs, backbone_outputs, is_training=False)
-        return action_head_outputs
-
-    def prepare_input(self, inputs) -> tuple[BatchFeature, BatchFeature]:
-        self.validate_inputs(inputs)
-        backbone_inputs = self.backbone.prepare_input(inputs)
-        action_inputs = self.action_head.prepare_input(inputs)
-
-        def to_device_with_maybe_dtype(x):
-            # Cast floating tensors to a memory-efficient compute dtype when requested.
-            # Rationale: Upcasting backbone activations to fp32 significantly increases VRAM.
-            # When compute_dtype is bfloat16, prefer bf16 for activations to match AMP behavior.
-            if not isinstance(x, torch.Tensor):
-                return x
-            if torch.is_floating_point(x):
-                if getattr(self, "compute_dtype", None) == "bfloat16":
-                    return x.to(self.device, dtype=torch.bfloat16)
-                # Fallback: preserve previous behavior if not using bf16 compute
-                return x.to(self.device, dtype=self.action_head.dtype)
-            # Non-floating tensors: move device only
-            return x.to(self.device)
-
-        backbone_inputs = tree.map_structure(to_device_with_maybe_dtype, backbone_inputs)
-        action_inputs = tree.map_structure(to_device_with_maybe_dtype, action_inputs)
-        return backbone_inputs, action_inputs
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
-        tune_visual = kwargs.pop("tune_visual", True)
-        tune_llm = kwargs.pop("tune_llm", False)
-        tune_projector = kwargs.pop("tune_projector", True)
-        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
-
-        print(f"Loading pretrained dual brain from {pretrained_model_name_or_path}")
-        print(f"Tune backbone vision tower: {tune_visual}")
-        print(f"Tune backbone LLM: {tune_llm}")
-        print(f"Tune action head projector: {tune_projector}")
-        print(f"Tune action head DiT: {tune_diffusion_model}")
-
-        # get the current model path being downloaded
-        try:
-            # NOTE(YL) This downloads the model to the local cache and returns the local path to the model
-            # saved in ~/.cache/huggingface/hub/
-            local_model_path = snapshot_download(pretrained_model_name_or_path, repo_type="model")
-            # HFValidationError, RepositoryNotFoundError
-        except (HFValidationError, RepositoryNotFoundError):
-            print(
-                f"Model not found or avail in the huggingface hub. Loading from local path: {pretrained_model_name_or_path}"
-            )
-            local_model_path = pretrained_model_name_or_path
-
-        pretrained_model = super().from_pretrained(
-            local_model_path, local_model_path=local_model_path, **kwargs
-        )
-
-        pretrained_model.backbone.set_trainable_parameters(tune_visual=tune_visual, tune_llm=tune_llm)
-        pretrained_model.action_head.set_trainable_parameters(
-            tune_projector=tune_projector, tune_diffusion_model=tune_diffusion_model
-        )
-        return pretrained_model
@@ -0,0 +1,971 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import importlib
+import json
+import logging
+from contextlib import suppress
+from copy import deepcopy
+from typing import TYPE_CHECKING, Any
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import HFValidationError, RepositoryNotFoundError
+from torch import nn
+from torch.distributions import Beta
+
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+from .action_head.cross_attention_dit import AlternateVLDiT, DiT, SelfAttentionTransformer
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoConfig, AutoModel, PretrainedConfig, PreTrainedModel
+    from transformers.feature_extraction_utils import BatchFeature
+else:
+    AutoConfig = None
+    AutoModel = None
+    PretrainedConfig = object
+    PreTrainedModel = object
+    BatchFeature = None
+
+try:
+    import tree
+except ImportError:
+    tree = None
+
+try:
+    from transformers import Qwen3VLConfig, Qwen3VLForConditionalGeneration
+except ImportError:
+    Qwen3VLConfig = None
+    Qwen3VLForConditionalGeneration = None
+
+logger = logging.getLogger(__name__)
+
+
+def _copy_default(value: Any) -> Any:
+    return deepcopy(value)
+
+
+GR00T_N1_7_DEFAULTS: dict[str, Any] = {
+    "model_dtype": "bfloat16",
+    "dtype": "bfloat16",
+    "model_name": "nvidia/Cosmos-Reason2-2B",
+    "backbone_model_type": "qwen",
+    "model_revision": None,
+    "tune_top_llm_layers": 0,
+    "backbone_embedding_dim": 2048,
+    "tune_llm": False,
+    "tune_visual": False,
+    "select_layer": 16,  # N1.7-3B checkpoint value; real checkpoint loads override this from config.json
+    "reproject_vision": False,
+    "use_flash_attention": True,
+    "load_bf16": False,
+    "backbone_trainable_params_fp32": True,
+    "image_crop_size": (230, 230),
+    "image_target_size": (256, 256),
+    "shortest_image_edge": None,
+    "crop_fraction": None,
+    "random_rotation_angle": None,
+    "color_jitter_params": None,
+    "use_albumentations_transforms": True,
+    "extra_augmentation_config": None,
+    "formalize_language": True,
+    "apply_sincos_state_encoding": False,
+    "use_percentiles": True,
+    "use_relative_action": False,
+    "max_state_dim": 132,
+    "max_action_dim": 132,
+    "action_horizon": 40,
+    "hidden_size": 1024,
+    "input_embedding_dim": 1536,
+    "state_history_length": 1,
+    "add_pos_embed": True,
+    "attn_dropout": 0.2,
+    "use_vlln": True,
+    "max_seq_len": 1024,
+    "use_alternate_vl_dit": True,
+    "attend_text_every_n_blocks": 2,
+    "diffusion_model_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 32,
+        "num_attention_heads": 32,
+        "attention_head_dim": 48,
+        "norm_type": "ada_norm",
+        "dropout": 0.2,
+        "final_dropout": True,
+        "output_dim": 1024,
+        "interleave_self_attention": True,
+    },
+    "vl_self_attention_cfg": {
+        "positional_embeddings": None,
+        "num_layers": 4,
+        "num_attention_heads": 32,
+        "attention_head_dim": 64,
+        "dropout": 0.2,
+        "final_dropout": True,
+    },
+    "num_inference_timesteps": 4,
+    "noise_beta_alpha": 1.5,
+    "noise_beta_beta": 1.0,
+    "noise_s": 0.999,
+    "num_timestep_buckets": 1000,
+    "tune_projector": True,
+    "tune_diffusion_model": True,
+    "tune_vlln": True,
+    "state_dropout_prob": 0.2,
+    "exclude_state": False,
+    "use_mean_std": False,
+    "max_num_embodiments": 32,
+    "rtc_ramp_rate": 6.0,
+}
+
+
+class GR00TN17Config(PretrainedConfig):
+    """Configuration for NVIDIA GR00T N1.7.
+
+    N1.7 uses the Cosmos-Reason2-2B / Qwen3-VL backbone and a multi-embodiment
+    flow-matching action head. This mirrors the public N1.7 checkpoint config
+    while keeping it local to LeRobot and independent from the external
+    Isaac-GR00T ``gr00t`` Python package.
+    """
+
+    model_type = "Gr00tN1d7"
+
+    _defaults = GR00T_N1_7_DEFAULTS
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in GR00T_N1_7_DEFAULTS.items():
+            setattr(self, key, _copy_default(kwargs.pop(key, value)))
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def to_filtered_dict(self, exclude_augment: bool = True) -> dict[str, Any]:
+        cfg = self.to_dict()
+        if not exclude_augment:
+            return cfg
+        exclude_keys = {
+            "random_rotation_angle",
+            "color_jitter_params",
+            "use_albumentations_transforms",
+            "formalize_language",
+            "image_crop_size",
+            "image_target_size",
+            "shortest_image_edge",
+            "crop_fraction",
+        }
+        return {k: v for k, v in cfg.items() if k not in exclude_keys}
+
+    def to_filtered_json(self, exclude_augment: bool = True, **kwargs) -> str:
+        return json.dumps(self.to_filtered_dict(exclude_augment), indent=2, default=str, **kwargs)
+
+
+class CategorySpecificLinear(nn.Module):
+    """Linear layer with category-specific weights for multi-embodiment support."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int):
+        super().__init__()
+        self.num_categories = num_categories
+        self.W = nn.Parameter(0.02 * torch.randn(num_categories, input_dim, hidden_dim))
+        self.b = nn.Parameter(torch.zeros(num_categories, hidden_dim))
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        selected_w = self.W[cat_ids]
+        selected_b = self.b[cat_ids]
+        return torch.bmm(x, selected_w) + selected_b.unsqueeze(1)
+
+
+class CategorySpecificMLP(nn.Module):
+    """Two-layer MLP with category-specific weights."""
+
+    def __init__(self, num_categories: int, input_dim: int, hidden_dim: int, output_dim: int):
+        super().__init__()
+        self.layer1 = CategorySpecificLinear(num_categories, input_dim, hidden_dim)
+        self.layer2 = CategorySpecificLinear(num_categories, hidden_dim, output_dim)
+
+    def forward(self, x: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        hidden = F.relu(self.layer1(x, cat_ids))
+        return self.layer2(hidden, cat_ids)
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    """Sinusoidal encoding of shape ``(B, T, D)`` for timestep tensors ``(B, T)``.
+
+    The frequency scalar is intentionally created on CPU and then broadcast with
+    the device-local arange result. That mirrors Isaac-GR00T's N1.7 timestep
+    embedding and avoids tiny dtype/device construction differences in parity
+    tests.
+    """
+
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        timesteps = timesteps.float()
+        half_dim = self.embedding_dim // 2
+        exponent = -torch.arange(half_dim, dtype=torch.float, device=timesteps.device) * (
+            torch.log(torch.tensor(10000.0)) / half_dim
+        )
+        freqs = timesteps.unsqueeze(-1) * exponent.exp()
+        return torch.cat([torch.sin(freqs), torch.cos(freqs)], dim=-1)
+
+
+def swish(x: torch.Tensor) -> torch.Tensor:
+    return x * torch.sigmoid(x)
+
+
+class MultiEmbodimentActionEncoder(nn.Module):
+    """Action encoder with category-specific projections and sinusoidal time encoding."""
+
+    def __init__(self, action_dim: int, hidden_size: int, num_embodiments: int):
+        super().__init__()
+        self.W1 = CategorySpecificLinear(num_embodiments, action_dim, hidden_size)
+        self.W2 = CategorySpecificLinear(num_embodiments, 2 * hidden_size, hidden_size)
+        self.W3 = CategorySpecificLinear(num_embodiments, hidden_size, hidden_size)
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
+
+    def forward(self, actions: torch.Tensor, timesteps: torch.Tensor, cat_ids: torch.Tensor) -> torch.Tensor:
+        batch_size, horizon, _ = actions.shape
+        if timesteps.dim() != 1 or timesteps.shape[0] != batch_size:
+            raise ValueError("Expected `timesteps` to have shape (B,).")
+        timesteps = timesteps.unsqueeze(1).expand(-1, horizon)
+        action_emb = self.W1(actions, cat_ids)
+        time_emb = self.pos_encoding(timesteps).to(dtype=action_emb.dtype)
+        x = swish(self.W2(torch.cat([action_emb, time_emb], dim=-1), cat_ids))
+        return self.W3(x, cat_ids)
+
+
+class Qwen3Backbone(nn.Module):
+    """Cosmos-Reason2/Qwen3-VL backbone used by GR00T N1.7.
+
+    The public checkpoint stores the action head in the GR00T checkpoint but
+    uses a Hugging Face Qwen3-VL-compatible backbone interface. This wrapper
+    keeps the nested HF module layout compatible across transformer versions
+    and exposes the hidden states consumed by the action head.
+    """
+
+    def __init__(
+        self,
+        model_name: str = "nvidia/Cosmos-Reason2-2B",
+        tune_llm: bool = False,
+        tune_visual: bool = False,
+        select_layer: int = -1,
+        reproject_vision: bool = False,
+        use_flash_attention: bool = False,
+        load_bf16: bool = False,
+        tune_top_llm_layers: int = 0,
+        trainable_params_fp32: bool = False,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_pretrained_weights: bool = True,
+    ):
+        if Qwen3VLForConditionalGeneration is None:
+            raise ImportError(
+                "Qwen3VLForConditionalGeneration is required for GR00T N1.7. "
+                "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'` "
+                "or use a transformers version that provides Qwen3-VL support."
+            )
+
+        super().__init__()
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+
+        extra_kwargs: dict[str, Any] = {}
+        if use_flash_attention:
+            try:
+                import flash_attn  # noqa: F401
+
+                extra_kwargs["attn_implementation"] = "flash_attention_2"
+            except ImportError:
+                logger.warning("flash_attn is not installed. Falling back to SDPA attention.")
+                extra_kwargs["attn_implementation"] = "sdpa"
+        if load_bf16:
+            extra_kwargs["torch_dtype"] = torch.bfloat16
+
+        if load_pretrained_weights:
+            self.model = Qwen3VLForConditionalGeneration.from_pretrained(
+                model_name,
+                **extra_kwargs,
+                **transformers_loading_kwargs,
+            ).eval()
+        else:
+            self.model = self._from_backbone_config(
+                model_name=model_name,
+                model_kwargs=extra_kwargs,
+                config_kwargs=transformers_loading_kwargs,
+            ).eval()
+
+        while len(self.language_model.layers) > select_layer:
+            self.language_model.layers.pop(-1)
+
+        self.select_layer = select_layer
+        self.set_trainable_parameters(tune_llm, tune_visual, tune_top_llm_layers)
+        if load_bf16 and trainable_params_fp32:
+            for parameter in self.parameters():
+                if parameter.requires_grad:
+                    parameter.data = parameter.data.to(torch.float32)
+
+    def set_trainable_parameters(
+        self, tune_llm: bool, tune_visual: bool, tune_top_llm_layers: int = 0
+    ) -> None:
+        self.tune_llm = tune_llm
+        self.tune_visual = tune_visual
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_llm:
+            self.language_model.requires_grad_(False)
+        if not tune_visual:
+            self.visual.requires_grad_(False)
+        if tune_top_llm_layers > 0:
+            for layer in self.language_model.layers[-tune_top_llm_layers:]:
+                for parameter in layer.parameters():
+                    parameter.requires_grad = True
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if self.language_model and not self.tune_llm:
+                self.language_model.eval()
+            if self.visual and not self.tune_visual:
+                self.visual.eval()
+
+    @property
+    def language_model(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).language_model
+
+    @property
+    def visual(self) -> nn.Module:
+        return getattr(self.model, "model", self.model).visual
+
+    def _from_backbone_config(
+        self,
+        *,
+        model_name: str,
+        model_kwargs: dict[str, Any],
+        config_kwargs: dict[str, Any],
+    ) -> nn.Module:
+        if _is_cosmos_reason2_backbone(model_name):
+            backbone_config = _cosmos_reason2_qwen3_vl_config()
+        else:
+            if AutoConfig is None:
+                raise ImportError(
+                    "AutoConfig is required to initialize a GR00T N1.7 backbone from config. "
+                    "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'`."
+                )
+            backbone_config = AutoConfig.from_pretrained(model_name, **config_kwargs)
+        return Qwen3VLForConditionalGeneration._from_config(backbone_config, **model_kwargs)
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+    def _ensure_mm_token_type_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        if "mm_token_type_ids" in model_input:
+            return
+        if "image_grid_thw" not in model_input and "video_grid_thw" not in model_input:
+            return
+
+        input_ids = model_input.get("input_ids")
+        if input_ids is None:
+            return
+
+        mm_token_type_ids = torch.zeros(input_ids.shape, dtype=torch.int32, device=input_ids.device)
+        image_token_id = getattr(self.model.config, "image_token_id", None)
+        video_token_id = getattr(self.model.config, "video_token_id", None)
+        if image_token_id is not None:
+            mm_token_type_ids[input_ids == image_token_id] = 1
+        if video_token_id is not None:
+            mm_token_type_ids[input_ids == video_token_id] = 2
+
+        model_input["mm_token_type_ids"] = mm_token_type_ids
+
+    def _ensure_legacy_qwen3_position_ids(self, model_input: dict[str, torch.Tensor]) -> None:
+        """Restore the Qwen3-VL text position ids used by older Transformers releases.
+
+        Transformers 5.x computes 3-row multimodal RoPE ids for Qwen3-VL and then
+        drops text position ids before calling text-layer flash attention. GR00T
+        N1.7 was aligned against the older Transformers path, where a fourth text
+        position row is forwarded alongside the temporal/height/width rows. Adding
+        the row here preserves the newer multimodal position computation while
+        keeping flash attention on the legacy code path.
+        """
+
+        if "position_ids" in model_input:
+            return
+
+        qwen3_model = getattr(self.model, "model", self.model)
+        compute_3d_position_ids = getattr(qwen3_model, "compute_3d_position_ids", None)
+        if compute_3d_position_ids is None:
+            return
+
+        position_ids = compute_3d_position_ids(
+            input_ids=model_input.get("input_ids"),
+            image_grid_thw=model_input.get("image_grid_thw"),
+            video_grid_thw=model_input.get("video_grid_thw"),
+            inputs_embeds=None,
+            attention_mask=model_input.get("attention_mask"),
+            past_key_values=None,
+            mm_token_type_ids=model_input.get("mm_token_type_ids"),
+        )
+        if position_ids.ndim == 3 and position_ids.shape[0] == 3:
+            position_ids = torch.cat([position_ids[:1], position_ids], dim=0)
+
+        model_input["position_ids"] = position_ids
+
+    def _last_decoder_layer_output(self, model_input: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Return the pre-final-norm decoder output consumed by the N1.7 action head.
+
+        Older Transformers releases exposed this tensor as ``hidden_states[-1]``.
+        Newer releases expose the post-final-norm tensor there instead. Capturing
+        the last decoder layer output directly keeps the N1.7 action head input
+        stable across Transformers versions.
+        """
+
+        captured: dict[str, torch.Tensor] = {}
+
+        def capture_output(_module: nn.Module, _inputs: tuple[Any, ...], output: Any) -> None:
+            if isinstance(output, torch.Tensor):
+                captured["features"] = output
+            elif isinstance(output, (tuple, list)) and output:
+                captured["features"] = output[0]
+            elif hasattr(output, "last_hidden_state"):
+                captured["features"] = output.last_hidden_state
+
+        hook = self.language_model.layers[-1].register_forward_hook(capture_output)
+        try:
+            outputs = self.model(**model_input, output_hidden_states=True)
+        finally:
+            hook.remove()
+
+        return captured.get("features", outputs.hidden_states[-1])
+
+    def forward(self, vl_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        keys_to_use = ["input_ids", "attention_mask", "pixel_values", "image_grid_thw"]
+        optional_keys = ["mm_token_type_ids", "pixel_values_videos", "video_grid_thw"]
+        model_input = {key: vl_input[key] for key in keys_to_use}
+        model_input.update({key: vl_input[key] for key in optional_keys if key in vl_input})
+        self._ensure_mm_token_type_ids(model_input)
+        self._ensure_legacy_qwen3_position_ids(model_input)
+        features = self._last_decoder_layer_output(model_input)
+        image_mask = model_input["input_ids"] == self.model.config.image_token_id
+        attention_mask = model_input["attention_mask"] == 1
+        return BatchFeature(
+            data={
+                "backbone_features": features,
+                "backbone_attention_mask": attention_mask,
+                "image_mask": image_mask,
+            }
+        )
+
+
+class GR00TN17ActionHead(nn.Module):
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config: GR00TN17Config):
+        require_package("diffusers", extra="groot")
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.input_embedding_dim = config.input_embedding_dim
+
+        if config.use_alternate_vl_dit:
+            self.model = AlternateVLDiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+                attend_text_every_n_blocks=config.attend_text_every_n_blocks,
+            )
+        else:
+            self.model = DiT(
+                **config.diffusion_model_cfg,
+                cross_attention_dim=config.backbone_embedding_dim,
+            )
+
+        self.action_dim = config.max_action_dim
+        self.action_horizon = config.action_horizon
+        self.num_inference_timesteps = config.num_inference_timesteps
+        self.state_encoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=config.max_state_dim * config.state_history_length,
+            hidden_dim=self.hidden_size,
+            output_dim=self.input_embedding_dim,
+        )
+        self.action_encoder = MultiEmbodimentActionEncoder(
+            action_dim=self.action_dim,
+            hidden_size=self.input_embedding_dim,
+            num_embodiments=config.max_num_embodiments,
+        )
+        self.action_decoder = CategorySpecificMLP(
+            num_categories=config.max_num_embodiments,
+            input_dim=self.hidden_size,
+            hidden_dim=self.hidden_size,
+            output_dim=self.action_dim,
+        )
+        self.vlln = nn.LayerNorm(config.backbone_embedding_dim) if config.use_vlln else nn.Identity()
+        vl_self_attention_cfg = getattr(config, "vl_self_attention_cfg", None)
+        if vl_self_attention_cfg and vl_self_attention_cfg.get("num_layers", 0) > 0:
+            self.vl_self_attention = SelfAttentionTransformer(**vl_self_attention_cfg)
+        else:
+            self.vl_self_attention = nn.Identity()
+        if config.add_pos_embed:
+            self.position_embedding = nn.Embedding(config.max_seq_len, self.input_embedding_dim)
+            nn.init.normal_(self.position_embedding.weight, mean=0.0, std=0.02)
+        self.state_dropout_prob = config.state_dropout_prob
+        self._noise_beta_alpha = config.noise_beta_alpha
+        self._noise_beta_beta = config.noise_beta_beta
+        self._beta_dist = None
+        self.num_timestep_buckets = config.num_timestep_buckets
+        self.set_trainable_parameters(config.tune_projector, config.tune_diffusion_model, config.tune_vlln)
+
+    def set_trainable_parameters(
+        self, tune_projector: bool, tune_diffusion_model: bool, tune_vlln: bool
+    ) -> None:
+        self.tune_projector = tune_projector
+        self.tune_diffusion_model = tune_diffusion_model
+        self.tune_vlln = tune_vlln
+        for parameter in self.parameters():
+            parameter.requires_grad = True
+        if not tune_projector:
+            self.state_encoder.requires_grad_(False)
+            self.action_encoder.requires_grad_(False)
+            self.action_decoder.requires_grad_(False)
+            if self.config.add_pos_embed:
+                self.position_embedding.requires_grad_(False)
+        if not tune_diffusion_model:
+            self.model.requires_grad_(False)
+        if not tune_vlln:
+            self.vlln.requires_grad_(False)
+            self.vl_self_attention.requires_grad_(False)
+
+    def set_frozen_modules_to_eval_mode(self) -> None:
+        if self.training:
+            if not self.tune_projector:
+                self.state_encoder.eval()
+                self.action_encoder.eval()
+                self.action_decoder.eval()
+                if self.config.add_pos_embed:
+                    self.position_embedding.eval()
+            if not self.tune_diffusion_model:
+                self.model.eval()
+            if not self.tune_vlln:
+                self.vlln.eval()
+                self.vl_self_attention.eval()
+
+    def sample_time(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        if self._beta_dist is None:
+            beta_alpha = torch.tensor(self._noise_beta_alpha, device="cpu", dtype=torch.float32)
+            beta_beta = torch.tensor(self._noise_beta_beta, device="cpu", dtype=torch.float32)
+            self._beta_dist = Beta(beta_alpha, beta_beta, validate_args=False)
+        sample = self._beta_dist.sample([batch_size]).to(device, dtype=dtype)
+        return (1 - sample) * self.config.noise_s
+
+    def process_backbone_output(self, backbone_output: BatchFeature) -> BatchFeature:
+        backbone_features = self.vlln(backbone_output["backbone_features"])
+        backbone_output["backbone_features"] = self.vl_self_attention(backbone_features)
+        return backbone_output
+
+    def forward(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        self.set_frozen_modules_to_eval_mode()
+        backbone_output = self.process_backbone_output(backbone_output)
+        vl_embeds = backbone_output.backbone_features
+        device = vl_embeds.device
+        embodiment_id = action_input.embodiment_id
+
+        if action_input.state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = action_input.state.view(action_input.state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, embodiment_id)
+
+        if self.training and self.state_dropout_prob > 0:
+            do_dropout = (
+                torch.rand(state_features.shape[0], device=state_features.device) < self.state_dropout_prob
+            )
+            state_features = state_features * (1 - do_dropout[:, None, None].to(dtype=state_features.dtype))
+
+        actions = action_input.action
+        noise = torch.randn(actions.shape, device=actions.device, dtype=actions.dtype)
+        t = self.sample_time(actions.shape[0], device=actions.device, dtype=actions.dtype)
+        t = t[:, None, None]
+        noisy_trajectory = (1 - t) * noise + t * actions
+        velocity = actions - noise
+        t_discretized = (t[:, 0, 0] * self.num_timestep_buckets).long()
+        action_features = self.action_encoder(noisy_trajectory, t_discretized, embodiment_id)
+
+        if self.config.add_pos_embed:
+            pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+            action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+
+        sa_embs = torch.cat((state_features, action_features), dim=1)
+        if self.config.use_alternate_vl_dit:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+                image_mask=backbone_output.image_mask,
+                backbone_attention_mask=backbone_output.backbone_attention_mask,
+            )
+        else:
+            model_output, _ = self.model(
+                hidden_states=sa_embs,
+                encoder_hidden_states=vl_embeds,
+                encoder_attention_mask=backbone_output.backbone_attention_mask,
+                timestep=t_discretized,
+                return_all_hidden_states=True,
+            )
+
+        pred = self.action_decoder(model_output, embodiment_id)
+        pred_actions = pred[:, -actions.shape[1] :]
+        action_mask = action_input.action_mask.to(dtype=pred_actions.dtype)
+        action_loss = F.mse_loss(pred_actions, velocity, reduction="none") * action_mask
+        loss = action_loss.sum() / (action_mask.sum() + 1e-6)
+        return BatchFeature(
+            data={
+                "loss": loss,
+                "action_loss": action_loss,
+                "action_mask": action_mask,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    def _encode_features(self, backbone_output: BatchFeature, action_input: BatchFeature) -> BatchFeature:
+        backbone_output = self.process_backbone_output(backbone_output)
+        state = action_input.state
+        if state.shape[1] != self.config.state_history_length:
+            raise ValueError("state history length does not match GR00T N1.7 config.")
+        state = state.view(state.shape[0], 1, -1)
+        state_features = self.state_encoder(state, action_input.embodiment_id)
+        return BatchFeature(
+            data={"backbone_features": backbone_output.backbone_features, "state_features": state_features}
+        )
+
+    @torch.no_grad()
+    def get_action_with_features(
+        self,
+        backbone_features: torch.Tensor,
+        state_features: torch.Tensor,
+        embodiment_id: torch.Tensor,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        vl_embeds = backbone_features
+        batch_size = vl_embeds.shape[0]
+        device = vl_embeds.device
+        actions = torch.randn(
+            size=(batch_size, self.config.action_horizon, self.action_dim),
+            dtype=vl_embeds.dtype,
+            device=device,
+        )
+        dt = 1.0 / self.num_inference_timesteps
+        vel_strength = torch.ones_like(actions)
+
+        if "action" in action_input:
+            if options is None:
+                raise ValueError("RTC options are required when action is provided to get_action.")
+            action_horizon_before_padding = options["action_horizon"]
+            actions[:, : options["rtc_overlap_steps"], :] = action_input["action"][
+                :,
+                action_horizon_before_padding - options["rtc_overlap_steps"] : action_horizon_before_padding,
+                :,
+            ]
+            vel_strength[:, : options["rtc_frozen_steps"], :] = 0.0
+            intermediate_steps = options["rtc_overlap_steps"] - options["rtc_frozen_steps"]
+            t = torch.linspace(0.0, 1.0, intermediate_steps + 2, device=device)
+            ramp = 1 - torch.exp(-options["rtc_ramp_rate"] * t)
+            ramp = ramp / ramp[-1].clamp_min(1e-8)
+            vel_strength[:, options["rtc_frozen_steps"] : options["rtc_overlap_steps"], :] = ramp[1:-1][
+                None, :, None
+            ].to(device)
+
+        for t_step in range(self.num_inference_timesteps):
+            t_cont = t_step / float(self.num_inference_timesteps)
+            t_discretized = int(t_cont * self.num_timestep_buckets)
+            timesteps_tensor = torch.full(size=(batch_size,), fill_value=t_discretized, device=device)
+            action_features = self.action_encoder(actions, timesteps_tensor, embodiment_id)
+            if self.config.add_pos_embed:
+                pos_ids = torch.arange(action_features.shape[1], dtype=torch.long, device=device)
+                action_features = action_features + self.position_embedding(pos_ids).unsqueeze(0)
+            sa_embs = torch.cat((state_features, action_features), dim=1)
+
+            if self.config.use_alternate_vl_dit:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                    image_mask=backbone_output.image_mask,
+                    backbone_attention_mask=backbone_output.backbone_attention_mask,
+                )
+            else:
+                model_output = self.model(
+                    hidden_states=sa_embs,
+                    encoder_hidden_states=vl_embeds,
+                    timestep=timesteps_tensor,
+                )
+            pred = self.action_decoder(model_output, embodiment_id)
+            actions = actions + dt * pred[:, -self.action_horizon :] * vel_strength
+
+        return BatchFeature(
+            data={
+                "action_pred": actions,
+                "backbone_features": vl_embeds,
+                "state_features": state_features,
+            }
+        )
+
+    @torch.no_grad()
+    def get_action(
+        self,
+        backbone_output: BatchFeature,
+        action_input: BatchFeature,
+        options: dict[str, Any] | None = None,
+    ) -> BatchFeature:
+        features = self._encode_features(backbone_output, action_input)
+        return self.get_action_with_features(
+            backbone_features=features.backbone_features,
+            state_features=features.state_features,
+            embodiment_id=action_input.embodiment_id,
+            backbone_output=backbone_output,
+            action_input=action_input,
+            options=options,
+        )
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    def prepare_input(self, batch: dict[str, Any]) -> BatchFeature:
+        return BatchFeature(data=batch)
+
+
+def _is_cosmos_reason2_backbone(model_name: str) -> bool:
+    return str(model_name).rstrip("/") == "nvidia/Cosmos-Reason2-2B"
+
+
+def _cosmos_reason2_qwen3_vl_config() -> PretrainedConfig:
+    if Qwen3VLConfig is None:
+        raise ImportError(
+            "Qwen3VLConfig is required for GR00T N1.7. "
+            "Install the GR00T optional dependencies with `pip install 'lerobot[groot]'`."
+        )
+    return Qwen3VLConfig(
+        image_token_id=151655,
+        video_token_id=151656,
+        vision_start_token_id=151652,
+        vision_end_token_id=151653,
+        tie_word_embeddings=True,
+        text_config={
+            "attention_bias": False,
+            "attention_dropout": 0.0,
+            "bos_token_id": 151643,
+            "dtype": "bfloat16",
+            "eos_token_id": 151645,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_size": 2048,
+            "initializer_range": 0.02,
+            "intermediate_size": 6144,
+            "max_position_embeddings": 262144,
+            "model_type": "qwen3_vl_text",
+            "num_attention_heads": 16,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 8,
+            "rms_norm_eps": 1e-6,
+            "rope_scaling": {
+                "mrope_interleaved": True,
+                "mrope_section": [24, 20, 20],
+                "rope_type": "default",
+            },
+            "rope_theta": 5000000,
+            "tie_word_embeddings": True,
+            "use_cache": True,
+            "vocab_size": 151936,
+        },
+        vision_config={
+            "deepstack_visual_indexes": [5, 11, 17],
+            "depth": 24,
+            "hidden_act": "gelu_pytorch_tanh",
+            "hidden_size": 1024,
+            "in_channels": 3,
+            "initializer_range": 0.02,
+            "intermediate_size": 4096,
+            "model_type": "qwen3_vl",
+            "num_heads": 16,
+            "num_position_embeddings": 2304,
+            "out_hidden_size": 2048,
+            "patch_size": 16,
+            "spatial_merge_size": 2,
+            "temporal_patch_size": 2,
+        },
+    )
+
+
+def get_backbone_cls(config: GR00TN17Config):
+    if "nvidia/Cosmos-Reason2" in config.model_name or "Qwen/Qwen3-VL" in config.model_name:
+        return Qwen3Backbone
+    if config.backbone_model_type == "qwen":
+        # Local backbone checkpoints (e.g. hub-cache snapshot paths) contain neither hub
+        # marker, so trust the explicit backbone type but surface what is being assumed.
+        logger.warning(
+            "Unrecognized GR00T N1.7 backbone model name '%s'; assuming a Qwen3-VL-compatible "
+            "backbone because backbone_model_type='qwen'.",
+            config.model_name,
+        )
+        return Qwen3Backbone
+    raise ValueError(f"Unsupported GR00T N1.7 backbone model: {config.model_name}")
+
+
+class GR00TN17(PreTrainedModel):
+    """GR00T N1.7 model with a Cosmos-Reason2/Qwen3-VL backbone."""
+
+    config_class = GR00TN17Config
+    supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: GR00TN17Config,
+        transformers_loading_kwargs: dict[str, Any] | None = None,
+        load_backbone_weights: bool = True,
+    ):
+        super().__init__(config)
+        transformers_loading_kwargs = transformers_loading_kwargs or {"trust_remote_code": True}
+        self.config = config
+        backbone_cls = get_backbone_cls(config)
+        self.backbone = backbone_cls(
+            model_name=config.model_name,
+            tune_llm=config.tune_llm,
+            tune_visual=config.tune_visual,
+            select_layer=config.select_layer,
+            reproject_vision=config.reproject_vision,
+            use_flash_attention=config.use_flash_attention,
+            load_bf16=config.load_bf16,
+            tune_top_llm_layers=config.tune_top_llm_layers,
+            trainable_params_fp32=config.backbone_trainable_params_fp32,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_pretrained_weights=load_backbone_weights,
+        )
+        self.action_head = GR00TN17ActionHead(config)
+        self.post_init()
+
+    def prepare_input(self, inputs: dict[str, Any]) -> tuple[BatchFeature, BatchFeature]:
+        global tree
+        if tree is None:
+            require_package("dm-tree", extra="groot", import_name="tree")
+            tree = importlib.import_module("tree")
+        backbone_inputs = self.backbone.prepare_input(inputs)
+        action_inputs = self.action_head.prepare_input(inputs)
+
+        def to_device_with_dtype(x):
+            if not isinstance(x, torch.Tensor):
+                return x
+            if torch.is_floating_point(x):
+                return x.to(self.device, dtype=self.dtype)
+            return x.to(self.device)
+
+        return (
+            tree.map_structure(to_device_with_dtype, backbone_inputs),
+            tree.map_structure(to_device_with_dtype, action_inputs),
+        )
+
+    def forward(self, inputs: dict[str, Any]) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head(backbone_outputs, action_inputs)
+
+    def get_action(self, inputs: dict[str, Any], options: dict[str, Any] | None = None) -> BatchFeature:
+        backbone_inputs, action_inputs = self.prepare_input(inputs)
+        backbone_outputs = self.backbone(backbone_inputs)
+        return self.action_head.get_action(backbone_outputs, action_inputs, options)
+
+    @property
+    def device(self) -> torch.device:
+        return next(iter(self.parameters())).device
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return next(iter(self.parameters())).dtype
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
+        tune_visual = kwargs.pop("tune_visual", True)
+        tune_llm = kwargs.pop("tune_llm", False)
+        tune_projector = kwargs.pop("tune_projector", True)
+        tune_diffusion_model = kwargs.pop("tune_diffusion_model", True)
+        tune_vlln = kwargs.pop("tune_vlln", True)
+        transformers_loading_kwargs = kwargs.pop("transformers_loading_kwargs", None) or {
+            "trust_remote_code": True
+        }
+        load_backbone_weights = kwargs.pop("load_backbone_weights", False)
+        # Only repo-agnostic hub kwargs are forwarded to the backbone loading kwargs:
+        # ``revision`` pins the GR00T checkpoint repo (see snapshot_download below) and would
+        # be invalid for the unrelated backbone repo (``config.model_name``). Pin the backbone
+        # itself by passing ``revision`` inside ``transformers_loading_kwargs``.
+        for key in ("cache_dir", "local_files_only", "token"):
+            if key in kwargs:
+                transformers_loading_kwargs.setdefault(key, kwargs[key])
+
+        try:
+            local_model_path = snapshot_download(
+                pretrained_model_name_or_path,
+                repo_type="model",
+                revision=kwargs.get("revision"),
+                cache_dir=kwargs.get("cache_dir"),
+                local_files_only=kwargs.get("local_files_only", False),
+                token=kwargs.get("token"),
+            )
+        except (HFValidationError, RepositoryNotFoundError):
+            local_model_path = pretrained_model_name_or_path
+
+        pretrained_model = super().from_pretrained(
+            local_model_path,
+            transformers_loading_kwargs=transformers_loading_kwargs,
+            load_backbone_weights=load_backbone_weights,
+            **kwargs,
+        )
+        pretrained_model.backbone.set_trainable_parameters(
+            tune_visual=tune_visual,
+            tune_llm=tune_llm,
+            tune_top_llm_layers=pretrained_model.config.tune_top_llm_layers,
+        )
+        pretrained_model.action_head.set_trainable_parameters(
+            tune_projector=tune_projector,
+            tune_diffusion_model=tune_diffusion_model,
+            tune_vlln=tune_vlln,
+        )
+        return pretrained_model
+
+
+def _register_with_transformers() -> None:
+    if AutoConfig is None or AutoModel is None:
+        return
+    try:
+        AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoConfig.register(GR00TN17Config.model_type, GR00TN17Config)
+    try:
+        AutoModel.register(GR00TN17Config, GR00TN17, exist_ok=True)
+    except TypeError:
+        with suppress(ValueError):
+            AutoModel.register(GR00TN17Config, GR00TN17)
+
+
+_register_with_transformers()
@@ -17,22 +17,13 @@
 """
 Groot Policy Wrapper for LeRobot Integration

-Minimal integration that delegates to Isaac-GR00T components where possible
-without porting their code. The intent is to:
-
- Download and load the pretrained GR00T model via GR00TN15.from_pretrained
- Optionally align action horizon similar to gr00t_finetune.py
- Expose predict_action via GR00T model.get_action
- Provide a training forward that can call the GR00T model forward if batch
-  structure matches.
-
-Notes:
- Dataset loading and full training orchestration is handled by Isaac-GR00T
-  TrainRunner in their codebase. If you want to invoke that flow end-to-end
-  from LeRobot, see `GrootPolicy.finetune_with_groot_runner` below.
+Minimal integration that delegates to Isaac-GR00T N1.7 components where
+possible without porting their code. Dataset loading and training
+orchestration are handled by LeRobot's standard training stack.
 """

 import builtins
+import logging
 import os
 from collections import deque
 from pathlib import Path
@@ -46,8 +37,19 @@ from lerobot.utils.constants import ACTION, OBS_IMAGES
 from lerobot.utils.import_utils import require_package

 from ..pretrained import PreTrainedPolicy
-from .configuration_groot import GrootConfig
-from .groot_n1 import GR00TN15
+from ..utils import get_device_from_parameters
+from .configuration_groot import (
+    GROOT_N1_5,
+    GROOT_N1_5_REMOVAL_GUIDANCE,
+    GROOT_N1_7,
+    GrootConfig,
+    infer_groot_model_version,
+    infer_groot_n1_7_action_execution_horizon,
+    infer_groot_n1_7_action_horizon,
+    normalize_groot_model_version,
+)
+
+logger = logging.getLogger(__name__)

 T = TypeVar("T", bound="GrootPolicy")

@@ -67,37 +69,41 @@ class GrootPolicy(PreTrainedPolicy):

        # Initialize GR00T model using ported components
        self._groot_model = self._create_groot_model()
+        self._action_queue_steps = self._resolve_action_queue_steps()

        self.reset()

    def _create_groot_model(self):
-        """Create and initialize the GR00T model using Isaac-GR00T API.
-
-        This is only called when creating a NEW policy (not when loading from checkpoint).
-
-        Steps (delegating to Isaac-GR00T):
-        1) Download and load pretrained model via GR00TN15.from_pretrained
-        2) Align action horizon with data_config if provided
-        """
+        """Create and initialize the GR00T N1.7 model using Isaac-GR00T APIs."""
        # Handle Flash Attention compatibility issues
        self._handle_flash_attention_compatibility()

-        model = GR00TN15.from_pretrained(
-            pretrained_model_name_or_path=self.config.base_model_path,
-            tune_llm=self.config.tune_llm,
-            tune_visual=self.config.tune_visual,
-            tune_projector=self.config.tune_projector,
-            tune_diffusion_model=self.config.tune_diffusion_model,
+        model_kwargs = {
+            "pretrained_model_name_or_path": self.config.base_model_path,
+            "tune_llm": self.config.tune_llm,
+            "tune_visual": self.config.tune_visual,
+            "tune_projector": self.config.tune_projector,
+            "tune_diffusion_model": self.config.tune_diffusion_model,
+        }
+        from .groot_n1_7 import GR00TN17
+
+        model = GR00TN17.from_pretrained(
+            **model_kwargs,
+            tune_vlln=True,
+            transformers_loading_kwargs={"trust_remote_code": True},
        )

-        model.compute_dtype = "bfloat16" if self.config.use_bf16 else model.compute_dtype
-        model.config.compute_dtype = model.compute_dtype
+        # GR00TN17 defines no compute_dtype attribute, so only record the
+        # bf16 preference when it is enabled instead of reading a default back.
+        if self.config.use_bf16:
+            model.compute_dtype = "bfloat16"
+            model.config.compute_dtype = "bfloat16"

        return model

    def reset(self):
        """Reset policy state when environment resets."""
-        self._action_queue = deque([], maxlen=self.config.n_action_steps)
+        self._action_queue = deque([], maxlen=self._action_queue_steps)

    @classmethod
    def from_pretrained(
@@ -118,7 +124,7 @@ class GrootPolicy(PreTrainedPolicy):
        """Load Groot policy from pretrained model.

        Handles two cases:
-        1. Base GR00T models (e.g., 'nvidia/GR00T-N1.5-3B') - loads the raw model
+        1. Base GR00T N1.7 models - loads the raw model
        2. Fine-tuned LeRobot checkpoints - loads config and weights from safetensors

        Args:
@@ -141,9 +147,15 @@ class GrootPolicy(PreTrainedPolicy):
        from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
        from huggingface_hub.errors import HfHubHTTPError

-        print(
-            "The Groot policy is a wrapper around Nvidia's GR00T N1.5 model.\n"
-            f"Loading pretrained model from: {pretrained_name_or_path}"
+        requested_version = (
+            normalize_groot_model_version(config.model_version)
+            if config is not None
+            else infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
+        )
+        logger.info(
+            "The Groot policy wraps NVIDIA's GR00T %s model. Loading pretrained model from: %s",
+            requested_version,
+            pretrained_name_or_path,
        )

        model_id = str(pretrained_name_or_path)
@@ -174,7 +186,7 @@ class GrootPolicy(PreTrainedPolicy):

        if is_finetuned_checkpoint:
            # This is a fine-tuned LeRobot checkpoint - use parent class loading
-            print("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
+            logger.info("Detected fine-tuned LeRobot checkpoint, loading with state dict...")
            return super().from_pretrained(
                pretrained_name_or_path=pretrained_name_or_path,
                config=config,
@@ -190,11 +202,15 @@ class GrootPolicy(PreTrainedPolicy):
            )

        # This is a base GR00T model - load it fresh
-        print("Detected base GR00T model, loading from HuggingFace...")
+        logger.info("Detected base GR00T model, loading from HuggingFace...")

        if config is None:
+            model_version = infer_groot_model_version(str(pretrained_name_or_path)) or GROOT_N1_7
            # Create default config with the pretrained path
-            config = GrootConfig(base_model_path=str(pretrained_name_or_path))
+            config = GrootConfig(
+                model_version=model_version,
+                base_model_path=str(pretrained_name_or_path),
+            )

            # Add minimal visual feature required for validation
            # validate_features() will automatically add state and action features
@@ -215,6 +231,16 @@ class GrootPolicy(PreTrainedPolicy):
            if hasattr(config, key):
                setattr(config, key, value)

+        config.model_version = normalize_groot_model_version(config.model_version)
+        inferred_version = infer_groot_model_version(config.base_model_path)
+        if inferred_version is not None and inferred_version != config.model_version:
+            message = (
+                f"GR00T model_version '{config.model_version}' does not match base_model_path "
+                f"'{config.base_model_path}', which looks like '{inferred_version}'."
+            )
+            if inferred_version == GROOT_N1_5:
+                message = f"{message} {GROOT_N1_5_REMOVAL_GUIDANCE}"
+            raise ValueError(message)
        # Create a fresh policy instance - this will automatically load the GR00T model
        # in __init__ via _create_groot_model()
        policy = cls(config)
@@ -225,21 +251,160 @@ class GrootPolicy(PreTrainedPolicy):
    def get_optim_params(self) -> dict:
        return self.parameters()

+    def _resolve_action_queue_steps(self) -> int:
+        n_action_steps = int(self.config.n_action_steps)
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        execution_horizon = infer_groot_n1_7_action_execution_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        horizons = [n_action_steps]
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+        if execution_horizon is not None:
+            horizons.append(execution_horizon)
+        return min(horizons)
+
+    def _resolve_prediction_horizon(self, actions: Tensor) -> int:
+        """Return the policy-facing action horizon for a native GR00T prediction."""
+
+        horizons = [actions.shape[1]]
+        checkpoint_action_horizon = infer_groot_n1_7_action_horizon(
+            self.config.base_model_path,
+            self.config.embodiment_tag,
+        )
+        if checkpoint_action_horizon is not None:
+            horizons.append(checkpoint_action_horizon)
+
+        for horizon in (self.config.chunk_size, self.config.n_action_steps):
+            horizon = int(horizon)
+            if horizon > 0:
+                horizons.append(horizon)
+
+        return max(1, min(horizons))
+
+    def _filter_groot_inputs(self, batch: dict[str, Tensor], *, include_action: bool) -> dict[str, Tensor]:
+        allowed_base = {"state", "state_mask", "embodiment_id"}
+        if include_action:
+            allowed_base.update({"action", "action_mask"})
+
+        allowed_base.update(
+            {
+                "input_ids",
+                "attention_mask",
+                "pixel_values",
+                "image_grid_thw",
+                "mm_token_type_ids",
+                "pixel_values_videos",
+                "video_grid_thw",
+            }
+        )
+        allowed_base.add("action_mask")
+
+        return {
+            k: v for k, v in batch.items() if k in allowed_base and not (k.startswith("next.") or k == "info")
+        }
+
+    def _prepare_n1_7_rtc_inputs(
+        self,
+        inputs: dict[str, Tensor],
+        *,
+        inference_delay: object,
+        prev_chunk_left_over: object,
+    ) -> tuple[dict[str, Tensor], dict[str, object] | None]:
+        if prev_chunk_left_over is None:
+            return inputs, None
+        if not isinstance(prev_chunk_left_over, torch.Tensor):
+            raise TypeError("prev_chunk_left_over must be a torch.Tensor for GR00T N1.7 RTC.")
+        if prev_chunk_left_over.numel() == 0:
+            return inputs, None
+
+        prev_actions = prev_chunk_left_over
+        if prev_actions.ndim == 2:
+            prev_actions = prev_actions.unsqueeze(0)
+        elif prev_actions.ndim != 3:
+            raise ValueError("prev_chunk_left_over must have shape (T, A) or (B, T, A) for GR00T N1.7 RTC.")
+
+        state = inputs.get("state")
+        if state is None:
+            raise ValueError("GR00T N1.7 RTC requires `state` in the preprocessed batch.")
+        batch_size = state.shape[0]
+        if prev_actions.shape[0] == 1 and batch_size > 1:
+            prev_actions = prev_actions.expand(batch_size, -1, -1).clone()
+        elif prev_actions.shape[0] != batch_size:
+            raise ValueError("prev_chunk_left_over batch size must match the current GR00T N1.7 batch size.")
+
+        # The generic LeRobot RTC engine pads short leftovers with exact zero
+        # rows for fixed-shape policy calls. Native GR00T N1.7 RTC treats every
+        # provided prefix row as a real action constraint, so strip that padding
+        # before constructing the native overlap options.
+        valid_prefix_rows = prev_actions.detach().abs().sum(dim=(0, 2)) > 0
+        if valid_prefix_rows.any():
+            valid_prefix_steps = int(valid_prefix_rows.nonzero()[-1].item()) + 1
+            prev_actions = prev_actions[:, :valid_prefix_steps, :]
+        else:
+            return inputs, None
+
+        model_action_horizon = int(
+            getattr(self._groot_model.config, "action_horizon", self.config.chunk_size)
+        )
+        max_action_dim = int(getattr(self._groot_model.config, "max_action_dim", self.config.max_action_dim))
+        if prev_actions.shape[1] > model_action_horizon:
+            prev_actions = prev_actions[:, -model_action_horizon:, :]
+
+        action_horizon = int(prev_actions.shape[1])
+        if action_horizon <= 0:
+            return inputs, None
+
+        if prev_actions.shape[2] > max_action_dim:
+            prev_actions = prev_actions[:, :, :max_action_dim]
+        elif prev_actions.shape[2] < max_action_dim:
+            pad = torch.zeros(
+                prev_actions.shape[0],
+                prev_actions.shape[1],
+                max_action_dim - prev_actions.shape[2],
+                dtype=prev_actions.dtype,
+                device=prev_actions.device,
+            )
+            prev_actions = torch.cat([prev_actions, pad], dim=2)
+
+        prev_actions = prev_actions.to(device=state.device, dtype=state.dtype)
+
+        rtc_config = getattr(self.config, "rtc_config", None)
+        execution_horizon = int(getattr(rtc_config, "execution_horizon", action_horizon))
+        overlap_steps = max(0, min(action_horizon, execution_horizon))
+        if overlap_steps == 0:
+            return inputs, None
+
+        try:
+            frozen_steps = int(inference_delay or 0)
+        except (TypeError, ValueError):
+            frozen_steps = 0
+        frozen_steps = max(0, min(frozen_steps, overlap_steps))
+
+        options = {
+            "action_horizon": action_horizon,
+            "rtc_overlap_steps": overlap_steps,
+            "rtc_frozen_steps": frozen_steps,
+            "rtc_ramp_rate": float(getattr(self._groot_model.config, "rtc_ramp_rate", 6.0)),
+        }
+
+        inputs = dict(inputs)
+        inputs["action"] = prev_actions
+        return inputs, options
+
    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
        """Training forward pass.

        Delegates to Isaac-GR00T model.forward when inputs are compatible.
        """
-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        allowed_base = {"state", "state_mask", "action", "action_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        groot_inputs = self._filter_groot_inputs(batch, include_action=True)

        # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)

        # Run GR00T forward under bf16 autocast when enabled to reduce activation memory
        # Rationale: Matches original GR00T finetuning (bf16 compute, fp32 params) and avoids fp32 upcasts.
@@ -248,38 +413,54 @@ class GrootPolicy(PreTrainedPolicy):

        # Isaac-GR00T returns a BatchFeature; loss key is typically 'loss'
        loss = outputs.get("loss")
+        if loss is None:
+            raise RuntimeError(
+                "GR00T model.forward did not return a 'loss'. Training batches must include "
+                "'action' and 'action_mask'; check the preprocessor output."
+            )

        loss_dict = {"loss": loss.item()}

        return loss, loss_dict

    @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor], **kwargs: object) -> Tensor:
        """Predict a chunk of actions for inference by delegating to Isaac-GR00T.

        Returns a tensor of shape (B, n_action_steps, action_dim).
+
+        For N1.7, LeRobot's RTC leftovers are converted into the native GR00T
+        action-overlap options before calling the underlying model.
        """
        self.eval()

-        # Build a clean input dict for GR00T: keep only tensors GR00T consumes
-        # Preprocessing is handled by the processor pipeline, so we just filter the batch
-        # NOTE: During inference, we should NOT pass action/action_mask (that's what we're predicting)
-        allowed_base = {"state", "state_mask", "embodiment_id"}
-        groot_inputs = {
-            k: v
-            for k, v in batch.items()
-            if (k in allowed_base or k.startswith("eagle_")) and not (k.startswith("next.") or k == "info")
-        }
+        # Preprocessing is handled by the processor pipeline, so we just filter the batch.
+        # During inference, we do not pass action because it is predicted.
+        # N1.7 still carries a 2-D action horizon mask from its checkpoint processor.
+        groot_inputs = self._filter_groot_inputs(batch, include_action=False)
+        groot_options = None
+        if self.config.model_version == GROOT_N1_7:
+            groot_inputs, groot_options = self._prepare_n1_7_rtc_inputs(
+                groot_inputs,
+                inference_delay=kwargs.get("inference_delay"),
+                prev_chunk_left_over=kwargs.get("prev_chunk_left_over"),
+            )

        # Get device from model parameters
-        device = next(self.parameters()).device
+        device = get_device_from_parameters(self)

        # Use bf16 autocast for inference to keep memory low and match backbone dtype
        with torch.autocast(device_type=device.type, dtype=torch.bfloat16, enabled=self.config.use_bf16):
-            outputs = self._groot_model.get_action(groot_inputs)
+            if groot_options is not None:
+                outputs = self._groot_model.get_action(groot_inputs, options=groot_options)
+            else:
+                outputs = self._groot_model.get_action(groot_inputs)

        actions = outputs.get("action_pred")

+        prediction_horizon = self._resolve_prediction_horizon(actions)
+        actions = actions[:, :prediction_horizon]
+
        original_action_dim = self.config.output_features[ACTION].shape[0]
        actions = actions[:, :, :original_action_dim]

@@ -292,40 +473,28 @@ class GrootPolicy(PreTrainedPolicy):

        if len(self._action_queue) == 0:
            actions = self.predict_action_chunk(batch)
-            self._action_queue.extend(actions.transpose(0, 1))
+            self._action_queue.extend(actions[:, : self._action_queue_steps].transpose(0, 1))
        return self._action_queue.popleft()

    # -------------------------
    # Internal helpers
    # -------------------------
    def _handle_flash_attention_compatibility(self) -> None:
-        """Handle Flash Attention compatibility issues by setting environment variables.
+        """Log Flash Attention availability (diagnostic only).

-        This addresses the common 'undefined symbol' error that occurs when Flash Attention
-        is compiled against a different PyTorch version than what's currently installed.
+        The GR00T N1.7 backbone automatically falls back to SDPA when ``flash_attn`` is
+        unavailable (see ``Qwen3Backbone``), so this probe only emits a hint; it does not
+        change behaviour or mutate global state.
        """
-
-        # Set environment variables to handle Flash Attention compatibility
-        # These help with symbol resolution issues
-        os.environ.setdefault("FLASH_ATTENTION_FORCE_BUILD", "0")
-        os.environ.setdefault("FLASH_ATTENTION_SKIP_CUDA_BUILD", "0")
-
-        # Try to import flash_attn and handle failures gracefully
        try:
            import flash_attn

-            print(f"[GROOT] Flash Attention version: {flash_attn.__version__}")
-        except ImportError as e:
-            print(f"[GROOT] Flash Attention not available: {e}")
-            print("[GROOT] Will use fallback attention mechanism")
-        except Exception as e:
-            if "undefined symbol" in str(e):
-                print(f"[GROOT] Flash Attention compatibility issue detected: {e}")
-                print("[GROOT] This is likely due to PyTorch/Flash Attention version mismatch")
-                print("[GROOT] Consider reinstalling Flash Attention with compatible version:")
-                print("  pip uninstall flash-attn")
-                print("  pip install --no-build-isolation flash-attn==2.6.3")
-                print("[GROOT] Continuing with fallback attention mechanism")
-            else:
-                print(f"[GROOT] Flash Attention error: {e}")
-                print("[GROOT] Continuing with fallback attention mechanism")
+            logger.debug("Flash Attention %s is available.", flash_attn.__version__)
+        except ImportError:
+            logger.debug("Flash Attention is not installed; the GR00T backbone will use SDPA.")
+        except Exception as e:  # noqa: BLE001
+            logger.warning(
+                "Flash Attention failed to import (%s); the GR00T backbone will use SDPA. If this is "
+                "an 'undefined symbol' error, reinstall a flash-attn build matching your torch version.",
+                e,
+            )
@@ -1,47 +0,0 @@
-from pathlib import Path
-from shutil import copytree
-
-from huggingface_hub import hf_hub_download
-
-
-def ensure_eagle_cache_ready(vendor_dir: Path, cache_dir: Path, assets_repo: str) -> None:
-    """Populate the Eagle processor directory in cache and ensure tokenizer assets exist.
-
-    - Copies the vendored Eagle files into cache_dir (overwriting when needed).
-    - Downloads vocab.json and merges.txt into the same cache_dir if missing.
-    """
-    cache_dir = Path(cache_dir)
-    vendor_dir = Path(vendor_dir)
-
-    try:
-        # Populate/refresh cache with vendor files to ensure a complete processor directory
-        print(f"[GROOT] Copying vendor Eagle files to cache: {vendor_dir} -> {cache_dir}")
-        copytree(vendor_dir, cache_dir, dirs_exist_ok=True)
-    except Exception as exc:  # nosec: B110
-        print(f"[GROOT] Warning: Failed to copy vendor Eagle files to cache: {exc}")
-
-    required_assets = [
-        "vocab.json",
-        "merges.txt",
-        "added_tokens.json",
-        "chat_template.json",
-        "special_tokens_map.json",
-        "config.json",
-        "generation_config.json",
-        "preprocessor_config.json",
-        "processor_config.json",
-        "tokenizer_config.json",
-    ]
-
-    print(f"[GROOT] Assets repo: {assets_repo} \n Cache dir: {cache_dir}")
-
-    for fname in required_assets:
-        dst = cache_dir / fname
-        if not dst.exists():
-            print(f"[GROOT] Fetching {fname}")
-            hf_hub_download(
-                repo_id=assets_repo,
-                filename=fname,
-                repo_type="model",
-                local_dir=str(cache_dir),
-            )
@@ -0,0 +1 @@
+../../../../docs/source/policy_molmoact2_README.md
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_molmoact2 import MolmoAct2Config
+from .modeling_molmoact2 import MolmoAct2Policy
+from .processor_molmoact2 import make_molmoact2_pre_post_processors
+
+__all__ = ["MolmoAct2Config", "MolmoAct2Policy", "make_molmoact2_pre_post_processors"]
@@ -0,0 +1,519 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import json
+import math
+import os
+from contextlib import suppress
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any
+
+from huggingface_hub import snapshot_download
+
+from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature, PreTrainedConfig
+from lerobot.optim import (
+    AdamWConfig,
+    CosineDecayWithWarmupSchedulerConfig,
+    LRSchedulerConfig,
+    OptimizerConfig,
+)
+from lerobot.utils.constants import ACTION, OBS_STATE
+
+from ..rtc.configuration_rtc import RTCConfig
+
+MOLMOACT2_DEFAULT_NUM_IMAGES = 2
+MOLMOACT2_IMAGE_TOKENS_PER_IMAGE = 196
+MOLMOACT2_FIXED_PROMPT_TOKEN_BUDGET = 80
+MOLMOACT2_TASK_TOKEN_BUDGET = 32
+MOLMOACT2_SEQUENCE_LENGTH_MARGIN = 32
+MOLMOACT2_SEQUENCE_LENGTH_MULTIPLE = 64
+MOLMOACT2_DISCRETE_ACTION_WRAPPER_TOKENS = 4
+MOLMOACT2_MIN_DISCRETE_ACTION_TOKENS_PER_STEP = 6
+MOLMOACT2_DISCRETE_ACTION_TOKENS_PER_DIM = 0.95
+
+
+def _hf_token() -> str | None:
+    return os.environ.get("HF_TOKEN") or os.environ.get("HF_ACCESS_TOKEN")
+
+
+def _resolve_checkpoint_location(
+    checkpoint_path: str,
+    *,
+    revision: str | None = None,
+    force_download: bool = False,
+) -> str:
+    checkpoint_path = str(checkpoint_path or "").strip()
+    if not checkpoint_path:
+        raise ValueError("MolmoAct2 policy requires `checkpoint_path`.")
+    local_path = Path(checkpoint_path).expanduser()
+    if local_path.exists():
+        return str(local_path)
+    return snapshot_download(
+        repo_id=checkpoint_path,
+        repo_type="model",
+        revision=revision,
+        force_download=force_download,
+        ignore_patterns=["*.py", "*.pyc", "__pycache__/*"],
+        token=_hf_token(),
+    )
+
+
+def _load_hf_norm_metadata_for_tag(
+    checkpoint_path: str,
+    *,
+    revision: str | None,
+    force_download: bool,
+    norm_tag: str | None,
+) -> dict[str, Any]:
+    norm_tag = str(norm_tag or "").strip()
+    if not norm_tag:
+        return {}
+    checkpoint_location = Path(
+        _resolve_checkpoint_location(
+            checkpoint_path,
+            revision=revision,
+            force_download=force_download,
+        )
+    )
+    norm_stats_filename = "norm_stats.json"
+    config_path = checkpoint_location / "config.json"
+    if config_path.exists():
+        with suppress(OSError, json.JSONDecodeError):
+            norm_stats_filename = str(
+                json.loads(config_path.read_text()).get("norm_stats_filename") or norm_stats_filename
+            )
+    stats_path = checkpoint_location / norm_stats_filename
+    if not stats_path.exists():
+        raise FileNotFoundError(
+            f"MolmoAct2 HF checkpoint is missing {norm_stats_filename!r}; cannot resolve norm_tag={norm_tag!r}."
+        )
+    payload = json.loads(stats_path.read_text())
+    metadata_by_tag = payload.get("metadata_by_tag")
+    if not isinstance(metadata_by_tag, dict):
+        raise ValueError(f"MolmoAct2 norm stats file {stats_path} has no metadata_by_tag mapping.")
+    metadata = metadata_by_tag.get(norm_tag)
+    if not isinstance(metadata, dict):
+        available = sorted(str(tag) for tag in metadata_by_tag)
+        raise ValueError(f"Unknown MolmoAct2 norm_tag={norm_tag!r}. Available tags: {available}.")
+    return metadata
+
+
+@LRSchedulerConfig.register_subclass("molmoact2_cosine_decay_with_warmup")
+@dataclass
+class MolmoAct2CosineDecayWithWarmupSchedulerConfig(CosineDecayWithWarmupSchedulerConfig):
+    """MolmoAct2-local cosine scheduler with optional decay-step auto-match.
+
+    LeRobot's generic cosine scheduler keeps an explicit integer decay length.
+    For MolmoAct2, leaving num_decay_steps unset means "decay across this run's
+    training steps"; build() is the first point where num_training_steps is known.
+    """
+
+    num_decay_steps: int | None
+
+    def build(self, optimizer, num_training_steps: int):
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.peak_lr,
+            decay_lr=self.decay_lr,
+            num_warmup_steps=self.num_warmup_steps,
+            num_decay_steps=num_training_steps if self.num_decay_steps is None else self.num_decay_steps,
+        ).build(optimizer, num_training_steps=num_training_steps)
+
+
+def _round_up(value: int, multiple: int) -> int:
+    return int(math.ceil(value / multiple) * multiple)
+
+
+def infer_molmoact2_max_sequence_length(
+    *,
+    num_images: int,
+    state_dim: int,
+    action_dim: int,
+    action_horizon: int,
+    include_discrete_action: bool,
+) -> int:
+    """Infer the padded text/image sequence cap from MolmoAct2's fixed token layout."""
+    if num_images < 1:
+        num_images = MOLMOACT2_DEFAULT_NUM_IMAGES
+    if state_dim < 0:
+        state_dim = 0
+    if action_dim < 1:
+        action_dim = 1
+    if action_horizon < 1:
+        action_horizon = 1
+
+    image_tokens = num_images * MOLMOACT2_IMAGE_TOKENS_PER_IMAGE
+    prompt_tokens = (
+        MOLMOACT2_FIXED_PROMPT_TOKEN_BUDGET
+        + MOLMOACT2_TASK_TOKEN_BUDGET
+        + state_dim
+        + MOLMOACT2_SEQUENCE_LENGTH_MARGIN
+    )
+    action_tokens = 0
+    if include_discrete_action:
+        action_tokens_per_step = max(
+            MOLMOACT2_MIN_DISCRETE_ACTION_TOKENS_PER_STEP,
+            math.ceil(action_dim * MOLMOACT2_DISCRETE_ACTION_TOKENS_PER_DIM),
+        )
+        action_tokens = MOLMOACT2_DISCRETE_ACTION_WRAPPER_TOKENS + action_horizon * action_tokens_per_step
+
+    return _round_up(
+        image_tokens + prompt_tokens + action_tokens,
+        MOLMOACT2_SEQUENCE_LENGTH_MULTIPLE,
+    )
+
+
+@PreTrainedConfig.register_subclass("molmoact2")
+@dataclass
+class MolmoAct2Config(PreTrainedConfig):
+    """MolmoAct2 policy backed by the converted HF checkpoint implementation."""
+
+    checkpoint_path: str = "allenai/MolmoAct2"
+    checkpoint_revision: str | None = None
+    checkpoint_force_download: bool = False
+
+    n_obs_steps: int = 1
+    chunk_size: int = 30
+    n_action_steps: int = 30
+
+    action_mode: str = "both"
+    inference_action_mode: str | None = None
+    discrete_action_tokenizer: str = "allenai/MolmoAct2-FAST-Tokenizer"
+    discrete_generation_max_steps: int | None = None
+    norm_tag: str | None = None
+
+    setup_type: str = ""
+    control_mode: str = ""
+    image_keys: list[str] = field(default_factory=list)
+    normalize_language: bool = True
+    add_setup_tokens: bool = True
+    add_control_tokens: bool = True
+    normalize_gripper: bool = False
+    num_state_tokens: int = 256
+    # Leave unset for the default MolmoAct2 sequence budget inferred from the fixed
+    # image/prompt/state/action token layout. Override only for unusual long prompts.
+    max_sequence_length: int | None = None
+
+    # Fixed by released MolmoAct2 checkpoints. We validate this at model load.
+    expected_max_action_dim: int = 32
+
+    # Flow-matching training knobs copied from the original MolmoAct2 training path.
+    num_flow_timesteps: int = 8
+    flow_matching_cutoff: float = 1.0
+    flow_matching_time_offset: float = 0.001
+    flow_matching_time_scale: float = 0.999
+    flow_matching_beta_alpha: float = 1.0
+    flow_matching_beta_beta: float = 1.5
+    num_inference_steps: int | None = None
+    mask_action_dim_padding: bool = True
+    enable_inference_cuda_graph: bool = True
+    # MolmoAct2-local eval option. When enabled, stochastic continuous action
+    # generation uses a rollout-local generator derived from eval_seed.
+    per_episode_seed: bool = False
+    eval_seed: int | None = None
+    rtc_config: RTCConfig | None = None
+
+    # Default is full finetuning with gradients from the action expert flowing into the VLM.
+    enable_lora_vlm: bool = False
+    lora_rank: int = 64
+    lora_alpha: int = 16
+    lora_dropout: float = 0.05
+    lora_bias: str = "none"
+    enable_lora_action_expert: bool = False
+    enable_knowledge_insulation: bool = False
+    freeze_embedding: bool = True
+    train_action_expert_only: bool = False
+    gradient_checkpointing: bool = False
+
+    model_dtype: str = "bfloat16"
+    softmax_auxiliary_loss: bool = True
+    softmax_auxiliary_loss_scale: float = 1e-4
+    discrete_loss_token_weighting: str = "root_subsegments_root_tokens"
+
+    optimizer_lr: float = 1e-5
+    optimizer_vit_lr: float = 5e-6
+    optimizer_connector_lr: float = 5e-6
+    optimizer_action_expert_lr: float = 5e-5
+    optimizer_betas: tuple[float, float] = (0.9, 0.95)
+    optimizer_eps: float = 1e-6
+    optimizer_weight_decay: float = 0.0
+    optimizer_grad_clip_norm: float = 1.0
+
+    scheduler_warmup_steps: int = 200
+    scheduler_decay_steps: int | None = None
+    scheduler_decay_lr: float = 1e-6
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.QUANTILES,
+            "ACTION": NormalizationMode.QUANTILES,
+        }
+    )
+
+    input_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    output_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    dataset_feature_names: dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.action_mode not in {"continuous", "discrete", "both"}:
+            raise ValueError(
+                f"Unsupported action_mode={self.action_mode!r}. "
+                "Expected one of {'continuous', 'discrete', 'both'}."
+            )
+        if self.inference_action_mode not in {None, "continuous", "discrete"}:
+            raise ValueError(
+                f"Unsupported inference_action_mode={self.inference_action_mode!r}. "
+                "Expected one of {None, 'continuous', 'discrete'}."
+            )
+        if self.inference_action_mode == "continuous" and self.action_mode == "discrete":
+            raise ValueError("MolmoAct2 action_mode='discrete' cannot run continuous inference.")
+        if self.inference_action_mode == "discrete" and self.action_mode == "continuous":
+            raise ValueError("MolmoAct2 action_mode='continuous' cannot run discrete inference.")
+        if self.train_action_expert_only and self.action_mode != "continuous":
+            raise ValueError("MolmoAct2 train_action_expert_only requires action_mode='continuous'.")
+        if self.train_action_expert_only and self.enable_lora_vlm:
+            raise ValueError("MolmoAct2 train_action_expert_only is incompatible with enable_lora_vlm.")
+        if self.enable_lora_action_expert and not self.enable_lora_vlm:
+            raise ValueError("MolmoAct2 enable_lora_action_expert requires enable_lora_vlm.")
+        if self.chunk_size < 1:
+            raise ValueError(f"chunk_size must be >= 1, got {self.chunk_size}.")
+        if self.n_action_steps < 1:
+            raise ValueError(f"n_action_steps must be >= 1, got {self.n_action_steps}.")
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError(
+                f"n_action_steps ({self.n_action_steps}) cannot exceed chunk_size ({self.chunk_size})."
+            )
+        if self.expected_max_action_dim != 32:
+            raise ValueError("MolmoAct2 released checkpoints use expected_max_action_dim=32.")
+        if self.model_dtype not in {"float32", "bfloat16", "float16"}:
+            raise ValueError(
+                f"Unsupported model_dtype={self.model_dtype!r}. Expected 'float32', 'bfloat16', or 'float16'."
+            )
+        if self.lora_rank < 1:
+            raise ValueError(f"lora_rank must be >= 1, got {self.lora_rank}.")
+        if self.lora_alpha < 1:
+            raise ValueError(f"lora_alpha must be >= 1, got {self.lora_alpha}.")
+        if not 0 <= self.lora_dropout <= 1:
+            raise ValueError(f"lora_dropout must be in [0, 1], got {self.lora_dropout}.")
+        if self.lora_bias not in {"none", "all", "lora_only"}:
+            raise ValueError(
+                f"Unsupported lora_bias={self.lora_bias!r}. Expected one of 'none', 'all', or 'lora_only'."
+            )
+        if self.discrete_loss_token_weighting not in {
+            "none",
+            "token",
+            "root_tokens",
+            "root_subsegments",
+            "root_subsegments_root_tokens",
+        }:
+            raise ValueError(
+                f"Unsupported discrete_loss_token_weighting={self.discrete_loss_token_weighting!r}."
+            )
+        if self.discrete_generation_max_steps is not None and self.discrete_generation_max_steps < 1:
+            raise ValueError(
+                f"discrete_generation_max_steps must be >= 1 or None, got {self.discrete_generation_max_steps}."
+            )
+        if self.max_sequence_length is not None and self.max_sequence_length < 1:
+            raise ValueError(f"max_sequence_length must be >= 1 or None, got {self.max_sequence_length}.")
+
+    def inferred_max_sequence_length(
+        self,
+        *,
+        num_images: int | None = None,
+        state_dim: int | None = None,
+        action_dim: int | None = None,
+        action_horizon: int | None = None,
+        include_discrete_action: bool | None = None,
+    ) -> int:
+        if self.max_sequence_length is not None:
+            return int(self.max_sequence_length)
+
+        if num_images is None:
+            num_images = len(self.image_keys) or len(self.image_features) or MOLMOACT2_DEFAULT_NUM_IMAGES
+        if state_dim is None:
+            state_feature = self.robot_state_feature
+            state_dim = int(state_feature.shape[0]) if state_feature is not None else 0
+        if action_dim is None:
+            action_feature = self.action_feature
+            action_dim = (
+                int(action_feature.shape[0]) if action_feature is not None else self.expected_max_action_dim
+            )
+        if action_horizon is None:
+            action_horizon = self.chunk_size
+        if include_discrete_action is None:
+            include_discrete_action = self.action_mode in {"discrete", "both"}
+
+        return infer_molmoact2_max_sequence_length(
+            num_images=int(num_images),
+            state_dim=int(state_dim),
+            action_dim=int(action_dim),
+            action_horizon=int(action_horizon),
+            include_discrete_action=bool(include_discrete_action),
+        )
+
+    @property
+    def observation_delta_indices(self) -> None:
+        return None
+
+    @property
+    def action_delta_indices(self) -> list[int]:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
+
+    def get_optimizer_preset(self) -> OptimizerConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self) -> LRSchedulerConfig | None:
+        return MolmoAct2CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.optimizer_lr,
+            decay_lr=self.scheduler_decay_lr,
+            num_warmup_steps=self.scheduler_warmup_steps,
+            num_decay_steps=self.scheduler_decay_steps,
+        )
+
+    def set_dataset_feature_metadata(self, features: dict[str, Any]) -> None:
+        self.dataset_feature_names = {}
+        for key in (ACTION, OBS_STATE):
+            feature = features.get(key) if isinstance(features, dict) else None
+            if isinstance(feature, dict) and feature.get("names") is not None:
+                self.dataset_feature_names[key] = feature["names"]
+
+    def validate_features(self) -> None:
+        """Validate and set up MolmoAct2 input and output features."""
+        image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
+        if not image_features:
+            raise ValueError(
+                "MolmoAct2 policy requires at least one visual input feature. "
+                "No features of type FeatureType.VISUAL found in input_features."
+            )
+
+        if OBS_STATE not in self.input_features:
+            state_feature = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(0,),
+            )
+            self.input_features[OBS_STATE] = state_feature
+
+        if ACTION not in self.output_features:
+            action_feature = PolicyFeature(
+                type=FeatureType.ACTION,
+                shape=(self.expected_max_action_dim,),
+            )
+            self.output_features[ACTION] = action_feature
+
+    def apply_norm_tag_metadata(self) -> None:
+        if not str(self.norm_tag or "").strip():
+            return
+        metadata = _load_hf_norm_metadata_for_tag(
+            self.checkpoint_path,
+            revision=self.checkpoint_revision,
+            force_download=bool(self.checkpoint_force_download),
+            norm_tag=self.norm_tag,
+        )
+        if metadata.get("action_horizon") is not None:
+            self.chunk_size = int(metadata["action_horizon"])
+        if metadata.get("n_action_steps") is not None:
+            self.n_action_steps = int(metadata["n_action_steps"])
+        if not self.setup_type and metadata.get("setup_type") is not None:
+            self.setup_type = str(metadata["setup_type"])
+        if not self.control_mode and metadata.get("control_mode") is not None:
+            self.control_mode = str(metadata["control_mode"])
+
+    def saved_policy_action_mode(self) -> str | None:
+        pretrained_path = getattr(self, "pretrained_path", None)
+        if pretrained_path is None:
+            return None
+        config_path = Path(pretrained_path) / "config.json"
+        if not config_path.exists():
+            return None
+        try:
+            mode = json.loads(config_path.read_text()).get("action_mode")
+        except (OSError, json.JSONDecodeError):
+            return None
+        if mode in {"continuous", "discrete", "both"}:
+            return str(mode)
+        return None
+
+    def training_action_mode(self, saved_policy_action_mode: str | None = None) -> str:
+        return saved_policy_action_mode or self.action_mode
+
+    def validate_inference_action_mode(self, saved_policy_action_mode: str | None = None) -> None:
+        requested_mode = self.inference_action_mode
+        if requested_mode is None:
+            return
+        training_mode = self.training_action_mode(saved_policy_action_mode)
+        if requested_mode == "continuous" and training_mode == "discrete":
+            raise ValueError(
+                "MolmoAct2 checkpoint was trained with action_mode='discrete' and cannot run "
+                "continuous inference."
+            )
+        if requested_mode == "discrete" and training_mode == "continuous":
+            raise ValueError(
+                "MolmoAct2 checkpoint was trained with action_mode='continuous' and cannot run "
+                "discrete inference. Train with action_mode='both' or action_mode='discrete' first."
+            )
+
+    def validate_checkpoint_action_mode(
+        self,
+        checkpoint_action_mode: str,
+        *,
+        has_action_expert: bool,
+    ) -> None:
+        if self.action_mode == "both" and checkpoint_action_mode != "both":
+            raise ValueError(
+                f"action_mode='both' requires checkpoint action_mode='both', got {checkpoint_action_mode!r}."
+            )
+        if self.action_mode == "discrete" and checkpoint_action_mode not in {"discrete", "both"}:
+            raise ValueError(
+                f"action_mode='discrete' requires checkpoint action_mode in {{'discrete', 'both'}}, "
+                f"got {checkpoint_action_mode!r}."
+            )
+        if self.action_mode in {"continuous", "both"} and not has_action_expert:
+            raise ValueError("Continuous MolmoAct2 training requires an action expert checkpoint.")
+
+    def resolve_inference_action_mode(
+        self,
+        requested_mode: str | None,
+        saved_policy_action_mode: str | None = None,
+    ) -> str:
+        training_mode = self.training_action_mode(saved_policy_action_mode)
+        if requested_mode is None:
+            requested_mode = self.inference_action_mode
+        if requested_mode is None:
+            raise ValueError(
+                "MolmoAct2 inference requires `inference_action_mode` to be set explicitly "
+                "to either 'continuous' or 'discrete'."
+            )
+        if requested_mode not in {"continuous", "discrete"}:
+            raise ValueError("MolmoAct2 inference_action_mode must be either 'continuous' or 'discrete'.")
+        if requested_mode == "continuous" and training_mode == "discrete":
+            raise ValueError("MolmoAct2 action_mode='discrete' checkpoint cannot run continuous inference.")
+        if requested_mode == "discrete" and training_mode == "continuous":
+            raise ValueError("MolmoAct2 action_mode='continuous' checkpoint cannot run discrete inference.")
+        return requested_mode
@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
@@ -0,0 +1,237 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+import logging
+import os
+from pathlib import Path
+from typing import ClassVar
+
+import numpy as np
+from tokenizers import ByteLevelBPETokenizer
+from tokenizers.trainers import BpeTrainer
+from huggingface_hub import snapshot_download
+from transformers import PreTrainedTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+
+def _hf_token() -> str | None:
+    return os.environ.get("HF_TOKEN") or os.environ.get("HF_ACCESS_TOKEN")
+
+
+def _resolve_tokenizer_location(
+    tokenizer_path: str,
+    *,
+    revision: str | None = None,
+    force_download: bool = False,
+) -> str:
+    local_path = Path(str(tokenizer_path)).expanduser()
+    if local_path.exists():
+        return str(local_path)
+    return snapshot_download(
+        repo_id=str(tokenizer_path),
+        repo_type="model",
+        revision=revision,
+        force_download=force_download,
+        ignore_patterns=["*.py", "*.pyc", "__pycache__/*"],
+        token=_hf_token(),
+    )
+
+
+class UniversalActionProcessor(ProcessorMixin):
+    attributes: ClassVar[list[str]] = ["tokenizer"]
+    tokenizer_class: str = "AutoTokenizer"
+
+    def __init__(
+        self,
+        tokenizer: PreTrainedTokenizerFast,
+        scale: float = 10,
+        vocab_size: int = 1024,
+        min_token: int = 0,
+        *,
+        action_dim: int | None = None,
+        time_horizon: int | None = None,
+    ):
+        self.scale = scale
+        self.vocab_size = vocab_size
+        self.min_token = min_token
+
+        # Action horizon and dimension needed during decoding. These can be specified
+        # in three ways (in order of priority):
+        # 1. passed in as kwargs to decode()
+        # 2. in the constructor
+        # 3. cached from the last time decode() was called
+        self.time_horizon = time_horizon
+        self.action_dim = action_dim
+        self.called_time_horizon = time_horizon
+        self.called_action_dim = action_dim
+
+        super().__init__(tokenizer)
+        self.bpe_tokenizer = self.tokenizer
+
+    def __call__(self, action_chunk: np.array) -> np.array:
+        from scipy.fft import dct
+
+        assert action_chunk.ndim <= 3, "Only 3 dimensions supported: [batch, timesteps, action_dim]"
+        if action_chunk.ndim == 2:
+            action_chunk = action_chunk[None, ...]
+
+        # Cache the time horizon and action dimension for decoding
+        self.called_time_horizon = action_chunk.shape[-2]
+        self.called_action_dim = action_chunk.shape[-1]
+
+        dct_coeff = dct(action_chunk, axis=1, norm="ortho")
+        dct_coeff = np.around(dct_coeff * self.scale)
+        tokens = []
+        for elem in dct_coeff:
+            token_str = "".join(map(chr, np.maximum(elem.flatten() - self.min_token, 0).astype(int)))
+            tokens.append(self.bpe_tokenizer(token_str)["input_ids"])
+        return tokens
+
+    def decode(
+        self,
+        tokens: list[list[int]],
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> np.array:
+        from scipy.fft import idct
+
+        self.time_horizon = time_horizon or self.time_horizon or self.called_time_horizon
+        self.action_dim = action_dim or self.action_dim or self.called_action_dim
+
+        # Cache the time horizon and action dimension for the next call
+        self.called_time_horizon = self.time_horizon
+        self.called_action_dim = self.action_dim
+
+        assert self.time_horizon is not None and self.action_dim is not None, (
+            "Tokenizer not initialized, call encode() once or pass in time_horizon and action_dim."
+        )
+
+        decoded_actions = []
+        for token in tokens:
+            try:
+                decoded_tokens = self.bpe_tokenizer.decode(token)
+                decoded_dct_coeff = np.array(list(map(ord, decoded_tokens))) + self.min_token
+                decoded_dct_coeff = decoded_dct_coeff.reshape(-1, self.action_dim)
+                assert decoded_dct_coeff.shape == (
+                    self.time_horizon,
+                    self.action_dim,
+                ), (
+                    f"Decoded DCT coefficients have shape {decoded_dct_coeff.shape}, expected ({self.time_horizon}, {self.action_dim})"
+                )
+            except Exception as e:
+                print(f"Error decoding tokens: {e}")
+                print(f"Tokens: {token}")
+                decoded_dct_coeff = np.zeros((self.time_horizon, self.action_dim))
+            decoded_actions.append(idct(decoded_dct_coeff / self.scale, axis=0, norm="ortho"))
+        return np.stack(decoded_actions)
+
+    @classmethod
+    def fit(
+        cls,
+        action_data: list[np.array],
+        scale: float = 10,
+        vocab_size: int = 1024,
+        *,
+        time_horizon: int | None = None,
+        action_dim: int | None = None,
+    ) -> "UniversalActionProcessor":
+        from scipy.fft import dct
+
+        # Run DCT over all inputs
+        dct_tokens = [dct(a, axis=0, norm="ortho").flatten() for a in action_data]
+
+        # Quantize and find min token
+        max_token = int(np.around(np.concatenate(dct_tokens) * scale).max())
+        min_token = int(np.around(np.concatenate(dct_tokens) * scale).min())
+        min_vocab_size = max_token - min_token
+
+        assert min_vocab_size <= vocab_size, (
+            f"Vocab size {vocab_size} is too small for the range of tokens {min_vocab_size}"
+        )
+        if min_vocab_size + 100 > vocab_size:
+            logging.warning(
+                f"Initial alphabet size {min_vocab_size} is almost as large as the vocab"
+                f"size {vocab_size}, consider increasing vocab size"
+            )
+
+        # Make token iterator for BPE training
+        def _token_iter():
+            for tokens in dct_tokens:
+                rounded_tokens = np.around(tokens * scale) - min_token
+                rounded_tokens = rounded_tokens.astype(int)
+                string = "".join(map(chr, rounded_tokens))
+                yield string
+
+        # Train BPE tokenizer
+        bpe = ByteLevelBPETokenizer()
+
+        # Set up the entire range of possible tokens as the initial alphabet
+        alphabet = [chr(i) for i in range(max_token - min_token + 1)]
+        trainer = BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=2,
+            show_progress=True,
+            special_tokens=[],
+            initial_alphabet=alphabet,
+            max_token_length=10000,
+        )
+
+        # Train the inner tokenizer (don't use ByteLevelBPETokenizer.train_from_iterator()
+        # because it doesn't support custom alphabets)
+        bpe._tokenizer.train_from_iterator(_token_iter(), trainer=trainer)
+
+        return cls(
+            PreTrainedTokenizerFast(tokenizer_object=bpe, clean_up_tokenization_spaces=False),
+            scale=scale,
+            vocab_size=vocab_size,
+            min_token=min_token,
+            time_horizon=time_horizon,
+            action_dim=action_dim,
+        )
+
+    @classmethod
+    def from_pretrained_local(
+        cls,
+        pretrained_model_name_or_path: str,
+        *,
+        revision: str | None = None,
+        force_download: bool = False,
+    ) -> "UniversalActionProcessor":
+        location = Path(
+            _resolve_tokenizer_location(
+                pretrained_model_name_or_path,
+                revision=revision,
+                force_download=force_download,
+            )
+        )
+        processor_config = {}
+        processor_config_path = location / "processor_config.json"
+        if processor_config_path.exists():
+            import json
+
+            processor_config = json.loads(processor_config_path.read_text())
+        tokenizer = PreTrainedTokenizerFast.from_pretrained(str(location))
+        return cls(
+            tokenizer,
+            scale=processor_config.get("scale", 10),
+            vocab_size=processor_config.get("vocab_size", 1024),
+            min_token=processor_config.get("min_token", 0),
+            action_dim=processor_config.get("action_dim"),
+            time_horizon=processor_config.get("time_horizon"),
+        )
@@ -0,0 +1,553 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+"""
+MolmoAct2 configuration
+"""
+
+from typing import Optional, Any
+
+from transformers import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class MolmoAct2VitConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MolmoAct2VisionTransformer`].
+    It is used to instantiate a `MolmoAct2VisionTransformer` according to the specified arguments,
+    defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Example:
+    ```python
+    >>> from transformers import MolmoAct2VitConfig, MolmoAct2VisionTransformer
+
+    >>> # Initializing a MolmoAct2VitConfig
+    >>> configuration = MolmoAct2VitConfig()
+
+    >>> # Initializing a MolmoAct2VisionTransformer (with random weights)
+    >>> model = MolmoAct2VisionTransformer(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "molmoact2"
+    base_config_key = "vit_config"
+
+    def __init__(
+        self,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        num_hidden_layers: int = 27,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int = 16,
+        head_dim: int = 72,
+        hidden_act: str = "gelu_pytorch_tanh",
+        layer_norm_eps: float = 1e-6,
+        image_default_input_size: tuple[int, int] = (378, 378),
+        image_patch_size: int = 14,
+        image_num_pos: int = 577,
+        attention_dropout: float = 0.0,
+        residual_dropout: float = 0.0,
+        initializer_range: float = 0.02,
+        float32_attention: bool = True,
+        attn_implementation: str = "eager",
+        **kwargs,
+    ):
+        self.attn_implementation = attn_implementation
+        super().__init__(attn_implementation=attn_implementation, **kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.layer_norm_eps = layer_norm_eps
+        self.image_default_input_size = image_default_input_size
+        self.image_patch_size = image_patch_size
+        self.image_num_pos = image_num_pos
+        self.attention_dropout = attention_dropout
+        self.residual_dropout = residual_dropout
+        self.initializer_range = initializer_range
+        self.float32_attention = float32_attention
+
+    @property
+    def image_num_patch(self):
+        h, w = self.image_default_input_size
+        return h // self.image_patch_size, w // self.image_patch_size
+
+
+class MolmoAct2AdapterConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of MolmoAct2Adapter. With MolmoAct2VitConfig,
+    It is used to instantiate an MolmoAct2VisionBackbone according to the specified arguments,
+    defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Example:
+
+    ```python
+    >>> from transformers import MolmoAct2VitConfig, MolmoAct2AdapterConfig, MolmoAct2VisionBackbone
+
+    >>> # Initializing a MolmoAct2VitConfig and a MolmoAct2AdapterConfig
+    >>> vit_config = MolmoAct2VitConfig()
+    >>> adapter_config = MolmoPoolingConfig()
+
+    >>> # Initializing a MolmoAct2VisionBackbone (with random weights)
+    >>> model = MolmoAct2VisionBackbone(vit_config, adapter_config)
+
+    >>> # Accessing the model configuration
+    >>> vit_configuration = model.vit_config
+    >>> adapter_configuration = model.adapter_config
+    ```"""
+
+    model_type = "molmoact2"
+    base_config_key = "adapter_config"
+
+    def __init__(
+        self,
+        vit_layers: tuple = (-3, -9),
+        pooling_attention_mask: bool = False,
+        hidden_size: int = 1152,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int = 16,
+        head_dim: int = 72,
+        float32_attention: bool = True,
+        attention_dropout: float = 0.0,
+        residual_dropout: float = 0.0,
+        hidden_act: str = "silu",
+        intermediate_size: int = 18944,
+        text_hidden_size: int = 3584,
+        image_feature_dropout: float = 0.0,
+        initializer_range: float = 0.02,
+        attn_implementation: str = "eager",
+        **kwargs,
+    ):
+        self.attn_implementation = attn_implementation
+        super().__init__(attn_implementation=attn_implementation, **kwargs)
+        self.vit_layers = vit_layers
+        self.pooling_attention_mask = pooling_attention_mask
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.float32_attention = float32_attention
+        self.attention_dropout = attention_dropout
+        self.residual_dropout = residual_dropout
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.text_hidden_size = text_hidden_size
+        self.image_feature_dropout = image_feature_dropout
+        self.initializer_range = initializer_range
+
+
+class MolmoAct2TextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MolmoAct2TextModel`]. It is used to instantiate a
+    `MolmoAct2TextModel` according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Example:
+    ```python
+    >>> from transformers import MolmoAct2TextConfig, MolmoAct2TextModel
+
+    >>> # Initializing a MolmoAct2TextConfig
+    >>> configuration = MolmoAct2TextConfig()
+
+    >>> # Initializing a MolmoAct2TextModel (with random weights)
+    >>> model = MolmoAct2TextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "molmoact2_text"
+    base_config_key = "text_config"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "blocks.*.self_attn.att_proj": "colwise",
+        "blocks.*.self_attn.attn_out": "rowwise",
+        "blocks.*.mlp.ff_proj": "colwise",
+        "blocks.*.mlp.ff_out": "rowwise",
+    }
+    base_model_pp_plan = {
+        "wte": (["input_ids"], ["inputs_embeds"]),
+        "blocks": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "ln_f": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        hidden_size: int = 3584,
+        num_attention_heads: int = 28,
+        num_key_value_heads: int | None = 4,
+        head_dim: int = 128,
+        vocab_size: int = 152064,
+        additional_vocab_size: int = 128,
+        qkv_bias: bool = True,
+        num_hidden_layers: int = 48,
+        intermediate_size: int = 18944,
+        hidden_act: str = "silu",
+        embedding_dropout: float = 0.0,
+        attention_dropout: float = 0.0,
+        residual_dropout: float = 0.0,
+        max_position_embeddings: int = 4096,
+        rope_theta: float = 1000000.0,
+        rope_scaling: dict[str, Any] = None,
+        rope_scaling_layers: list[int] | None = None,
+        use_qk_norm: bool = False,
+        qk_norm_type: str = "olmo",
+        layer_norm_eps: int = 1e-6,
+        norm_after: bool = False,
+        initializer_range: float = 0.02,
+        use_cache=True,
+        tie_word_embeddings=False,
+        attn_implementation: str = "eager",
+        **kwargs,
+    ):
+        self.attn_implementation = attn_implementation
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings, attn_implementation=attn_implementation, **kwargs
+        )
+        self.hidden_size = hidden_size
+        self.num_attention_heads = num_attention_heads
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.vocab_size = vocab_size
+        self.additional_vocab_size = additional_vocab_size
+        self.qkv_bias = qkv_bias
+        self.num_hidden_layers = num_hidden_layers
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.embedding_dropout = embedding_dropout
+        self.attention_dropout = attention_dropout
+        self.residual_dropout = residual_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.rope_scaling_layers = rope_scaling_layers
+        self.use_qk_norm = use_qk_norm
+        self.qk_norm_type = qk_norm_type
+        self.layer_norm_eps = layer_norm_eps
+        self.norm_after = norm_after
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+
+        # Validate the correctness of rotary position embeddings parameters
+        rope_config_validation(self)
+
+
+class MolmoAct2ActionExpertConfig(PretrainedConfig):
+    r"""Configuration for the MolmoAct2 modern action expert."""
+
+    model_type = "molmoact2_action_expert"
+    base_config_key = "action_expert_config"
+
+    def __init__(
+        self,
+        max_action_horizon: int = 32,
+        max_action_dim: int = 32,
+        hidden_size: int = 1024,
+        num_layers: int = 32,
+        num_heads: int = 16,
+        mlp_ratio: float = 8.0 / 3.0,
+        ffn_multiple_of: int = 256,
+        timestep_embed_dim: int = 256,
+        dropout: float = 0.0,
+        attn_dropout: float = 0.0,
+        context_layer_norm: bool = True,
+        qk_norm: bool = True,
+        qk_norm_eps: float = 1e-6,
+        rope: bool = True,
+        causal_attn: bool = False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.max_action_horizon = max_action_horizon
+        self.max_action_dim = max_action_dim
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.mlp_ratio = mlp_ratio
+        self.ffn_multiple_of = ffn_multiple_of
+        self.timestep_embed_dim = timestep_embed_dim
+        self.dropout = dropout
+        self.attn_dropout = attn_dropout
+        self.context_layer_norm = context_layer_norm
+        self.qk_norm = qk_norm
+        self.qk_norm_eps = qk_norm_eps
+        self.rope = rope
+        self.causal_attn = causal_attn
+
+    def to_dict(self):
+        output = super().to_dict()
+        # These are derived from the parent MolmoAct2Config for HF exports. Keeping
+        # them out of the public nested config avoids duplicated sources of truth.
+        output.pop("max_action_horizon", None)
+        output.pop("max_action_dim", None)
+        return output
+
+
+class MolmoAct2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MolmoAct2ForConditionalGeneration`].
+    It is used to instantiate an MolmoAct2 model according to the specified arguments, defining the model architecture.
+
+    Example:
+
+    ```python
+    >>> from transformers import MolmoAct2Config, MolmoAct2VitConfig, MolmoAct2AdapterConfig, MolmoAct2TextConfig
+
+    >>> # Initializing a MolmoAct2VitConfig
+    >>> vit_config = MolmoAct2VitConfig()
+
+    >>> # Initializing a MolmoAct2AdapterConfig
+    >>> adapter_config = MolmoAct2AdapterConfig()
+
+    >>> # Initializing a MolmoAct2TextConfig
+    >>> text_config = MolmoAct2TextConfig()
+
+    >>> # Initializing a MolmoAct2Config
+    >>> configuration = MolmoAct2Config(
+    >>>     vit_config=vit_config,
+    >>>     adapter_config=adapter_config,
+    >>>     text_config=text_config,
+    >>>     image_start_token_id=151936,
+    >>>     image_end_token_id=151937,
+    >>>     image_patch_id=151938,
+    >>>     image_col_id=151939,
+    >>>     low_res_image_start_token_id=151940,
+    >>>     image_low_res_id=151942,
+    >>>     frame_start_token_id=151943,
+    >>>     frame_end_token_id=151944,
+    >>> )
+
+    >>> # Initializing a model
+    >>> model = MolmoAct2ForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "molmoact2"
+    sub_configs = {
+        "text_config": MolmoAct2TextConfig,
+        "vit_config": MolmoAct2VitConfig,
+        "adapter_config": MolmoAct2AdapterConfig,
+        "action_expert_config": MolmoAct2ActionExpertConfig,
+    }
+
+    def __init__(
+        self,
+        vit_config: MolmoAct2VitConfig = None,
+        adapter_config: MolmoAct2AdapterConfig = None,
+        text_config: MolmoAct2TextConfig = None,
+        action_expert_config: MolmoAct2ActionExpertConfig = None,
+        image_start_token_id: int = None,
+        low_res_image_start_token_id: int = None,
+        image_end_token_id: int = None,
+        image_low_res_id: int = None,
+        image_patch_id: int = None,
+        image_col_id: int = None,
+        frame_start_token_id: int = None,
+        frame_end_token_id: int = None,
+        use_frame_special_tokens: bool = True,
+        initializer_range: float = 0.02,
+        add_action_expert: bool = True,
+        max_action_dim: int = 32,
+        max_action_horizon: int = 30,
+        n_obs_steps: int = 30,
+        action_mode: str = "both",
+        state_format: str = "discrete",
+        flow_matching_num_steps: int = 10,
+        flow_matching_cutoff: float = 1.0,
+        flow_matching_time_offset: float = 0.001,
+        flow_matching_time_scale: float = 0.999,
+        flow_matching_beta_alpha: float = 1.0,
+        flow_matching_beta_beta: float = 1.5,
+        mask_action_dim_padding: bool = True,
+        enable_depth_reasoning: bool = False,
+        depth_mode: int = 2,
+        num_depth_codes: int = 100,
+        action_expert_depth_gate: bool = False,
+        action_expert_depth_gate_per_layer: bool = False,
+        action_expert_depth_gate_init_bias: float = -4.0,
+        action_output_token_id: int = None,
+        action_start_token_id: int = None,
+        action_end_token_id: int = None,
+        action_token_start_id: int = None,
+        num_action_tokens: int = 0,
+        depth_output_token_id: int = None,
+        depth_start_token_id: int = None,
+        depth_end_token_id: int = None,
+        depth_token_start_id: int = None,
+        num_depth_tokens: int = 0,
+        state_start_token_id: int = None,
+        state_end_token_id: int = None,
+        state_token_start_id: int = None,
+        num_state_tokens: int = 0,
+        add_setup_tokens: bool = True,
+        add_control_tokens: bool = True,
+        norm_stats_filename: str = "norm_stats.json",
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if vit_config is None:
+            self.vit_config = MolmoAct2VitConfig()
+        elif isinstance(vit_config, dict):
+            self.vit_config = MolmoAct2VitConfig(**vit_config)
+        else:
+            self.vit_config = vit_config
+        if adapter_config is None:
+            self.adapter_config = MolmoAct2AdapterConfig()
+        elif isinstance(adapter_config, dict):
+            self.adapter_config = MolmoAct2AdapterConfig(**adapter_config)
+        else:
+            self.adapter_config = adapter_config
+        if text_config is None:
+            self.text_config = MolmoAct2TextConfig()
+        elif isinstance(text_config, dict):
+            self.text_config = MolmoAct2TextConfig(**text_config)
+        else:
+            self.text_config = text_config
+        self.add_action_expert = bool(add_action_expert)
+        if not self.add_action_expert:
+            self.action_expert_config = None
+        elif action_expert_config is None:
+            self.action_expert_config = MolmoAct2ActionExpertConfig(
+                max_action_horizon=max_action_horizon,
+                max_action_dim=max_action_dim,
+                num_layers=self.text_config.num_hidden_layers,
+            )
+        elif isinstance(action_expert_config, dict):
+            self.action_expert_config = MolmoAct2ActionExpertConfig(**action_expert_config)
+        else:
+            self.action_expert_config = action_expert_config
+        if self.add_action_expert:
+            self.action_expert_config.max_action_dim = int(max_action_dim)
+            self.action_expert_config.max_action_horizon = int(max_action_horizon)
+            self._validate_release_action_config(
+                state_format=state_format,
+            )
+        self.image_start_token_id = image_start_token_id
+        self.low_res_image_start_token_id = low_res_image_start_token_id
+        self.image_end_token_id = image_end_token_id
+        self.image_low_res_id = image_low_res_id
+        self.image_high_res_id = image_patch_id
+        self.image_patch_id = image_patch_id
+        self.image_col_id = image_col_id
+        self.frame_start_token_id = frame_start_token_id
+        self.frame_end_token_id = frame_end_token_id
+        self.use_frame_special_tokens = use_frame_special_tokens
+        self.initializer_range = initializer_range
+        self.max_action_dim = max_action_dim
+        self.max_action_horizon = max_action_horizon
+        self.n_obs_steps = n_obs_steps
+        self.action_mode = action_mode
+        self.state_format = state_format
+        self.flow_matching_num_steps = flow_matching_num_steps
+        self.flow_matching_cutoff = flow_matching_cutoff
+        self.flow_matching_time_offset = flow_matching_time_offset
+        self.flow_matching_time_scale = flow_matching_time_scale
+        self.flow_matching_beta_alpha = flow_matching_beta_alpha
+        self.flow_matching_beta_beta = flow_matching_beta_beta
+        self.mask_action_dim_padding = mask_action_dim_padding
+        self.enable_depth_reasoning = enable_depth_reasoning
+        self.depth_mode = depth_mode
+        self.num_depth_codes = num_depth_codes
+        self.action_expert_depth_gate = action_expert_depth_gate
+        self.action_expert_depth_gate_per_layer = action_expert_depth_gate_per_layer
+        self.action_expert_depth_gate_init_bias = action_expert_depth_gate_init_bias
+        self.action_output_token_id = action_output_token_id
+        self.action_start_token_id = action_start_token_id
+        self.action_end_token_id = action_end_token_id
+        self.action_token_start_id = action_token_start_id
+        self.num_action_tokens = num_action_tokens
+        self.depth_output_token_id = depth_output_token_id
+        self.depth_start_token_id = depth_start_token_id
+        self.depth_end_token_id = depth_end_token_id
+        self.depth_token_start_id = depth_token_start_id
+        self.num_depth_tokens = num_depth_tokens
+        self.state_start_token_id = state_start_token_id
+        self.state_end_token_id = state_end_token_id
+        self.state_token_start_id = state_token_start_id
+        self.num_state_tokens = num_state_tokens
+        self.add_setup_tokens = add_setup_tokens
+        self.add_control_tokens = add_control_tokens
+        self.norm_stats_filename = norm_stats_filename
+
+    @staticmethod
+    def _validate_release_action_config(
+        *,
+        state_format: str,
+    ) -> None:
+        if state_format != "discrete":
+            raise ValueError("MolmoAct2 HF export supports only state_format='discrete'.")
+
+    @property
+    def image_num_patch(self):
+        assert self.vit_config is not None
+        return self.vit_config.image_num_patch
+
+    @property
+    def num_attention_heads(self):
+        return self.text_config.num_attention_heads
+
+    @property
+    def num_key_value_heads(self):
+        return self.text_config.num_key_value_heads
+
+    @property
+    def head_dim(self):
+        return self.text_config.head_dim
+
+    @property
+    def num_hidden_layers(self):
+        return self.text_config.num_hidden_layers
+
+    @property
+    def hidden_size(self):
+        return self.text_config.hidden_size
+
+    @property
+    def vocab_size(self):
+        return self.text_config.vocab_size
+
+    @property
+    def max_position_embeddings(self):
+        return self.text_config.max_position_embeddings
+
+
+MolmoAct2VitConfig.register_for_auto_class()
+MolmoAct2AdapterConfig.register_for_auto_class()
+MolmoAct2TextConfig.register_for_auto_class()
+MolmoAct2ActionExpertConfig.register_for_auto_class()
+MolmoAct2Config.register_for_auto_class()
@@ -0,0 +1,564 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+"""Image processor class for MolmoAct2"""
+
+from typing import Optional, Union
+import numpy as np
+import einops
+import torch
+import torchvision.transforms
+
+from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ImageInput,
+    PILImageResampling,
+    make_flat_list_of_images,
+    valid_images,
+    to_numpy_array,
+)
+from transformers.image_transforms import convert_to_rgb
+from transformers.processing_utils import ImagesKwargs
+from transformers.image_processing_utils import BaseImageProcessor, get_size_dict
+from transformers.utils import logging
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.utils import TensorType, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def normalize_image(
+    image: np.ndarray,
+    image_mean: list[float],
+    image_std: list[float],
+) -> np.ndarray:
+    if np.allclose(image_mean, [0.5, 0.5, 0.5]) and np.allclose(image_std, [0.5, 0.5, 0.5]):
+        return image * np.asarray(2.0, dtype=np.float32) - np.asarray(1.0, dtype=np.float32)
+    image -= np.array(image_mean, dtype=np.float32)[None, None, :]
+    image /= np.array(image_std, dtype=np.float32)[None, None, :]
+    return image
+
+
+def resize_image(
+    image: np.ndarray,
+    desired_output_size: list[int],
+    resample: PILImageResampling,
+) -> np.ndarray:
+    image = torch.permute(torch.from_numpy(image), [2, 0, 1])
+    dtype = image.dtype
+    if torch.is_floating_point(image):
+        in_min = 0.0
+        in_max = 1.0
+        resized = torchvision.transforms.Resize(
+            desired_output_size,
+            resample,
+            antialias=False,
+        )(image)
+        resized = torch.clip(resized, 0.0, 1.0).to(dtype)
+    else:
+        assert image.dtype == torch.uint8, "SigLIP expects float images or uint8 images, but got {}".format(
+            image.dtype
+        )
+        in_min = 0.0
+        in_max = 255.0
+        resized = torchvision.transforms.Resize(
+            desired_output_size,
+            resample,
+            antialias=False,
+        )(image)
+        resized = torch.clip(resized, 0, 255).to(dtype)
+
+    resized = resized.to(torch.float32)
+    resized = (resized - in_min) / (in_max - in_min)
+
+    resized = torch.permute(resized, [1, 2, 0]).numpy()
+
+    return resized
+
+
+def select_tiling(h, w, patch_size, max_num_crops):
+    """Divide in image of size [w, h] in up to max_num_patches of size patch_size"""
+    original_size = np.stack([h, w])  # [1, 2]
+    original_res = h * w
+    tilings = []
+    for i in range(1, max_num_crops + 1):
+        for j in range(1, max_num_crops + 1):
+            if i * j <= max_num_crops:
+                tilings.append((i, j))
+    # sort so argmin and argmax favour smaller tilings in the event of a tie
+    tilings.sort(key=lambda x: (x[0] * x[1], x[0]))
+    candidate_tilings = np.array(tilings, dtype=np.int32)  # [n_resolutions, 2]
+    candidate_resolutions = candidate_tilings * patch_size  # [n_resolutions, 2]
+
+    # How much we would need to scale the image to fit exactly in each tiling
+    original_size = np.stack([h, w], dtype=np.float32)  # [1, 2]
+
+    # The original size can be zero in rare cases if the image is smaller than the margin
+    # In those cases letting the scale become infinite means the tiling is based on the
+    # other side, or falls back to the smallest tiling
+    with np.errstate(divide="ignore"):
+        required_scale_d = (candidate_resolutions.astype(np.float32) / original_size,)
+    required_scale = np.min(required_scale_d, axis=-1, keepdims=True)  # [n_resolutions, 1]
+    if np.all(required_scale < 1):
+        # We are forced to downscale, so try to minimize the amount of downscaling
+        ix = np.argmax(required_scale)
+    else:
+        # Pick the resolution that required the least upscaling so that it most closely fits the image
+        required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
+        ix = np.argmin(required_scale)
+    return candidate_tilings[ix]
+
+
+def build_resized_image(
+    image: np.ndarray,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    resized = resize_image(
+        image,
+        base_image_input_size,
+        resample,
+    )
+    resized = normalize_image(resized, image_mean, image_std)
+    if len(resized.shape) == 3:
+        resized = np.expand_dims(resized, 0)
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    resize_idx = np.arange(crop_patch_w * crop_patch_h).reshape([crop_patch_h, crop_patch_w])
+    return resized, resize_idx
+
+
+def build_overlapping_crops(
+    image: np.ndarray,
+    max_crops: int,
+    overlap_margins: list[int],
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Decompose an image into a set of overlapping crops
+
+    :return crop_arr: [n_crops, h, w, 3] The crops
+    :return patch_idx: [overlap_patch_h, overlap_patch_w] For each patch in the resized image
+                        the crops were extracted from, what patch in `crop_arr` it corresponds to
+    """
+    original_image_h, original_image_w = image.shape[:2]
+    crop_size = base_image_input_size[0]
+    assert base_image_input_size[0] == base_image_input_size[1]
+
+    left_margin, right_margin = overlap_margins
+    total_margin_pixels = image_patch_size * (right_margin + left_margin)  # pixels removed per dim
+    crop_patches = base_image_input_size[0] // image_patch_size  # patches per crop dim
+    crop_window_patches = crop_patches - (right_margin + left_margin)  # usable patches
+    crop_window_size = crop_window_patches * image_patch_size
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    original_image_h, original_image_w = image.shape[:2]
+    crop_size = base_image_input_size[0]
+
+    # Decide how to tile the image, to account for the overlap margins we compute the tiling
+    # as if we had an image without the margins and were using a crop size without the margins
+    tiling = select_tiling(
+        original_image_h - total_margin_pixels,
+        original_image_w - total_margin_pixels,
+        crop_window_size,
+        max_crops,
+    )
+
+    src = resize_image(
+        image,
+        [
+            tiling[0] * crop_window_size + total_margin_pixels,
+            tiling[1] * crop_window_size + total_margin_pixels,
+        ],
+        resample,
+    )
+    src = normalize_image(src, image_mean, image_std)
+
+    # Now we have to split the image into crops, and track what patches came from
+    # where in `patch_idx_arr`
+    n_crops = tiling[0] * tiling[1]
+    crop_arr = np.zeros([n_crops, crop_size, crop_size, 3], dtype=src.dtype)
+    patch_idx_arr = np.zeros([n_crops, crop_patch_h, crop_patch_w], dtype=np.int32)
+    on_crop = 0
+    for i in range(tiling[0]):
+        # Slide over `src` by `crop_window_size` steps, but extract crops of size `crops_size`
+        # which results in overlapping crop windows
+        y0 = i * crop_window_size
+        for j in range(tiling[1]):
+            x0 = j * crop_window_size
+            crop_arr[on_crop] = src[y0 : y0 + crop_size, x0 : x0 + crop_size]
+            patch_idx = np.arange(crop_patch_w * crop_patch_h).reshape(crop_patch_h, crop_patch_w)
+            patch_idx += on_crop * crop_patch_h * crop_patch_w
+
+            # Mask out idx that are in the overlap region
+            if i != 0:
+                patch_idx[:left_margin, :] = -1
+            if j != 0:
+                patch_idx[:, :left_margin] = -1
+            if i != tiling[0] - 1:
+                patch_idx[-right_margin:, :] = -1
+            if j != tiling[1] - 1:
+                patch_idx[:, -right_margin:] = -1
+            patch_idx_arr[on_crop] = patch_idx
+            on_crop += 1
+
+    # `patch_idx_arr` is ordered crop-by-crop, here we transpose `patch_idx_arr`
+    # so it is ordered left-to-right order
+    patch_idx_arr = np.reshape(patch_idx_arr, [tiling[0], tiling[1], crop_patch_h, crop_patch_w])
+    patch_idx_arr = np.transpose(patch_idx_arr, [0, 2, 1, 3])
+    patch_idx_arr = np.reshape(patch_idx_arr, [-1])
+
+    # Now get the parts not in the overlap region, so it should map each patch in `src`
+    # to the correct patch it should come from in `crop_arr`
+    patch_idx_arr = patch_idx_arr[patch_idx_arr >= 0].reshape(
+        src.shape[0] // image_patch_size,
+        src.shape[1] // image_patch_size,
+    )
+    return crop_arr, patch_idx_arr
+
+
+def batch_pixels_to_patches(array: np.ndarray, patch_size: int) -> np.ndarray:
+    """Reshape images of [n_images, h, w, 3] -> [n_images, n_patches, pixels_per_patch]"""
+    if len(array.shape) == 3:
+        n_crops, h, w = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size])
+        array = np.transpose(array, [0, 1, 3, 2, 4])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size])
+        return array
+    else:
+        n_crops, h, w, c = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size, c])
+        array = np.transpose(array, [0, 1, 3, 2, 4, 5])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size * c])
+        return array
+
+
+def arange_for_pooling(
+    idx_arr: np.ndarray,
+    pool_h: int,
+    pool_w: int,
+) -> np.ndarray:
+    h_pad = pool_h * ((idx_arr.shape[0] + pool_h - 1) // pool_h) - idx_arr.shape[0]
+    w_pad = pool_w * ((idx_arr.shape[1] + pool_w - 1) // pool_w) - idx_arr.shape[1]
+    idx_arr = np.pad(
+        idx_arr,
+        [[h_pad // 2, (h_pad + 1) // 2], [w_pad // 2, (w_pad + 1) // 2]],
+        mode="constant",
+        constant_values=-1,
+    )
+    return einops.rearrange(idx_arr, "(h dh) (w dw) -> h w (dh dw)", dh=pool_h, dw=pool_w)
+
+
+def image_to_patches_and_grids(
+    image: np.ndarray,
+    max_crops: int,
+    overlap_margins: list[int],
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+    image_pooling_w: int,
+    image_pooling_h: int,
+    crop_mode: str = "overlap-and-resize-c2",
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    :return image_grids, the shape of each (low-res, high-res) image after pooling
+    :return crops, the image crops to processes with the ViT
+    :return pooled_patch_idx, for each patch_id tokens in `image_tokens`, the indices of the
+                                patches in `crops` to pool for that token, masked with -1
+    """
+    if isinstance(base_image_input_size, int):
+        base_image_input_size = (base_image_input_size, base_image_input_size)
+
+    base_image_input_d = image_patch_size
+    pooling_w = image_pooling_w
+    pooling_h = image_pooling_h
+    crop_patch_w = base_image_input_size[1] // base_image_input_d
+    crop_patch_h = base_image_input_size[0] // base_image_input_d
+
+    if crop_mode == "resize":
+        resized, resize_idx = build_resized_image(
+            image,
+            base_image_input_size,
+            resample,
+            image_mean,
+            image_std,
+            image_patch_size,
+        )
+        resize_idx = arange_for_pooling(resize_idx, pooling_h, pooling_w)
+        resized_h, resized_w = resize_idx.shape[:2]
+        resize_idx = resize_idx.reshape([-1, pooling_h * pooling_w])
+        image_grid = [np.array([resized_h, resized_w, 0, 0])]
+        return (
+            np.stack(image_grid, 0),
+            batch_pixels_to_patches(resized, image_patch_size),
+            resize_idx,
+        )
+
+    if crop_mode not in {"overlap-and-resize-c2", "overlap-and-resize"}:
+        raise ValueError(f"Unsupported MolmoAct2 image crop_mode {crop_mode!r}.")
+
+    crop_arr, patch_idx_arr = build_overlapping_crops(
+        image,
+        max_crops,
+        overlap_margins,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    pooling_idx = arange_for_pooling(patch_idx_arr, pooling_h, pooling_w)
+    h, w = pooling_idx.shape[:2]
+    pooling_idx = pooling_idx.reshape([-1, pooling_h * pooling_w])
+
+    # Finally do the same for the global image
+    resized, resize_idx = build_resized_image(
+        image,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    crop_arr = np.concatenate([resized, crop_arr], 0)
+
+    resize_idx = arange_for_pooling(resize_idx, pooling_h, pooling_w)
+    resized_h, resized_w = resize_idx.shape[:2]
+    resize_idx = resize_idx.reshape([-1, pooling_h * pooling_w])
+
+    # Global image goes first, so the order of patches in previous crops gets increased
+    pooling_idx = np.where(pooling_idx >= 0, pooling_idx + crop_patch_h * crop_patch_w, -1)
+    pooling_idx = np.concatenate([resize_idx, pooling_idx])
+    image_grid = [np.array([resized_h, resized_w, h, w])]
+
+    return (np.stack(image_grid, 0), batch_pixels_to_patches(crop_arr, image_patch_size), pooling_idx)
+
+
+class MolmoAct2ImagesKwargs(ImagesKwargs, total=False):
+    max_crops: int | None
+    overlap_margins: list[int] | None
+    crop_mode: str | None
+    patch_size: int | None
+    pooling_size: list[int] | None
+
+
+class MolmoAct2ImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a MolmoAct2 image processor that preprocesses images for the model.
+
+    Args:
+        size (`dict[str, int]` *optional*, defaults to `{"height": 378, "width": 378}`):
+            Size of the image after resizing.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
+            Resampling filter to use when resizing the image.
+        image_mean (`float` or `list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        image_std (`float` or `list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+        max_crops (`int`, *optional*, defaults to `8`):
+            Maximum number of crops to use per image.
+        overlap_margins (`list[int]`, *optional*, defaults to `[4, 4]`):
+            Overlap margins to use.
+        patch_size (`int`, *optional*, defaults to 14):
+            The spatial patch size of the vision encoder.
+        pooling_size (`list[int]`, *optional*, defaults to `[2, 2]`):
+            The pooling size of the vision adapter.
+    """
+
+    model_input_names = ["pixel_values", "image_token_pooling", "image_grids", "image_num_crops"]
+
+    def __init__(
+        self,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling = PILImageResampling.BILINEAR,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool = True,
+        max_crops: int = 8,
+        overlap_margins: list[int] = [4, 4],
+        crop_mode: str = "overlap-and-resize-c2",
+        patch_size: int = 14,
+        pooling_size: list[int] = [2, 2],
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 378, "width": 378}
+        size = get_size_dict(size, default_to_square=True)
+        self.size = size
+
+        self.resample = resample
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+        self.do_convert_rgb = do_convert_rgb
+
+        self.max_crops = max_crops
+        self.overlap_margins = overlap_margins
+        self.crop_mode = crop_mode
+        self.patch_size = patch_size
+        self.pooling_size = pooling_size
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool | None = None,
+        max_crops: int | None = None,
+        overlap_margins: list[int] | None = None,
+        crop_mode: str | None = None,
+        patch_size: int | None = None,
+        pooling_size: list[int] | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess.
+            size (`dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use when resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            max_crops (`int`, *optional*, defaults to `self.max_crops`):
+                Maximum number of crops to use per image.
+            overlap_margins (`list[int]`, *optional*, defaults to `self.overlap_margins`):
+                Overlap margins to use.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            pooling_size (`list[int]`, *optional*, defaults to `self.pooling_size`):
+                The pooling size of the vision adapter.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+
+        Returns:
+            A `BatchFeature` containing the following keys:
+                - `pixel_values`: The preprocessed images.
+                - `image_token_pooling`: The indices of the patches in `crops` to pool for each token in `image_tokens`.
+                - `image_grids`: The image grids.
+                - `image_num_crops`: The number of crops for each image.
+        """
+        if size is not None:
+            if "height" not in size or "width" not in size:
+                raise ValueError("size must contain 'height' and 'width' keys.")
+        else:
+            size = {**self.size}
+
+        base_image_input_size = [size["height"], size["width"]]
+
+        resample = resample or self.resample
+        image_mean = image_mean or self.image_mean
+        image_std = image_std or self.image_std
+        do_convert_rgb = do_convert_rgb or self.do_convert_rgb
+
+        max_crops = max_crops or self.max_crops
+        overlap_margins = overlap_margins or self.overlap_margins
+        crop_mode = crop_mode or self.crop_mode
+        patch_size = patch_size or self.patch_size
+        pooling_size = pooling_size or self.pooling_size
+
+        image_pooling_h, image_pooling_w = pooling_size
+
+        if images is not None:
+            images = self.fetch_images(images)
+            images = make_flat_list_of_images(images)
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        data = {}
+        if images is not None:
+            batch_grids = []
+            batch_crops = []
+            batch_pooled_patches_idx = []
+            batch_num_crops = []
+
+            for image in images:
+                image_grid, crops, pooled_idx = image_to_patches_and_grids(
+                    image,
+                    max_crops,
+                    overlap_margins,
+                    base_image_input_size,
+                    resample,
+                    image_mean,
+                    image_std,
+                    patch_size,
+                    image_pooling_w,
+                    image_pooling_h,
+                    crop_mode,
+                )
+                batch_grids.append(image_grid)
+                batch_crops.append(crops)
+                batch_pooled_patches_idx.append(pooled_idx)
+                batch_num_crops.append(crops.shape[0])
+
+            pixel_values = np.concatenate(batch_crops, 0)
+            image_token_pooling = np.concatenate(batch_pooled_patches_idx, 0)
+            image_grids = np.concatenate(batch_grids, 0)
+            image_num_crops = np.array(batch_num_crops)
+
+            data.update(
+                pixel_values=pixel_values,
+                image_token_pooling=image_token_pooling,
+                image_grids=image_grids,
+                image_num_crops=image_num_crops,
+            )
+
+        return BatchFeature(data, tensor_type=return_tensors)
+
+
+MolmoAct2ImageProcessor.register_for_auto_class()
@@ -0,0 +1,748 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+"""Inference utilities for MolmoAct2"""
+
+from dataclasses import dataclass
+from typing import Any, Optional, Tuple
+from collections.abc import Iterable, Sequence
+
+import torch
+from torch.nn import functional as F
+from transformers.cache_utils import Cache
+from transformers.configuration_utils import PretrainedConfig
+
+
+@dataclass
+class _ActionFlowInputs:
+    trajectory: torch.Tensor
+    context: Any
+    modulations: Sequence[Any]
+    action_dim_is_pad: torch.Tensor | None
+
+
+@dataclass
+class _ActionFlowCudaGraph:
+    key: tuple[Any, ...]
+    graph: torch.cuda.CUDAGraph
+    static_inputs: _ActionFlowInputs
+    output: torch.Tensor
+
+
+@dataclass
+class _DepthDecodeCudaGraphLayerStage:
+    residual: torch.Tensor
+    query: torch.Tensor
+    key: torch.Tensor
+    value: torch.Tensor
+
+
+@dataclass
+class _DepthDecodeCudaGraphPostStage:
+    graph: torch.cuda.CUDAGraph
+    attn_context: torch.Tensor
+
+
+@dataclass
+class _DepthDecodeCudaGraph:
+    cache_key: tuple[Any, ...]
+    pre_graph: torch.cuda.CUDAGraph
+    token_ids: torch.Tensor
+    cos: torch.Tensor
+    sin: torch.Tensor
+    positions: torch.Tensor
+    stages: Sequence[_DepthDecodeCudaGraphLayerStage]
+    post_graphs: Sequence[_DepthDecodeCudaGraphPostStage]
+    output: torch.Tensor
+
+
+@dataclass
+class _DepthDecodeCudaGraphSpec:
+    eligible: bool
+    cache_key_prefix: tuple[Any, ...]
+    num_hidden_layers: int
+    head_dim: int
+    num_attention_heads: int
+
+
+def _cache_seq_len_int(past_key_values: Cache | None) -> int:
+    if past_key_values is None:
+        return 0
+    seq_len = past_key_values.get_seq_length()
+    if torch.is_tensor(seq_len):
+        return int(seq_len.item())
+    return int(seq_len)
+
+
+def _cache_max_len_int(past_key_values: Cache | None) -> int:
+    if past_key_values is None:
+        return -1
+    max_len = past_key_values.get_max_cache_shape()
+    if torch.is_tensor(max_len):
+        return int(max_len.item())
+    return int(max_len)
+
+
+def _iter_cache_key_values(
+    past_key_values: Cache,
+) -> Iterable[tuple[torch.Tensor | None, torch.Tensor | None]]:
+    layers = getattr(past_key_values, "layers", None)
+    if layers is not None:
+        for layer in layers:
+            yield getattr(layer, "keys", None), getattr(layer, "values", None)
+        return
+    for layer in past_key_values:
+        yield layer[0], layer[1]
+
+
+class _DepthDecodeStaticLayerCache:
+    is_compileable = False
+    is_sliding = False
+
+    def __init__(self, max_cache_len: int) -> None:
+        self.max_cache_len = int(max_cache_len)
+        self.cumulative_length = 0
+        self.keys: torch.Tensor | None = None
+        self.values: torch.Tensor | None = None
+
+    def _allocate(self, key_states: torch.Tensor, value_states: torch.Tensor) -> None:
+        bsz, n_heads = key_states.shape[:2]
+        self.keys = torch.empty(
+            (bsz, n_heads, self.max_cache_len, key_states.shape[-1]),
+            dtype=key_states.dtype,
+            device=key_states.device,
+        )
+        self.values = torch.empty(
+            (bsz, n_heads, self.max_cache_len, value_states.shape[-1]),
+            dtype=value_states.dtype,
+            device=value_states.device,
+        )
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        *args,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if self.keys is None:
+            self._allocate(key_states, value_states)
+        start = self.cumulative_length
+        end = start + key_states.shape[-2]
+        if end > self.max_cache_len:
+            raise RuntimeError(f"KV cache length {end} exceeds max_cache_len={self.max_cache_len}.")
+        self.keys[:, :, start:end, :].copy_(key_states)
+        self.values[:, :, start:end, :].copy_(value_states)
+        self.cumulative_length = end
+        return self.keys[:, :, :end, :], self.values[:, :, :end, :]
+
+    def get_seq_length(self) -> int:
+        return self.cumulative_length
+
+    def get_max_cache_shape(self) -> int:
+        return -1
+
+    def reset(self) -> None:
+        self.cumulative_length = 0
+
+
+class _DepthDecodeStaticCache(Cache):
+    def __init__(self, config: PretrainedConfig, max_cache_len: int) -> None:
+        text_config = config.get_text_config(decoder=True)
+        super().__init__(
+            layers=[
+                _DepthDecodeStaticLayerCache(max_cache_len=max_cache_len)
+                for _ in range(text_config.num_hidden_layers)
+            ]
+        )
+
+    def get_seq_length(self, layer_idx: int = 0) -> int:
+        return self.layers[layer_idx].get_seq_length()
+
+    def get_max_cache_shape(self, layer_idx: int = 0) -> int:
+        return self.layers[layer_idx].get_max_cache_shape()
+
+    def reset(self) -> None:
+        for layer in self.layers:
+            layer.reset()
+
+
+class ActionCudaGraphManager:
+    def __init__(self, model: Any) -> None:
+        self.model = model
+        self.enabled = True
+        self.action_flow_graph: _ActionFlowCudaGraph | None = None
+
+    def set_enabled(self, enabled: bool) -> None:
+        self.enabled = bool(enabled)
+
+    def can_use_action_flow(self, inputs: _ActionFlowInputs) -> bool:
+        action_model = self.model
+        if not self.enabled:
+            return False
+        if action_model.training or action_model._require_action_expert().training:
+            return False
+        if inputs.trajectory.device.type != "cuda":
+            return False
+
+        def all_on_cuda():
+            yield inputs.trajectory
+            for k, v in inputs.context.kv_contexts:
+                yield k
+                yield v
+            for t in (
+                inputs.context.cross_mask,
+                inputs.context.self_mask,
+                inputs.context.valid_action,
+                inputs.action_dim_is_pad,
+            ):
+                if t is not None:
+                    yield t
+            if inputs.context.rope_cache is not None:
+                yield from inputs.context.rope_cache
+            for step in inputs.modulations:
+                yield step.conditioning
+                for block_modulation in step.block_modulations:
+                    yield from block_modulation
+                yield from step.final_modulation
+
+        return all(t.device.type == "cuda" for t in all_on_cuda())
+
+    def run_action_flow(
+        self,
+        inputs: _ActionFlowInputs,
+        steps: int,
+        run_loop,
+    ) -> torch.Tensor:
+        key = _cuda_graph_key(inputs, steps)
+        cache = self.action_flow_graph
+        if cache is None or cache.key != key:
+            static_inputs = _clone_static_inputs(inputs)
+            graph, output = _capture_cuda_graph(
+                lambda: run_loop(static_inputs, steps),
+                inputs.trajectory.device,
+                after_warmup=lambda: static_inputs.trajectory.copy_(inputs.trajectory),
+            )
+            cache = _ActionFlowCudaGraph(
+                key=key,
+                graph=graph,
+                static_inputs=static_inputs,
+                output=output,
+            )
+            self.action_flow_graph = cache
+        else:
+            _copy_inputs_(cache.static_inputs, inputs)
+
+        cache.graph.replay()
+        return cache.output.clone()
+
+
+class DepthDecodeCudaGraphManager:
+    def __init__(self, model: Any) -> None:
+        self.model = model
+        self.backbone = model.model
+        self.enabled = True
+        self.graph: _DepthDecodeCudaGraph | None = None
+        self.graph_spec: _DepthDecodeCudaGraphSpec | None = None
+
+    def set_enabled(self, enabled: bool) -> None:
+        self.enabled = bool(enabled)
+
+    def make_static_cache(self, max_cache_len: int) -> _DepthDecodeStaticCache:
+        return _DepthDecodeStaticCache(
+            config=self.model.config.text_config,
+            max_cache_len=max_cache_len,
+        )
+
+    def _depth_decode_spec(self) -> _DepthDecodeCudaGraphSpec:
+        static = self.graph_spec
+        if static is None:
+            cfg = self.backbone.transformer.config
+            rotary_emb = getattr(self.backbone.transformer, "rotary_emb", None)
+            static = _DepthDecodeCudaGraphSpec(
+                eligible=(
+                    not cfg.norm_after
+                    and cfg.rope_scaling_layers is None
+                    and getattr(rotary_emb, "rope_type", None) == "default"
+                    and cfg._attn_implementation == "sdpa"
+                ),
+                cache_key_prefix=(
+                    cfg.hidden_size,
+                    cfg.num_attention_heads,
+                    cfg.num_key_value_heads,
+                    cfg.head_dim,
+                    cfg.num_hidden_layers,
+                    cfg.use_qk_norm,
+                    cfg.qk_norm_type,
+                    cfg._attn_implementation,
+                ),
+                num_hidden_layers=cfg.num_hidden_layers,
+                head_dim=cfg.head_dim,
+                num_attention_heads=cfg.num_attention_heads,
+            )
+            self.graph_spec = static
+        return static
+
+    def can_use(
+        self,
+        next_input_ids: torch.Tensor,
+        *,
+        past_key_values: Cache,
+        attention_bias: torch.Tensor,
+    ) -> bool:
+        if not self.enabled or self.model.training or self.backbone.transformer.training:
+            return False
+        if next_input_ids.device.type != "cuda":
+            return False
+        if next_input_ids.ndim != 2 or next_input_ids.shape[0] != 1 or next_input_ids.shape[1] != 1:
+            return False
+        if not isinstance(past_key_values, _DepthDecodeStaticCache):
+            return False
+        if not torch.is_tensor(attention_bias) or attention_bias.device != next_input_ids.device:
+            return False
+        return self._depth_decode_spec().eligible
+
+    def _depth_decode_key(
+        self,
+        next_input_ids: torch.Tensor,
+        attention_bias: torch.Tensor,
+    ) -> tuple[Any, ...]:
+        device = next_input_ids.device
+        return (
+            self._depth_decode_spec().cache_key_prefix,
+            device.type,
+            device.index,
+            self.model.lm_head.weight.dtype,
+            attention_bias.shape[-1],
+        )
+
+    def _select_depth_decode_rope(self, cos: torch.Tensor, sin: torch.Tensor, *, past_length: int) -> None:
+        emb = self.backbone.transformer.rotary_emb
+        cos.copy_(emb._pos_cos_cache[0, :, past_length : past_length + 1, :])
+        sin.copy_(emb._pos_sin_cache[0, :, past_length : past_length + 1, :])
+
+    def _depth_decode_pre_layer(
+        self,
+        layer_idx: int,
+        hidden_states: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        block = self.backbone.transformer.blocks[layer_idx]
+        attention = block.self_attn
+        residual = hidden_states
+        hidden_states = block.attn_norm(hidden_states)
+
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, attention.head_dim)
+        qkv = attention.att_proj(hidden_states)
+        query_states, key_states, value_states = qkv.split(attention.fused_dims, dim=-1)
+        value_states = value_states.view(hidden_shape)
+
+        apply_qk_norm = attention.q_norm is not None and attention.k_norm is not None
+        norm_after_view = apply_qk_norm and attention.qk_norm_type == "qwen3"
+
+        if apply_qk_norm and not norm_after_view:
+            query_states = attention.q_norm(query_states)
+            key_states = attention.k_norm(key_states)
+
+        query_states = query_states.view(hidden_shape)
+        key_states = key_states.view(hidden_shape)
+
+        if norm_after_view:
+            query_states = attention.q_norm(query_states)
+            key_states = attention.k_norm(key_states)
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        query_states, key_states = _apply_rotary_pos_emb(query_states, key_states, cos, sin)
+        return residual, query_states, key_states, value_states
+
+    def _depth_decode_pre0(
+        self,
+        token_ids: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        inputs_embeds = self.model._embed_base_tokens(token_ids)
+        return self._depth_decode_pre_layer(0, inputs_embeds, cos, sin)
+
+    def _depth_decode_post_layer(
+        self,
+        layer_idx: int,
+        residual: torch.Tensor,
+        attn_context: torch.Tensor,
+    ) -> torch.Tensor:
+        block = self.backbone.transformer.blocks[layer_idx]
+        attention = block.self_attn
+        input_shape = residual.shape[:-1]
+        attn_output = attn_context.reshape(*input_shape, -1).contiguous()
+        attn_output = attention.attn_out(attn_output)
+        hidden_states = residual + block.dropout(attn_output)
+
+        residual = hidden_states
+        hidden_states = block.ff_norm(hidden_states)
+        hidden_states = block.mlp(hidden_states)
+        hidden_states = residual + block.dropout(hidden_states)
+        return hidden_states
+
+    def _depth_decode_post_and_pre_next(
+        self,
+        layer_idx: int,
+        residual: torch.Tensor,
+        attn_context: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        hidden_states = self._depth_decode_post_layer(layer_idx, residual, attn_context)
+        return self._depth_decode_pre_layer(layer_idx + 1, hidden_states, cos, sin)
+
+    def _depth_decode_last_post(
+        self,
+        layer_idx: int,
+        residual: torch.Tensor,
+        attn_context: torch.Tensor,
+    ) -> torch.Tensor:
+        hidden_states = self._depth_decode_post_layer(layer_idx, residual, attn_context)
+        return self.backbone.transformer.ln_f(hidden_states)
+
+    def _build_depth_decode_graph(
+        self,
+        next_input_ids: torch.Tensor,
+        *,
+        past_length: int,
+        attention_bias: torch.Tensor,
+    ) -> _DepthDecodeCudaGraph:
+        text_config = self.backbone.transformer.config
+        device = next_input_ids.device
+        dtype = self.model.lm_head.weight.dtype
+        static = self._depth_decode_spec()
+        num_layers = static.num_hidden_layers
+        head_dim = static.head_dim
+        max_cache_len = int(attention_bias.shape[-1])
+        max_rope_len = max(int(text_config.max_position_embeddings or 0), max_cache_len)
+        self.backbone.transformer.prepare_rope_cache(device=device, max_seq_len=max_rope_len)
+
+        token_ids = torch.empty((1, 1), device=device, dtype=torch.long)
+        cos = torch.empty((1, 1, head_dim), device=device, dtype=dtype)
+        sin = torch.empty_like(cos)
+        positions = torch.arange(max_cache_len, device=device, dtype=torch.long)
+        context_shape = (1, 1, static.num_attention_heads, head_dim)
+
+        token_ids.copy_(next_input_ids)
+        self._select_depth_decode_rope(cos, sin, past_length=past_length)
+
+        pre_graph, pre_output = _capture_cuda_graph(
+            lambda: self._depth_decode_pre0(token_ids, cos, sin),
+            device,
+        )
+        stages = [_DepthDecodeCudaGraphLayerStage(*pre_output)]
+        post_graphs = []
+        for layer_idx in range(num_layers - 1):
+            stage = stages[-1]
+            attn_context = torch.empty(context_shape, device=device, dtype=dtype)
+            graph, output = _capture_cuda_graph(
+                lambda layer_idx=layer_idx, stage=stage, attn_context=attn_context: (
+                    self._depth_decode_post_and_pre_next(
+                        layer_idx,
+                        stage.residual,
+                        attn_context,
+                        cos,
+                        sin,
+                    )
+                ),
+                device,
+            )
+            post_graphs.append(_DepthDecodeCudaGraphPostStage(graph=graph, attn_context=attn_context))
+            stages.append(_DepthDecodeCudaGraphLayerStage(*output))
+
+        last_stage = stages[-1]
+        last_attn_context = torch.empty(context_shape, device=device, dtype=dtype)
+        last_graph, last_output = _capture_cuda_graph(
+            lambda: self._depth_decode_last_post(
+                num_layers - 1,
+                last_stage.residual,
+                last_attn_context,
+            ),
+            device,
+        )
+        post_graphs.append(_DepthDecodeCudaGraphPostStage(graph=last_graph, attn_context=last_attn_context))
+        return _DepthDecodeCudaGraph(
+            cache_key=self._depth_decode_key(next_input_ids, attention_bias),
+            pre_graph=pre_graph,
+            token_ids=token_ids,
+            cos=cos,
+            sin=sin,
+            positions=positions,
+            stages=tuple(stages),
+            post_graphs=tuple(post_graphs),
+            output=last_output,
+        )
+
+    def _get_depth_decode_graph(
+        self,
+        next_input_ids: torch.Tensor,
+        *,
+        past_length: int,
+        attention_bias: torch.Tensor,
+    ) -> _DepthDecodeCudaGraph:
+        key = self._depth_decode_key(next_input_ids, attention_bias)
+        decode_graph = self.graph
+        if decode_graph is None or decode_graph.cache_key != key:
+            decode_graph = self._build_depth_decode_graph(
+                next_input_ids,
+                past_length=past_length,
+                attention_bias=attention_bias,
+            )
+            self.graph = decode_graph
+        else:
+            decode_graph.token_ids.copy_(next_input_ids)
+            self._select_depth_decode_rope(decode_graph.cos, decode_graph.sin, past_length=past_length)
+        return decode_graph
+
+    def _run_depth_decode_attention_core(
+        self,
+        layer_idx: int,
+        stage: _DepthDecodeCudaGraphLayerStage,
+        *,
+        past_key_values: Cache,
+        attention_bias: torch.Tensor,
+        cache_position: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+    ) -> torch.Tensor:
+        attention = self.backbone.transformer.blocks[layer_idx].self_attn
+        cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+        key_states, value_states = past_key_values.update(
+            stage.key,
+            stage.value,
+            layer_idx,
+            cache_kwargs,
+        )
+        key_states = _repeat_kv(key_states, attention.num_key_value_groups)
+        value_states = _repeat_kv(value_states, attention.num_key_value_groups)
+        attn_output = F.scaled_dot_product_attention(
+            stage.query,
+            key_states,
+            value_states,
+            attn_mask=attention_bias,
+            dropout_p=0.0,
+            is_causal=False,
+        )
+        return attn_output.transpose(1, 2)
+
+    def run(
+        self,
+        next_input_ids: torch.Tensor,
+        *,
+        past_key_values: Cache,
+        attention_bias: torch.Tensor,
+        past_length: int,
+    ) -> tuple[torch.Tensor, Cache]:
+        end = past_length + 1
+        decode_graph = self._get_depth_decode_graph(
+            next_input_ids,
+            past_length=past_length,
+            attention_bias=attention_bias,
+        )
+        cache_position = decode_graph.positions[past_length:end]
+        attention_bias_q = attention_bias[:, :, past_length:end, :end]
+
+        decode_graph.pre_graph.replay()
+
+        for layer_idx, post_graph in enumerate(decode_graph.post_graphs):
+            attn_context = self._run_depth_decode_attention_core(
+                layer_idx,
+                decode_graph.stages[layer_idx],
+                past_key_values=past_key_values,
+                attention_bias=attention_bias_q,
+                cache_position=cache_position,
+                cos=decode_graph.cos,
+                sin=decode_graph.sin,
+            )
+            post_graph.attn_context.copy_(attn_context)
+            post_graph.graph.replay()
+
+        return decode_graph.output, past_key_values
+
+
+def _cuda_graph_tensor_signature(
+    tensor: torch.Tensor | None,
+) -> tuple[Any, ...] | None:
+    if tensor is None:
+        return None
+    return (
+        tuple(tensor.shape),
+        tuple(tensor.stride()),
+        str(tensor.dtype),
+        str(tensor.device),
+    )
+
+
+def _cuda_graph_context_signature(context: Any) -> tuple[Any, ...]:
+    sig = _cuda_graph_tensor_signature
+    return (
+        tuple((sig(k), sig(v)) for k, v in context.kv_contexts),
+        sig(context.cross_mask),
+        sig(context.self_mask),
+        sig(context.valid_action),
+        None if context.rope_cache is None else tuple(sig(t) for t in context.rope_cache),
+    )
+
+
+def _cuda_graph_modulation_signature(modulations: Sequence[Any]) -> tuple[Any, ...]:
+    sig = _cuda_graph_tensor_signature
+    return tuple(
+        (
+            sig(step.conditioning),
+            tuple(tuple(sig(t) for t in block_modulation) for block_modulation in step.block_modulations),
+            tuple(sig(t) for t in step.final_modulation),
+        )
+        for step in modulations
+    )
+
+
+def _cuda_graph_key(inputs: _ActionFlowInputs, steps: int) -> tuple[Any, ...]:
+    sig = _cuda_graph_tensor_signature
+    return (
+        sig(inputs.trajectory),
+        _cuda_graph_context_signature(inputs.context),
+        _cuda_graph_modulation_signature(inputs.modulations),
+        sig(inputs.action_dim_is_pad),
+        int(steps),
+    )
+
+
+def _clone_static_tensor(tensor: torch.Tensor | None) -> torch.Tensor | None:
+    if tensor is None:
+        return None
+    static = torch.empty_strided(
+        tuple(tensor.shape),
+        tuple(tensor.stride()),
+        device=tensor.device,
+        dtype=tensor.dtype,
+    )
+    static.copy_(tensor)
+    return static
+
+
+def _clone_static_context(context: Any) -> Any:
+    rope_cache = None
+    if context.rope_cache is not None:
+        rope_cache = tuple(_clone_static_tensor(t) for t in context.rope_cache)
+    return context.__class__(
+        kv_contexts=tuple((_clone_static_tensor(k), _clone_static_tensor(v)) for k, v in context.kv_contexts),
+        cross_mask=_clone_static_tensor(context.cross_mask),
+        self_mask=_clone_static_tensor(context.self_mask),
+        valid_action=_clone_static_tensor(context.valid_action),
+        rope_cache=rope_cache,
+    )
+
+
+def _clone_static_modulations(modulations: Sequence[Any]) -> Sequence[Any]:
+    return tuple(
+        step.__class__(
+            conditioning=_clone_static_tensor(step.conditioning),
+            block_modulations=tuple(
+                tuple(_clone_static_tensor(t) for t in block_modulation)
+                for block_modulation in step.block_modulations
+            ),
+            final_modulation=tuple(_clone_static_tensor(t) for t in step.final_modulation),
+        )
+        for step in modulations
+    )
+
+
+def _clone_static_inputs(inputs: _ActionFlowInputs) -> _ActionFlowInputs:
+    return _ActionFlowInputs(
+        trajectory=_clone_static_tensor(inputs.trajectory),
+        context=_clone_static_context(inputs.context),
+        modulations=_clone_static_modulations(inputs.modulations),
+        action_dim_is_pad=_clone_static_tensor(inputs.action_dim_is_pad),
+    )
+
+
+def _copy_context_(dst: Any, src: Any) -> None:
+    for (dst_k, dst_v), (src_k, src_v) in zip(dst.kv_contexts, src.kv_contexts):
+        dst_k.copy_(src_k)
+        dst_v.copy_(src_v)
+    if src.cross_mask is not None:
+        dst.cross_mask.copy_(src.cross_mask)
+    if src.self_mask is not None:
+        dst.self_mask.copy_(src.self_mask)
+    if src.valid_action is not None:
+        dst.valid_action.copy_(src.valid_action)
+    if src.rope_cache is not None:
+        for dst_tensor, src_tensor in zip(dst.rope_cache, src.rope_cache):
+            dst_tensor.copy_(src_tensor)
+
+
+def _copy_inputs_(dst: _ActionFlowInputs, src: _ActionFlowInputs) -> None:
+    dst.trajectory.copy_(src.trajectory)
+    _copy_context_(dst.context, src.context)
+    if src.action_dim_is_pad is not None:
+        dst.action_dim_is_pad.copy_(src.action_dim_is_pad)
+
+
+def _rotate_half(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def _apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (_rotate_half(q) * sin)
+    k_embed = (k * cos) + (_rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def _repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def _capture_cuda_graph(
+    fn,
+    device: torch.device,
+    *,
+    after_warmup=None,
+) -> tuple[torch.cuda.CUDAGraph, Any]:
+    warmup_stream = torch.cuda.Stream(device=device)
+    warmup_stream.wait_stream(torch.cuda.current_stream(device))
+    with torch.cuda.stream(warmup_stream):
+        fn()
+    torch.cuda.current_stream(device).wait_stream(warmup_stream)
+    if after_warmup is not None:
+        after_warmup()
+
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        output = fn()
+    return graph, output
@@ -0,0 +1,431 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+"""
+Processor class for MolmoAct2.
+"""
+
+from typing import Optional, Union
+import dataclasses
+
+import numpy as np
+
+from transformers.image_utils import ImageInput
+from transformers.video_utils import VideoInput
+from transformers.processing_utils import (
+    Unpack,
+    ProcessingKwargs,
+    ProcessorMixin,
+)
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.tokenization_utils_base import TextInput, PreTokenizedInput
+from transformers.utils import logging
+
+from transformers import AutoTokenizer
+from .image_processing_molmoact2 import MolmoAct2ImagesKwargs, MolmoAct2ImageProcessor
+from .video_processing_molmoact2 import MolmoAct2VideoProcessorKwargs, MolmoAct2VideoProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+# Special tokens, these should be present in any tokenizer we use since the preprocessor uses them
+IMAGE_PATCH_TOKEN = f"<im_patch>"  # Where to insert high-res tokens
+IMAGE_LOW_RES_TOKEN = f"<im_low>"  # Where to insert low-res tokens
+IM_START_TOKEN = f"<im_start>"
+LOW_RES_IMAGE_START_TOKEN = f"<low_res_im_start>"
+FRAME_START_TOKEN = f"<frame_start>"
+IM_END_TOKEN = f"<im_end>"
+FRAME_END_TOKEN = f"<frame_end>"
+IM_COL_TOKEN = f"<im_col>"
+IMAGE_PROMPT = "<|image|>"
+VIDEO_PROMPT = "<|video|>"
+
+IMAGE_TOKENS = [
+    IMAGE_PATCH_TOKEN,
+    IM_COL_TOKEN,
+    IM_START_TOKEN,
+    LOW_RES_IMAGE_START_TOKEN,
+    FRAME_START_TOKEN,
+    IM_END_TOKEN,
+    FRAME_END_TOKEN,
+    IMAGE_LOW_RES_TOKEN,
+]
+
+
+class MolmoAct2ProcessorKwargs(ProcessingKwargs, total=False):
+    """MolmoAct2 processor kwargs"""
+
+    images_kwargs: MolmoAct2ImagesKwargs
+    videos_kwargs: MolmoAct2VideoProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": True,
+        },
+        "videos_kwargs": {"return_metadata": True},
+    }
+
+
+class MolmoAct2Processor(ProcessorMixin):
+    attributes = ["image_processor", "video_processor", "tokenizer"]
+    optional_attributes = [
+        "chat_template",
+        "time_mode",
+        "image_use_col_tokens",
+        "use_single_crop_col_tokens",
+        "use_single_crop_start_token",
+        "video_use_col_tokens",
+        "use_frame_special_tokens",
+    ]
+    image_processor_class = "AutoImageProcessor"
+    video_processor_class = "AutoVideoProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor: MolmoAct2ImageProcessor = None,
+        video_processor: MolmoAct2VideoProcessor = None,
+        tokenizer: AutoTokenizer = None,
+        chat_template: str | None = None,
+        image_use_col_tokens: bool | None = True,
+        use_single_crop_col_tokens: bool | None = None,
+        use_single_crop_start_token: bool | None = True,
+        video_use_col_tokens: bool | None = False,
+        use_frame_special_tokens: bool | None = True,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            image_processor,
+            video_processor,
+            tokenizer,
+            chat_template=chat_template,
+        )
+        self.image_use_col_tokens = image_use_col_tokens
+        self.use_single_crop_col_tokens = use_single_crop_col_tokens
+        self.use_single_crop_start_token = use_single_crop_start_token
+        self.video_use_col_tokens = video_use_col_tokens
+        self.use_frame_special_tokens = use_frame_special_tokens
+
+        self.image_placeholder_token = IMAGE_PROMPT
+        self.video_placeholder_token = VIDEO_PROMPT
+        self.image_token_ids = [tokenizer.convert_tokens_to_ids(token) for token in IMAGE_TOKENS]
+
+    def get_image_tokens(self, image_grid: np.ndarray):
+        resized_h, resized_w, height, width = image_grid
+        if int(height) == 0 or int(width) == 0:
+            per_row = np.full(resized_w, IMAGE_PATCH_TOKEN)
+            use_single_crop_col_tokens = (
+                self.image_use_col_tokens
+                if self.use_single_crop_col_tokens is None
+                else self.use_single_crop_col_tokens
+            )
+            if use_single_crop_col_tokens:
+                per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+            joint = [
+                [IM_START_TOKEN],
+                np.tile(per_row, [resized_h]),
+                [IM_END_TOKEN],
+            ]
+            return np.concatenate(joint)
+        per_row = np.full(width, IMAGE_PATCH_TOKEN)
+        if self.image_use_col_tokens:
+            per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+        joint = [
+            [IM_START_TOKEN],
+            np.tile(per_row, [height]),
+            [IM_END_TOKEN],
+        ]
+        per_row = np.full(resized_w, IMAGE_PATCH_TOKEN)
+        use_single_crop_col_tokens = (
+            self.image_use_col_tokens
+            if self.use_single_crop_col_tokens is None
+            else self.use_single_crop_col_tokens
+        )
+        image_start_token = LOW_RES_IMAGE_START_TOKEN if self.use_single_crop_start_token else IM_START_TOKEN
+        if use_single_crop_col_tokens:
+            per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+        joint = [
+            [image_start_token],
+            np.tile(per_row, [resized_h]),
+            [IM_END_TOKEN],
+        ] + joint
+
+        return np.concatenate(joint)
+
+    def get_video_string(
+        self,
+        video_grid: np.ndarray,
+        timestamps: np.ndarray,
+    ):
+        if self.use_frame_special_tokens:
+            start_token_id = FRAME_START_TOKEN
+            end_token_id = FRAME_END_TOKEN
+        else:
+            start_token_id = IM_START_TOKEN
+            end_token_id = IM_END_TOKEN
+
+        num_frames, h, w = video_grid
+        video_string: str = ""
+        for frame_idx, frame_time in enumerate(timestamps):
+            # `per-frame-compact` time mode
+            prev_space = " " if frame_idx > 0 else ""
+            frame_prefix = prev_space + f"{frame_time:.1f} "  # explicit whitespace before/after image tokens
+
+            video_string += frame_prefix
+            per_row = np.full(w, IMAGE_PATCH_TOKEN)
+            if self.video_use_col_tokens:
+                per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+            extra_tokens = np.tile(per_row, [h])
+            video_tokens = [
+                [start_token_id],
+                extra_tokens,
+                [end_token_id],
+            ]
+            video_string += "".join(np.concatenate(video_tokens, 0))
+
+        return video_string
+
+    def insert_bos(
+        self,
+        input_ids: np.ndarray,
+        attention_mask: np.ndarray,
+        bos_token_id: int,
+        pad_token_id: int,
+    ):
+        """
+        Args:
+            input_ids: [B, S] array with left padding
+            attention_mask: [B, S] array (0 for pad, 1 for valid)
+            bos_token_id: int
+            pad_token_id: int
+        Returns:
+            input_ids_out: [B, S] or [B, S+1] array with bos inserted if needed
+            attention_mask_out: same shape as input_ids_out
+        """
+
+        need_to_expand = len(input_ids.shape) == 1
+        if need_to_expand:
+            input_ids = input_ids[None, :]
+            attention_mask = attention_mask[None, :]
+
+        B, S = input_ids.shape
+
+        # Handle zero-length sequence
+        if S == 0:
+            new_input_ids = np.full((B, 1), bos_token_id, dtype=input_ids.dtype)
+            new_attention_mask = np.ones((B, 1), dtype=attention_mask.dtype)
+            if need_to_expand:
+                new_input_ids = new_input_ids[0]
+                new_attention_mask = new_attention_mask[0]
+            return new_input_ids, new_attention_mask
+
+        first_valid_index = (attention_mask == 1).argmax(axis=-1)  # [B]
+        bos_already_present = np.all(input_ids[np.arange(B), first_valid_index] == bos_token_id)
+
+        if bos_already_present:
+            if need_to_expand:
+                input_ids = input_ids[0]
+                attention_mask = attention_mask[0]
+            return input_ids, attention_mask
+        else:
+            new_input_ids = np.full((B, S + 1), pad_token_id, dtype=input_ids.dtype)
+            new_attention_mask = np.zeros((B, S + 1), dtype=attention_mask.dtype)
+
+            src_idx = np.tile(np.arange(S), (B, 1))  # [B, S]
+            valid_mask = src_idx >= first_valid_index[:, None]  # [B, S]
+            tgt_idx = src_idx + 1  # shit right
+            batch_idx = np.tile(np.arange(B)[:, None], (1, S))  # [B, S]
+
+            # flatten valid_positions
+            flat_vals = input_ids[valid_mask]
+            flat_batch = batch_idx[valid_mask]
+            flat_tgt = tgt_idx[valid_mask]
+
+            new_input_ids[flat_batch, flat_tgt] = flat_vals
+            new_attention_mask[flat_batch, flat_tgt] = 1
+
+            insert_pos = first_valid_index
+            new_input_ids[np.arange(B), insert_pos] = bos_token_id
+            new_attention_mask[np.arange(B), insert_pos] = 1
+
+            if need_to_expand:
+                new_input_ids = new_input_ids[0]
+                new_attention_mask = new_attention_mask[0]
+
+            return new_input_ids, new_attention_mask
+
+    def __call__(
+        self,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        images: ImageInput = None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[MolmoAct2ProcessorKwargs],
+    ) -> BatchFeature:
+        """
+
+        Args:
+            text (`str`, `list[str]`, `list[list[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            videos (`dict[str, Any]` or `list[dict[str, Any]]`):
+                The video or batch of videos to be prepared. Each video can be a dictionary with the following keys:
+                - `"frames"`: `np.ndarray` of shape (T, H, W, 3)
+                - `"timestamps"`: `np.ndarray` of shape (T,)
+                - `"sampled_fps"`: `float` (optional)
+                - `"sampling_augmentation"`: `str` (optional)
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            `BatchFeature`: A [`BatchFeature`] with the following fields:
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **image_token_pooling** -- Indices of the patches in `image_grids` to pool for each token in `image_tokens`.
+              Returned when `images` is not `None`.
+            - **image_grids** -- Grids of images. Returned when `images` is not `None`.
+            - **image_num_crops** -- Number of crops for each image. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **video_token_pooling** -- Indices of the patches in `video_grids` to pool for each token in `video_tokens`.
+              Returned when `videos` is not `None`.
+            - **video_grids** -- Grids of videos. Returned when `videos` is not `None`.
+        """
+
+        output_kwargs = self._merge_kwargs(
+            MolmoAct2ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if images is not None:
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+            image_grids = image_inputs["image_grids"]
+        else:
+            image_inputs = {}
+            image_grids = None
+
+        if videos is not None:
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grids = videos_inputs["video_grids"]
+            # If user has not requested video metadata, pop it
+            if "return_metadata" not in kwargs:
+                video_metadata = videos_inputs.pop("video_metadata")
+            else:
+                video_metadata = videos_inputs["video_metadata"]
+        else:
+            videos_inputs = {}
+            video_grids = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+
+        if image_grids is not None:
+            index = 0
+            for i in range(len(text)):
+                num_images = text[i].count(self.image_placeholder_token)
+                image_grids_i = image_grids[index : index + num_images]
+                for image_grid in image_grids_i:
+                    image_tokens = self.get_image_tokens(image_grid)
+                    image_string = "".join(image_tokens)
+                    text[i] = text[i].replace(self.image_placeholder_token, image_string, 1)
+                index += num_images
+
+        if video_grids is not None:
+            index = 0
+            for i in range(len(text)):
+                num_videos = text[i].count(self.video_placeholder_token)
+                assert num_videos in {0, 1}, "At most one video is supported for now"
+                video_grids_i = video_grids[index : index + num_videos]
+                metadata_i = video_metadata[index : index + num_videos]
+                for video_grid, metadata in zip(video_grids_i, metadata_i):
+                    video_string = self.get_video_string(
+                        video_grid,
+                        metadata.timestamps,
+                    )
+                    text[i] = text[i].replace(self.video_placeholder_token, video_string, 1)
+                index += num_videos
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        input_ids = text_inputs["input_ids"]
+        attention_mask = text_inputs["attention_mask"]
+
+        input_ids = np.array(input_ids)
+        attention_mask = np.array(attention_mask)
+
+        bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
+        input_ids, attention_mask = self.insert_bos(
+            input_ids, attention_mask, bos, self.tokenizer.pad_token_id
+        )
+
+        if return_mm_token_type_ids:
+            image_tokens = np.array(self.image_token_ids).astype(input_ids.dtype)
+            token_type_ids = np.any(input_ids[:, :, None] == image_tokens[None, None, :], axis=-1)
+            text_inputs["token_type_ids"] = token_type_ids.tolist()
+
+        text_inputs["input_ids"] = input_ids.tolist()
+        text_inputs["attention_mask"] = attention_mask.tolist()
+
+        return BatchFeature(
+            data={**text_inputs, **image_inputs, **videos_inputs},
+            tensor_type=return_tensors,
+        )
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+
+MolmoAct2Processor.register_for_auto_class()
@@ -0,0 +1,997 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The Allen Institute for Artificial Intelligence and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# ruff: noqa
+
+"""Video processor class for MolmoAct2"""
+
+from functools import partial
+import os
+import warnings
+from contextlib import redirect_stdout
+from io import BytesIO
+from urllib.parse import urlparse
+from typing import Optional, Union
+from collections.abc import Callable
+
+import numpy as np
+import requests
+import einops
+import torch
+import torchvision.transforms
+
+from transformers.image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+    validate_kwargs,
+)
+from transformers.video_utils import (
+    VideoInput,
+    is_valid_video,
+    make_batched_videos,
+    make_batched_metadata,
+    VideoMetadata,
+)
+from transformers.processing_utils import Unpack, VideosKwargs
+from transformers.video_processing_utils import BaseVideoProcessor
+from transformers.utils import logging
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.utils import (
+    is_av_available,
+    is_decord_available,
+    is_torchcodec_available,
+    is_yt_dlp_available,
+    TensorType,
+    logging,
+    to_numpy,
+)
+
+
+logger = logging.get_logger(__name__)
+
+MAX_VIDEO_FPS = 8
+
+
+def normalize_image(
+    image: np.ndarray,
+    image_mean: list[float],
+    image_std: list[float],
+) -> np.ndarray:
+    if np.allclose(image_mean, [0.5, 0.5, 0.5]) and np.allclose(image_std, [0.5, 0.5, 0.5]):
+        return image * np.asarray(2.0, dtype=np.float32) - np.asarray(1.0, dtype=np.float32)
+    image -= np.array(image_mean, dtype=np.float32)[None, None, :]
+    image /= np.array(image_std, dtype=np.float32)[None, None, :]
+    return image
+
+
+def resize_image(
+    image: np.ndarray,
+    desired_output_size: list[int],
+    resample: PILImageResampling,
+) -> np.ndarray:
+    if len(image.shape) == 3:
+        is_video = False
+        image = torch.permute(torch.from_numpy(image), [2, 0, 1])
+    else:
+        is_video = True
+        image = torch.permute(torch.from_numpy(image), [0, 3, 1, 2])
+    dtype = image.dtype
+    if torch.is_floating_point(image):
+        in_min = 0.0
+        in_max = 1.0
+        resized = torchvision.transforms.Resize(
+            desired_output_size,
+            resample,
+            antialias=False,
+        )(image)
+        resized = torch.clip(resized, 0.0, 1.0).to(dtype)
+    else:
+        assert image.dtype == torch.uint8, "SigLIP expects float images or uint8 images, but got {}".format(
+            image.dtype
+        )
+        in_min = 0.0
+        in_max = 255.0
+        resized = torchvision.transforms.Resize(
+            desired_output_size,
+            resample,
+            antialias=False,
+        )(image)
+        resized = torch.clip(resized, 0, 255).to(dtype)
+
+    resized = resized.to(torch.float32)
+    resized = (resized - in_min) / (in_max - in_min)
+
+    if is_video:
+        resized = torch.permute(resized, [0, 2, 3, 1]).numpy()
+    else:
+        resized = torch.permute(resized, [1, 2, 0]).numpy()
+
+    return resized
+
+
+def build_resized_image(
+    image: np.ndarray,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    resized = resize_image(
+        image,
+        base_image_input_size,
+        resample,
+    )
+    resized = normalize_image(resized, image_mean, image_std)
+    if len(resized.shape) == 3:
+        resized = np.expand_dims(resized, 0)
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    resize_idx = np.arange(crop_patch_w * crop_patch_h).reshape([crop_patch_h, crop_patch_w])
+    return resized, resize_idx
+
+
+def batch_pixels_to_patches(array: np.ndarray, patch_size: int) -> np.ndarray:
+    """Reshape images of [n_images, h, w, 3] -> [n_images, n_patches, pixels_per_patch]"""
+    if len(array.shape) == 3:
+        n_crops, h, w = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size])
+        array = np.transpose(array, [0, 1, 3, 2, 4])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size])
+        return array
+    else:
+        n_crops, h, w, c = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size, c])
+        array = np.transpose(array, [0, 1, 3, 2, 4, 5])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size * c])
+        return array
+
+
+def arange_for_pooling(
+    idx_arr: np.ndarray,
+    pool_h: int,
+    pool_w: int,
+) -> np.ndarray:
+    h_pad = pool_h * ((idx_arr.shape[0] + pool_h - 1) // pool_h) - idx_arr.shape[0]
+    w_pad = pool_w * ((idx_arr.shape[1] + pool_w - 1) // pool_w) - idx_arr.shape[1]
+    idx_arr = np.pad(
+        idx_arr,
+        [[h_pad // 2, (h_pad + 1) // 2], [w_pad // 2, (w_pad + 1) // 2]],
+        mode="constant",
+        constant_values=-1,
+    )
+    return einops.rearrange(idx_arr, "(h dh) (w dw) -> h w (dh dw)", dh=pool_h, dw=pool_w)
+
+
+def image_to_patches_and_grids(
+    image: ImageInput,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+    image_pooling_w: int,
+    image_pooling_h: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    :return image_grids, the shape of each image after pooling
+    :return crops, the image crops to processes with the ViT
+    :return pooled_patch_idx, for each patch_id tokens in `image_tokens`, the indices of the
+                                patches in `crops` to pool for that token, masked with -1
+    """
+    if isinstance(base_image_input_size, int):
+        base_image_input_size = (base_image_input_size, base_image_input_size)
+
+    pooling_w = image_pooling_w
+    pooling_h = image_pooling_h
+
+    resized, resize_idx = build_resized_image(
+        image,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    pooling_idx = arange_for_pooling(resize_idx, pooling_h, pooling_w)
+    h, w = pooling_idx.shape[:2]
+    pooling_idx = pooling_idx.reshape([-1, pooling_h * pooling_w])
+    image_grid = [h, w]
+    return (
+        image_grid,
+        batch_pixels_to_patches(resized, image_patch_size),
+        pooling_idx,
+    )
+
+
+def get_candidate_target_fps(
+    video_fps: int | float,
+    sampling_fps: int | float,
+    max_fps: int | float = MAX_VIDEO_FPS,
+) -> list[float]:
+    """
+    Return the subset of `video_fps` factors that remain multiples of `sampling_fps`.
+
+    Examples:
+        >>> get_candidate_target_fps(video_fps=6, sampling_fps=2)
+        [2, 6]
+        >>> get_candidate_target_fps(video_fps=5, sampling_fps=1)
+        [1, 5]
+        >>> get_candidate_target_fps(video_fps=2, sampling_fps=2)
+        [2]
+        >>> get_candidate_target_fps(video_fps=5, sampling_fps=2)
+        Traceback (most recent call last):
+            ...
+        ValueError: sampling_fps=2 must divide video_fps=5 to produce consistent frame steps.
+    """
+    video_fps = int(video_fps)
+    sampling_fps = int(sampling_fps)
+    max_fps = int(max_fps)
+
+    if sampling_fps is None:
+        raise ValueError("sampling_fps must be provided")
+    if video_fps <= 0 or sampling_fps <= 0:
+        raise ValueError(f"video_fps and sampling_fps must be positive (got {video_fps}, {sampling_fps})")
+    if video_fps % sampling_fps != 0:
+        raise ValueError(f"sampling_fps={sampling_fps} must divide video_fps={video_fps}.")
+
+    candidates = []
+    for candidate in range(sampling_fps, video_fps + 1, sampling_fps):
+        if candidate > max_fps:
+            break
+        if video_fps % candidate == 0:
+            candidates.append(float(candidate))
+
+    return candidates
+
+
+def read_video_decord(
+    video_path,
+    sample_timestamps_fn: Callable,
+    **kwargs,
+) -> np.ndarray:
+    """
+    Decode a video using the Decord backend.
+
+    Args:
+        video_path (`str`):
+            Path to the video file.
+        sample_timestamps_fn (`Callable`):
+            A callable function that will return timestamps at which the video should be sampled.
+
+    Returns:
+        tuple[`np.array`, `VideoMetadata`]: A tuple containing:
+            - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
+            - `VideoMetadata` object.
+    """
+    # Lazy import from decord
+    import importlib
+
+    decord = importlib.import_module("decord")
+
+    vr = decord.VideoReader(uri=video_path, ctx=decord.cpu(0))  # decord has problems with gpu
+    video_fps = vr.get_avg_fps()
+    total_num_frames = len(vr)
+    time_stamps = vr.get_frame_timestamp(list(range(len(vr))))
+    duration = time_stamps[-1][1] - time_stamps[0][0]
+
+    metadata = VideoMetadata(
+        total_num_frames=int(total_num_frames),
+        fps=float(video_fps),
+        duration=float(duration),
+        video_backend="decord",
+    )
+
+    target_timestamps = sample_timestamps_fn(metadata=metadata, **kwargs)
+    target_timestamps = np.array(target_timestamps)
+    offset = time_stamps[0, 0]
+
+    ix = np.searchsorted(time_stamps[:, 1], target_timestamps + offset, side="right")
+    ix = np.minimum(ix, len(time_stamps) - 1)
+
+    video = vr.get_batch(ix).asnumpy()
+    metadata.update(
+        {
+            "frames_indices": target_timestamps * video_fps,
+            "height": video.shape[1],
+            "width": video.shape[2],
+        }
+    )
+    return video, metadata
+
+
+def read_video_torchcodec(
+    video_path,
+    sample_timestamps_fn: Callable,
+    **kwargs,
+) -> np.ndarray:
+    """
+    Decode a video using torchcodec decoder.
+
+    Args:
+        video_path (`str`):
+            Path to the video file.
+        sample_timestamps_fn (`Callable`):
+            A callable function that will return timestamps at which the video should be sampled.
+
+    Returns:
+        tuple[`np.array`, `VideoMetadata`]: A tuple containing:
+            - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
+            - `VideoMetadata` object.
+    """
+    # Lazy import torchcodec
+    import importlib
+
+    torchcodec = importlib.import_module("torchcodec")
+
+    decoder = torchcodec.decoders.VideoDecoder(
+        video_path,
+        # Interestingly `exact` mode takes less than approximate when we load the whole video
+        seek_mode="exact",
+        # Allow FFmpeg decide on the number of threads for efficiency
+        num_ffmpeg_threads=0,
+    )
+    # If the first frame starts at > 0, we effectively clip the video starting at that time
+    # since (most) video players would also skip to that time
+    time_offset = decoder.metadata.begin_stream_seconds_from_content
+    # Note this duration does assume we started playing at `time_offset`
+    duration = decoder.metadata.duration_seconds
+
+    metadata = VideoMetadata(
+        total_num_frames=decoder.metadata.num_frames,
+        fps=decoder.metadata.average_fps,
+        duration=duration,
+        video_backend="torchcodec",
+        height=decoder.metadata.height,
+        width=decoder.metadata.width,
+    )
+
+    target_timestamps = sample_timestamps_fn(metadata=metadata, **kwargs)
+
+    # Floating point/rounding issues might cause `target_timestamps` to be very slightly
+    # out-of-bounds, to handle this we sanity check then clip them
+    assert all(x >= 0 for x in target_timestamps)
+    assert all(x < duration + 1e-6 for x in target_timestamps)
+    # 1e-6 padding since torchcodec can throw out-of-bounds errors even if you ask for the
+    # exact boundary value, we should still get the first/last frame anyway
+    max_timestamp = decoder.metadata.end_stream_seconds_from_content - 1e-6
+    min_timestamp = decoder.metadata.begin_stream_seconds_from_content + 1e-6
+    # Note we avoid using numpy ops here to reduce floating precision issues
+    timestamps = [x + time_offset for x in target_timestamps]
+    timestamps = [max(min_timestamp, min(max_timestamp, x)) for x in timestamps]
+
+    video = (
+        decoder.get_frames_played_at(timestamps).data.numpy().transpose(0, 2, 3, 1)
+    )  # Convert to THWC format
+    target_timestamps = np.array(target_timestamps)
+    metadata.frames_indices = target_timestamps * metadata.fps
+
+    return video, metadata
+
+
+def read_video_pyav(
+    video_path,
+    sample_timestamps_fn: Callable,
+    **kwargs,
+) -> np.ndarray:
+    """
+    Decode a video using the PyAV backend.
+
+    Args:
+        video_path (`str`):
+            Path to the video file.
+        sample_timestamps_fn (`Callable`):
+            A callable function that will return timestamps at which the video should be sampled.
+
+    Returns:
+        tuple[`np.array`, `VideoMetadata`]: A tuple containing:
+            - Numpy array of frames in RGB (shape: [num_frames, height, width, 3]).
+            - `VideoMetadata` object.
+    """
+    # Lazy import torchcodec
+    import importlib
+
+    av = importlib.import_module("av")
+
+    with av.open(video_path) as container:
+        video_stream = container.streams.video[0]
+        fps = video_stream.average_rate or video_stream.guessed_rate
+        it = container.decode(video=0)
+        frames = list(it)
+
+        stream = container.streams.video[0]
+        start = frames[0].pts * stream.time_base
+        container_end = stream.duration
+        if container_end is not None:
+            container_end *= stream.time_base
+        if container_end is None or container_end < frames[-1].pts:
+            # Some problem with stream duration, so use the frame PTS directly
+            # and guess the duration of the last frame
+            end = frames[-1].pts * stream.time_base + 1 / fps
+        else:
+            end = container_end
+        duration = float(end - start)
+
+        metadata = VideoMetadata(
+            total_num_frames=len(frames),
+            fps=float(fps),
+            duration=float(duration),
+            video_backend="pyav",
+            height=video_stream.height,
+            width=video_stream.width,
+        )
+
+        target_timestamps = sample_timestamps_fn(metadata=metadata, **kwargs)
+        offset = float(start)
+
+        target_timestamps = np.array(target_timestamps)
+        end_time_stamps = np.array([float(frame.pts * stream.time_base) for frame in frames[1:]] + [duration])
+        indices = np.searchsorted(end_time_stamps, target_timestamps + offset, side="right")
+        indices = np.minimum(indices, len(end_time_stamps) - 1)
+
+        video = np.stack(
+            [frames[i].to_ndarray(format="rgb24", channel_last=True) for i in indices],
+            axis=0,
+        )
+
+        metadata.frames_indices = target_timestamps * fps
+
+        return video, metadata
+
+
+VIDEO_DECODERS = {
+    "decord": read_video_decord,
+    "torchcodec": read_video_torchcodec,
+    "pyav": read_video_pyav,
+}
+
+
+def load_video(
+    video: VideoInput,
+    backend: str = "decord",
+    sample_timestamps_fn: Callable | None = None,
+    **kwargs,
+):
+    """
+    Loads `video` to a numpy array.
+
+    Args:
+        video (`VideoInput`):
+            The video to convert to the numpy array format. Can be a link to video or local path.
+        backend (`str`, *optional*, defaults to `"decord"`):
+            The backend to use when loading the video. Can be any of ["decord", "pyav", ""torchcodec"]. Defaults to "decord".
+        sample_timestamps_fn (`Callable`):
+            A callable function that will return timestamps at which the video should be sampled.
+    """
+
+    # Early exit if provided an array or `PIL` frames
+    if not isinstance(video, str):
+        metadata = [None] * len(video)
+        return video, metadata
+
+    if urlparse(video).netloc in ["www.youtube.com", "youtube.com"]:
+        if not is_yt_dlp_available():
+            raise ImportError("To load a video from YouTube url you have  to install `yt_dlp` first.")
+        # Lazy import from yt_dlp
+        import importlib
+
+        yt_dlp = importlib.import_module("yt_dlp")
+
+        buffer = BytesIO()
+        with redirect_stdout(buffer), yt_dlp.YoutubeDL() as f:
+            f.download([video])
+        bytes_obj = buffer.getvalue()
+        file_obj = BytesIO(bytes_obj)
+    elif video.startswith("http://") or video.startswith("https://"):
+        file_obj = BytesIO(requests.get(video, timeout=10).content)
+    elif os.path.isfile(video):
+        file_obj = video
+    else:
+        raise TypeError(
+            "Incorrect format used for video. Should be an url linking to an video or a local path."
+        )
+
+    # can also load with decord, but not cv2/torchvision
+    # both will fail in case of url links
+    video_is_url = video.startswith("http://") or video.startswith("https://")
+    if video_is_url and backend == "opencv":
+        raise ValueError("If you are trying to load a video from URL, you cannot use 'opencv' as backend")
+
+    if (
+        (not is_decord_available() and backend == "decord")
+        or (not is_torchcodec_available() and backend == "torchcodec")
+        or (not is_av_available() and backend == "pyav")
+    ):
+        raise ImportError(
+            f"You chose backend={backend} for loading the video but the required library is not found in your environment "
+            f"Make sure to install {backend} before loading the video."
+        )
+
+    video_decoder = VIDEO_DECODERS[backend]
+    video, metadata = video_decoder(file_obj, sample_timestamps_fn, **kwargs)
+    return video, metadata
+
+
+def get_target_fps(
+    video_fps: float,
+    max_frames: int,
+    total_frames: int,
+    frame_sample_mode: str,
+    candidate_target_fps: tuple[float],
+) -> float:
+    """
+    Get the target fps that best spans the video and has the most frames sampled
+    """
+    num_frames_sampled = 0
+    selected_target_fps = None
+    for target_fps in candidate_target_fps:
+        step_size = max(int(video_fps / target_fps), 1)
+        num_frames_sampled_at_fps = int(total_frames / step_size)
+        if num_frames_sampled == 0:
+            if "uniform" in frame_sample_mode:
+                if num_frames_sampled_at_fps > max_frames:
+                    break
+            selected_target_fps = target_fps
+            num_frames_sampled = num_frames_sampled_at_fps
+
+        else:
+            # the candidate sampling fps increases so frame count can't decrease
+            assert num_frames_sampled <= num_frames_sampled_at_fps
+            if num_frames_sampled_at_fps > max_frames:
+                # choose the sampling fps that spans the video
+                continue
+
+            elif num_frames_sampled_at_fps > num_frames_sampled:
+                # both are less than max_frames, choose the one with higher density of frames sampled
+                selected_target_fps = target_fps
+                num_frames_sampled = num_frames_sampled_at_fps
+    return selected_target_fps
+
+
+def get_frame_times_and_chosen_fps(selected_target_fps, total_frames, max_frames, video_fps):
+    if selected_target_fps is None:
+        frame_indices = np.linspace(0, total_frames, max_frames, endpoint=False, dtype=int)
+    else:
+        step_size = max(int(video_fps / selected_target_fps), 1)
+        frame_indices = np.arange(0, total_frames, step_size)
+    if len(frame_indices) > max_frames:
+        frame_indices = frame_indices[:max_frames]
+    return selected_target_fps, frame_indices
+
+
+class MolmoAct2VideoProcessorKwargs(VideosKwargs, total=False):
+    patch_size: int | None
+    pooling_size: list[int] | None
+    frame_sample_mode: str | None
+    max_fps: int | None
+    sampling_fps: int | None
+
+
+class MolmoAct2VideoProcessor(BaseVideoProcessor):
+    resample = PILImageResampling.BILINEAR
+    size = {"height": 378, "width": 378}
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    patch_size = 14
+    pooling_size = [3, 3]
+    do_sample_frames = True
+    frame_sample_mode = "uniform_last_frame"
+    max_fps = 2
+    sampling_fps = 2
+    valid_kwargs = MolmoAct2VideoProcessorKwargs
+    model_input_names = ["pixel_values_videos", "video_token_pooling", "video_grids"]
+
+    def __init__(self, **kwargs: Unpack[MolmoAct2VideoProcessorKwargs]):
+        super().__init__(**kwargs)
+        if self.size is not None and (
+            self.size.get("height", None) is None or self.size.get("width", None) is None
+        ):
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+    def _further_process_kwargs(
+        self,
+        size: SizeDict | None = None,
+        **kwargs,
+    ) -> dict:
+        """
+        Update kwargs that need further processing before being validated
+        Can be overridden by subclasses to customize the processing of kwargs.
+        """
+        if size is not None and ("height" not in size or "width" not in size):
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+        return super()._further_process_kwargs(size=size, **kwargs)
+
+    def sample_times(
+        self,
+        metadata: VideoMetadata,
+        frame_sample_mode: str,
+        num_frames: int,
+        max_fps: int | None = None,
+        sampling_fps: int | None = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Time-based sampling if an array video is passed
+        Args:
+            metadata (`VideoMetadata`):
+                Metadata of the video containing information about total duration, fps and total number of frames.
+            frame_sample_mode (`str`, *optional*):
+                Mode to sample frames. Defaults to `self.frame_sample_mode`.
+            num_frames (`int`, *optional*):
+                Maximum number of frames to sample. Defaults to `self.num_frames`.
+            man_fps (`int`, *optional*):
+                Maximum frames per second to sample.
+            sampling_fps (`int`, *optional*):
+                Sampling frames per second. Defaults to `self.sampling_fps`.
+                Used when `frame_sample_mode` is `"fps"`.
+        """
+        frame_sample_mode = frame_sample_mode or self.frame_sample_mode
+        num_frames = num_frames or self.num_frames
+        sampling_fps = sampling_fps or self.sampling_fps
+
+        duration = metadata.duration or metadata.total_num_frames / metadata.fps
+        if frame_sample_mode == "fps":
+            candidate_target_fps = get_candidate_target_fps(metadata.fps, sampling_fps)
+            # Try larger and larger FPSs until we hit one that can't span the video
+            target_fps = candidate_target_fps[0]
+            for candidate_fps in candidate_target_fps[1:]:
+                if num_frames / candidate_fps < duration:
+                    break
+                target_fps = candidate_fps
+            times = np.arange(0, num_frames) / target_fps
+            times = times[times < duration]
+            return times
+        elif frame_sample_mode == "uniform_last_frame":
+            if max_fps is not None:
+                max_duration = (num_frames - 1) / max_fps  # -1 to include the last frame
+                if max_duration < duration:
+                    times = np.linspace(0, duration, num=num_frames, endpoint=True, dtype=np.float64)
+                else:
+                    times = np.arange(0.0, stop=duration, step=1 / max_fps)
+                    times = np.concatenate([times, [duration]], axis=0)
+                    assert len(times) <= num_frames
+            else:
+                times = np.linspace(0, duration, num=num_frames, endpoint=True, dtype=np.float64)
+            return times
+        else:
+            raise NotImplementedError(frame_sample_mode)
+
+    def sample_frames(
+        self,
+        metadata: VideoMetadata,
+        frame_sample_mode: str | None = None,
+        num_frames: int | None = None,
+        max_fps: int | None = None,
+        sampling_fps: int | None = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Frame-based sampling if an array video is passed
+        Args:
+            metadata (`VideoMetadata`):
+                Metadata of the video containing information about total duration, fps and total number of frames.
+            frame_sample_mode (`str`, *optional*):
+                Mode to sample frames. Defaults to `self.frame_sample_mode`.
+            num_frames (`int`, *optional*):
+                Maximum number of frames to sample. Defaults to `self.num_frames`.
+            max_fps (`int`, *optional*):
+                Maximum frames per second to sample.
+            sampling_fps (`int`, *optional*):
+                Sampling frames per second. Defaults to `self.sampling_fps`.
+                Used when `frame_sample_mode` is `"fps"`.
+        """
+        frame_sample_mode = frame_sample_mode or self.frame_sample_mode
+        num_frames = num_frames or self.num_frames
+        sampling_fps = sampling_fps or self.sampling_fps
+
+        total_num_frames = metadata.total_num_frames
+        if frame_sample_mode == "uniform_last_frame" and max_fps is not None:
+            duration = total_num_frames / metadata.fps
+            if total_num_frames <= 2:
+                return np.arange(total_num_frames).astype(int)
+            if duration > (num_frames - 1) / max_fps:  # -1 to include the last frame
+                # uniform fallback
+                indices = np.linspace(
+                    0,
+                    total_num_frames - 1,
+                    num=min(num_frames, total_num_frames),
+                    endpoint=True,
+                ).astype(int)
+                return indices
+            else:
+                float_indices = np.arange(
+                    0.0,
+                    stop=total_num_frames - 1,
+                    step=float(metadata.fps / max_fps),
+                )
+                if np.round(float_indices[-1]) != total_num_frames - 1:
+                    float_indices = np.concatenate([float_indices, [total_num_frames - 1]], axis=0)
+                indices = np.round(float_indices).astype(int)
+                assert indices[-1] < total_num_frames
+                assert len(float_indices) <= num_frames
+                return indices
+        elif frame_sample_mode == "uniform_last_frame":
+            indices = np.linspace(
+                0,
+                total_num_frames - 1,
+                num=min(num_frames, total_num_frames),
+                endpoint=True,
+            ).astype(int)
+            return indices
+        elif frame_sample_mode == "fps":
+            candidate_target_fps = get_candidate_target_fps(metadata.fps, sampling_fps)
+            selected_target_fps = get_target_fps(
+                metadata.fps,
+                num_frames,
+                total_num_frames,
+                frame_sample_mode,
+                candidate_target_fps,
+            )
+            _, indices = get_frame_times_and_chosen_fps(
+                selected_target_fps,
+                total_num_frames,
+                num_frames,
+                metadata.fps,
+            )
+            return indices
+        else:
+            raise NotImplementedError(frame_sample_mode)
+
+    def fetch_videos(self, video_url_or_urls: str | list[str] | list[list[str]], sample_timestamps_fn=None):
+        """
+        Convert a single or a list of urls into the corresponding `np.array` objects.
+
+        If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
+        returned.
+        """
+        if (not is_decord_available()) and (not is_torchcodec_available()) and (not is_av_available()):
+            raise ImportError(
+                "MolmoAct2VideoProcessor requires `decord`, `torchcodec`, or `av` to be installed."
+            )
+
+        if is_decord_available():
+            backend = "decord"
+        elif is_torchcodec_available():
+            warnings.warn(
+                "`decord` is not installed and cannot be used to decode the video by default. "
+                "Falling back to `torchcodec`."
+            )
+            backend = "torchcodec"
+        else:
+            warnings.warn(
+                "`decord` is not installed and cannot be used to decode the video by default. "
+                "Falling back to `PyAV`."
+            )
+            backend = "pyav"
+
+        if isinstance(video_url_or_urls, list):
+            return list(
+                zip(
+                    *[
+                        self.fetch_videos(x, sample_timestamps_fn=sample_timestamps_fn)
+                        for x in video_url_or_urls
+                    ]
+                )
+            )
+        else:
+            return load_video(video_url_or_urls, backend=backend, sample_timestamps_fn=sample_timestamps_fn)
+
+    def _decode_and_sample_videos(
+        self,
+        videos: VideoInput,
+        video_metadata: VideoMetadata | dict,
+        do_sample_frames: bool | None = None,
+        sample_indices_fn: Callable | None = None,
+        sample_timestamps_fn: Callable | None = None,
+    ):
+        """
+        Decode input videos and sample frames if needed.
+        """
+        videos = make_batched_videos(videos)
+        video_metadata = make_batched_metadata(videos, video_metadata=video_metadata)
+
+        # Framed-based sampling if an array video is passed
+        # Otherwise, time-based sampling with decoding
+        if is_valid_video(videos[0]) and do_sample_frames:
+            assert video_metadata[0].fps is not None, "FPS must be provided for video input"
+            sampled_videos = []
+            sampled_metadata = []
+            for video, metadata in zip(videos, video_metadata):
+                indices = sample_indices_fn(metadata=metadata)
+                metadata.frames_indices = indices
+                sampled_videos.append(video[indices])
+                sampled_metadata.append(metadata)
+            videos = sampled_videos
+            video_metadata = sampled_metadata
+        elif not is_valid_video(videos[0]):
+            if sample_indices_fn is None:
+                logger.warning(
+                    "do_sample_frames is False, but video array is not provided: "
+                    "Will decode the video and sample frames using MolmoAct2's default sampling mode"
+                )
+            if isinstance(videos[0], list):
+                raise ValueError("A list of images is not supported for video input!")
+            else:
+                videos, video_metadata = self.fetch_videos(videos, sample_timestamps_fn=sample_timestamps_fn)
+
+        return videos, video_metadata
+
+    def _prepare_input_videos(
+        self,
+        videos: VideoInput,
+        **kwargs,
+    ) -> list[np.ndarray]:
+        processed_videos = [to_numpy(video) for video in videos]
+        return processed_videos
+
+    def preprocess(
+        self,
+        videos: VideoInput,
+        **kwargs: Unpack[MolmoAct2VideoProcessorKwargs],
+    ) -> BatchFeature:
+        validate_kwargs(
+            captured_kwargs=kwargs.keys(),
+            valid_processor_keys=list(self.valid_kwargs.__annotations__.keys()) + ["return_tensors"],
+        )
+
+        # Set default kwargs from self. This ensures that if a kwarg is not provided
+        # by the user, it gets its default value from the instance, or is set to None.
+        for kwarg_name in self.valid_kwargs.__annotations__:
+            kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
+
+        do_sample_frames = kwargs.pop("do_sample_frames")
+        video_metadata = kwargs.pop("video_metadata")
+
+        sample_indices_fn = partial(self.sample_frames, **kwargs) if do_sample_frames else None
+        sample_timestamps_fn = partial(self.sample_times, **kwargs)
+        videos, video_metadata = self._decode_and_sample_videos(
+            videos,
+            video_metadata=video_metadata,
+            do_sample_frames=do_sample_frames,
+            sample_indices_fn=sample_indices_fn,
+            sample_timestamps_fn=sample_timestamps_fn,
+        )
+        videos = self._prepare_input_videos(videos=videos)
+
+        kwargs = self._further_process_kwargs(**kwargs)
+
+        return_metadata = kwargs.pop("return_metadata")
+        preprocessed_videos = self._preprocess(videos=videos, **kwargs)
+        if return_metadata:
+            preprocessed_videos["video_metadata"] = video_metadata
+        return preprocessed_videos
+
+    def _preprocess(
+        self,
+        videos: list[np.ndarray],
+        size: SizeDict | None = None,
+        resample: PILImageResampling | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool | None = None,
+        patch_size: int | None = None,
+        pooling_size: list[int] | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Preprocess a video for the model.
+        Args:
+            videos (`VideoInput`):
+                Video to preprocess.
+            size (`SizeDict`, *optional*, defaults to `self.size`):
+                Size of the image after resizing.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use when resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            pooling_size (`list[int]`, *optional*, defaults to `self.pooling_size`):
+                The pooling size of the vision adapter.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+
+        Returns:
+            A `BatchFeature` containing the following keys:
+                - `pixel_values_videos`: The preprocessed videos.
+                - `video_token_pooling`: The indices of the patches in `crops` to pool for each token in `video_tokens`.
+                - `video_grids`: The video grids.
+        """
+        if size.height is None or size.width is None:
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+        base_image_input_size = [size.height, size.width]
+
+        resample = resample or self.resample
+        image_mean = image_mean or self.image_mean
+        image_std = image_std or self.image_std
+        do_convert_rgb = do_convert_rgb or self.do_convert_rgb
+
+        patch_size = patch_size or self.patch_size
+        pooling_size = pooling_size or self.pooling_size
+
+        image_pooling_h, image_pooling_w = pooling_size
+
+        batch_grids = []
+        batch_crops = []
+        batch_pooled_patches_idx = []
+
+        for video in videos:
+            all_crops = []
+            pooled_patches_idx = []
+
+            for frame in video:
+                image_grid, crops, pooled_idx = image_to_patches_and_grids(
+                    frame,
+                    base_image_input_size,
+                    resample,
+                    image_mean,
+                    image_std,
+                    patch_size,
+                    image_pooling_w,
+                    image_pooling_h,
+                )
+                offset = sum(np.prod(x.shape[:2]) for x in all_crops)
+                pooled_idx_with_offset = np.where(pooled_idx >= 0, pooled_idx + offset, pooled_idx)
+                pooled_patches_idx.append(pooled_idx_with_offset)
+                all_crops.append(crops)
+
+            video_grid = np.array([len(video), image_grid[0], image_grid[1]])
+            all_crops = np.concatenate(all_crops, 0)
+            pooled_patches_idx = np.concatenate(pooled_patches_idx, 0)
+
+            batch_grids.append(video_grid)
+            batch_crops.append(all_crops)
+            batch_pooled_patches_idx.append(pooled_patches_idx)
+
+        video_grids = np.stack(batch_grids, 0)
+        pixel_values_videos = np.concatenate(batch_crops, 0)
+        video_token_pooling = np.concatenate(batch_pooled_patches_idx, 0)
+
+        data = dict(
+            pixel_values_videos=pixel_values_videos,
+            video_token_pooling=video_token_pooling,
+            video_grids=video_grids,
+        )
+
+        return BatchFeature(data, tensor_type=return_tensors)
+
+
+MolmoAct2VideoProcessor.register_for_auto_class()
@@ -15,7 +15,6 @@
 # limitations under the License.

 import builtins
-import copy
 import logging
 import math
 from collections import deque
@@ -30,6 +29,7 @@ from lerobot.utils.import_utils import _transformers_available, require_package

 # Conditional import for type checking and lazy loading
 if TYPE_CHECKING or _transformers_available:
+    from transformers.cache_utils import DynamicCache
    from transformers.models.auto import CONFIG_MAPPING
    from transformers.models.gemma import modeling_gemma

@@ -41,6 +41,7 @@ if TYPE_CHECKING or _transformers_available:
    )
 else:
    CONFIG_MAPPING = None
+    DynamicCache = None
    modeling_gemma = None
    PiGemmaForCausalLM = None
    _gated_residual = None
@@ -141,6 +142,15 @@ def make_att_2d_masks(pad_masks, att_masks):  # see openpi `make_att_2d_masks` (
    return att_2d_masks & pad_2d_masks


+def clone_past_key_values(past_key_values):
+    """Clone the DynamicCache returned by prefix prefill for compiled denoising."""
+    return DynamicCache(
+        tuple(
+            (keys.clone(), values.clone(), sliding_window) for keys, values, sliding_window in past_key_values
+        )
+    )
+
+
 def pad_vector(vector, new_dim):
    """Pad the last dimension of a vector to new_dim with zeros.

@@ -227,16 +237,13 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)


 # Define the complete layer computation function for gradient checkpointing
-def compute_layer_complete(
-    layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
-):
-    models = [paligemma.model.language_model, gemma_expert.model]
+def compute_layer_complete(inputs_embeds, attention_mask, position_ids, adarms_cond, layers, rotary_emb):
    query_states = []
    key_states = []
    value_states = []
    gates = []
    for i, hidden_states in enumerate(inputs_embeds):
-        layer = models[i].layers[layer_idx]
+        layer = layers[i]
        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
        gates.append(gate)
        input_shape = hidden_states.shape[:-1]
@@ -258,15 +265,16 @@ def compute_layer_complete(
        device=query_states.device,
        dtype=query_states.dtype,
    )
-    cos, sin = paligemma.model.language_model.rotary_emb(dummy_tensor, position_ids)
+    cos, sin = rotary_emb(dummy_tensor, position_ids)
    query_states, key_states = modeling_gemma.apply_rotary_pos_emb(
        query_states, key_states, cos, sin, unsqueeze_dim=1
    )
    batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    paligemma_layer = layers[0]
+    scaling = paligemma_layer.self_attn.scaling
    # Attention computation
    att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma_layer.self_attn,
        query_states,
        key_states,
        value_states,
@@ -274,13 +282,13 @@ def compute_layer_complete(
        scaling,
    )
    # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma_layer.self_attn.head_dim
    att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
    # Process layer outputs
    outputs_embeds = []
    start_pos = 0
    for i, hidden_states in enumerate(inputs_embeds):
-        layer = models[i].layers[layer_idx]
+        layer = layers[i]
        end_pos = start_pos + hidden_states.shape[1]
        if att_output.dtype != layer.self_attn.o_proj.weight.dtype:
            att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
@@ -488,8 +496,9 @@ class PaliGemmaWithExpertModel(
            prefix_output = None
            prefix_past_key_values = None
        else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
-            num_layers = self.paligemma.config.text_config.num_hidden_layers
+            paligemma_layers = self.paligemma.model.language_model.layers
+            gemma_expert_layers = self.gemma_expert.model.layers
+            rotary_emb = self.paligemma.model.language_model.rotary_emb

            # Check if gradient checkpointing is enabled for any of the models
            use_gradient_checkpointing = (
@@ -499,36 +508,39 @@ class PaliGemmaWithExpertModel(
            ) or (hasattr(self, "gradient_checkpointing") and self.gradient_checkpointing and self.training)

            # Process all layers with gradient checkpointing if enabled
-            for layer_idx in range(num_layers):
+            for layers in zip(paligemma_layers, gemma_expert_layers, strict=True):
                if use_gradient_checkpointing:
                    inputs_embeds = torch.utils.checkpoint.checkpoint(
                        compute_layer_complete,
-                        layer_idx,
                        inputs_embeds,
                        attention_mask,
                        position_ids,
                        adarms_cond,
                        use_reentrant=False,
                        preserve_rng_state=False,
-                        paligemma=self.paligemma,
-                        gemma_expert=self.gemma_expert,
+                        layers=layers,
+                        rotary_emb=rotary_emb,
                    )
                else:
                    inputs_embeds = compute_layer_complete(
-                        layer_idx,
                        inputs_embeds,
                        attention_mask,
                        position_ids,
                        adarms_cond,
-                        paligemma=self.paligemma,
-                        gemma_expert=self.gemma_expert,
+                        layers=layers,
+                        rotary_emb=rotary_emb,
                    )

            # final norm
+            final_norms = (
+                self.paligemma.model.language_model.norm,
+                self.gemma_expert.model.norm,
+            )
+
            def compute_final_norms(inputs_embeds, adarms_cond):
                outputs_embeds = []
                for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = layernorm_forward(final_norms[i], hidden_states, adarms_cond[i])
                    outputs_embeds.append(out_emb)
                return outputs_embeds

@@ -907,7 +919,7 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001

-        past_key_values = copy.deepcopy(past_key_values)
+        past_key_values = clone_past_key_values(past_key_values)
        outputs_embeds, _ = self.paligemma_with_expert.forward(
            attention_mask=full_att_2d_masks_4d,
            position_ids=position_ids,
@@ -15,7 +15,6 @@
 # limitations under the License.

 import builtins
-import copy
 import logging
 import math
 from collections import deque
@@ -30,6 +29,7 @@ from lerobot.utils.import_utils import _transformers_available, require_package

 # Conditional import for type checking and lazy loading
 if TYPE_CHECKING or _transformers_available:
+    from transformers.cache_utils import DynamicCache
    from transformers.models.auto import CONFIG_MAPPING
    from transformers.models.gemma import modeling_gemma

@@ -41,6 +41,7 @@ if TYPE_CHECKING or _transformers_available:
    )
 else:
    CONFIG_MAPPING = None
+    DynamicCache = None
    modeling_gemma = None
    PiGemmaForCausalLM = None
    _gated_residual = None
@@ -138,6 +139,15 @@ def make_att_2d_masks(pad_masks, att_masks):  # see openpi `make_att_2d_masks` (
    return att_2d_masks & pad_2d_masks


+def clone_past_key_values(past_key_values):
+    """Clone the DynamicCache returned by prefix prefill for compiled denoising."""
+    return DynamicCache(
+        tuple(
+            (keys.clone(), values.clone(), sliding_window) for keys, values, sliding_window in past_key_values
+        )
+    )
+
+
 def pad_vector(vector, new_dim):
    """Pad the last dimension of a vector to new_dim with zeros.

@@ -224,16 +234,13 @@ def resize_with_pad_torch(  # see openpi `resize_with_pad_torch` (exact copy)


 # Define the complete layer computation function for gradient checkpointing
-def compute_layer_complete(
-    layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond, paligemma, gemma_expert
-):
-    models = [paligemma.model.language_model, gemma_expert.model]
+def compute_layer_complete(inputs_embeds, attention_mask, position_ids, adarms_cond, layers, rotary_emb):
    query_states = []
    key_states = []
    value_states = []
    gates = []
    for i, hidden_states in enumerate(inputs_embeds):
-        layer = models[i].layers[layer_idx]
+        layer = layers[i]
        hidden_states, gate = layernorm_forward(layer.input_layernorm, hidden_states, adarms_cond[i])
        gates.append(gate)
        input_shape = hidden_states.shape[:-1]
@@ -255,15 +262,16 @@ def compute_layer_complete(
        device=query_states.device,
        dtype=query_states.dtype,
    )
-    cos, sin = paligemma.model.language_model.rotary_emb(dummy_tensor, position_ids)
+    cos, sin = rotary_emb(dummy_tensor, position_ids)
    query_states, key_states = modeling_gemma.apply_rotary_pos_emb(
        query_states, key_states, cos, sin, unsqueeze_dim=1
    )
    batch_size = query_states.shape[0]
-    scaling = paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+    paligemma_layer = layers[0]
+    scaling = paligemma_layer.self_attn.scaling
    # Attention computation
    att_output, _ = modeling_gemma.eager_attention_forward(
-        paligemma.model.language_model.layers[layer_idx].self_attn,
+        paligemma_layer.self_attn,
        query_states,
        key_states,
        value_states,
@@ -271,13 +279,13 @@ def compute_layer_complete(
        scaling,
    )
    # Get head_dim from the current layer, not from the model
-    head_dim = paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+    head_dim = paligemma_layer.self_attn.head_dim
    att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
    # Process layer outputs
    outputs_embeds = []
    start_pos = 0
    for i, hidden_states in enumerate(inputs_embeds):
-        layer = models[i].layers[layer_idx]
+        layer = layers[i]
        end_pos = start_pos + hidden_states.shape[1]
        if att_output.dtype != layer.self_attn.o_proj.weight.dtype:
            att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
@@ -485,8 +493,9 @@ class PaliGemmaWithExpertModel(
            prefix_output = None
            prefix_past_key_values = None
        else:
-            models = [self.paligemma.model.language_model, self.gemma_expert.model]
-            num_layers = self.paligemma.config.text_config.num_hidden_layers
+            paligemma_layers = self.paligemma.model.language_model.layers
+            gemma_expert_layers = self.gemma_expert.model.layers
+            rotary_emb = self.paligemma.model.language_model.rotary_emb

            # Check if gradient checkpointing is enabled for any of the models
            use_gradient_checkpointing = (
@@ -496,36 +505,39 @@ class PaliGemmaWithExpertModel(
            ) or (hasattr(self, "gradient_checkpointing") and self.gradient_checkpointing and self.training)

            # Process all layers with gradient checkpointing if enabled
-            for layer_idx in range(num_layers):
+            for layers in zip(paligemma_layers, gemma_expert_layers, strict=True):
                if use_gradient_checkpointing:
                    inputs_embeds = torch.utils.checkpoint.checkpoint(
                        compute_layer_complete,
-                        layer_idx,
                        inputs_embeds,
                        attention_mask,
                        position_ids,
                        adarms_cond,
                        use_reentrant=False,
                        preserve_rng_state=False,
-                        paligemma=self.paligemma,
-                        gemma_expert=self.gemma_expert,
+                        layers=layers,
+                        rotary_emb=rotary_emb,
                    )
                else:
                    inputs_embeds = compute_layer_complete(
-                        layer_idx,
                        inputs_embeds,
                        attention_mask,
                        position_ids,
                        adarms_cond,
-                        paligemma=self.paligemma,
-                        gemma_expert=self.gemma_expert,
+                        layers=layers,
+                        rotary_emb=rotary_emb,
                    )

            # final norm
+            final_norms = (
+                self.paligemma.model.language_model.norm,
+                self.gemma_expert.model.norm,
+            )
+
            def compute_final_norms(inputs_embeds, adarms_cond):
                outputs_embeds = []
                for i, hidden_states in enumerate(inputs_embeds):
-                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    out_emb, _ = layernorm_forward(final_norms[i], hidden_states, adarms_cond[i])
                    outputs_embeds.append(out_emb)
                return outputs_embeds

@@ -880,7 +892,7 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001

-        past_key_values = copy.deepcopy(past_key_values)
+        past_key_values = clone_past_key_values(past_key_values)
        outputs_embeds, _ = self.paligemma_with_expert.forward(
            attention_mask=full_att_2d_masks_4d,
            position_ids=position_ids,
@@ -0,0 +1 @@
+../../../../docs/source/policy_vla_jepa_README.md
@@ -0,0 +1,23 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_vla_jepa import VLAJEPAConfig
+from .modeling_vla_jepa import VLAJEPAPolicy
+from .processor_vla_jepa import make_vla_jepa_pre_post_processors
+
+__all__ = [
+    "VLAJEPAConfig",
+    "VLAJEPAPolicy",
+    "make_vla_jepa_pre_post_processors",
+]
@@ -0,0 +1,337 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections import OrderedDict
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from torch import nn
+from torch.distributions import Beta
+
+from lerobot.utils.import_utils import _diffusers_available, require_package
+
+if TYPE_CHECKING or _diffusers_available:
+    from diffusers import ConfigMixin, ModelMixin
+    from diffusers.configuration_utils import register_to_config
+    from diffusers.models.attention import Attention, FeedForward
+    from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+else:
+
+    class ModelMixin:  # type: ignore[no-redef]
+        pass
+
+    class ConfigMixin:  # type: ignore[no-redef]
+        pass
+
+    register_to_config = lambda f: f  # noqa: E731
+    Attention = FeedForward = TimestepEmbedding = Timesteps = None
+
+from .configuration_vla_jepa import VLAJEPAConfig
+
+
+class SinusoidalPositionalEncoding(nn.Module):
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        timesteps = timesteps.float()
+        batch_size, seq_len = timesteps.shape
+        half_dim = self.embedding_dim // 2
+        exponent = -torch.arange(half_dim, dtype=torch.float, device=timesteps.device)
+        exponent = exponent * (torch.log(torch.tensor(10000.0, device=timesteps.device)) / max(half_dim, 1))
+        freqs = timesteps.unsqueeze(-1) * exponent.exp()
+        return torch.cat([torch.sin(freqs), torch.cos(freqs)], dim=-1).view(batch_size, seq_len, -1)
+
+
+class ActionEncoder(nn.Module):
+    def __init__(self, action_dim: int, hidden_size: int):
+        super().__init__()
+        self.layer1 = nn.Linear(action_dim, hidden_size)
+        self.layer2 = nn.Linear(hidden_size * 2, hidden_size)
+        self.layer3 = nn.Linear(hidden_size, hidden_size)
+        self.pos_encoding = SinusoidalPositionalEncoding(hidden_size)
+
+    def forward(self, actions: torch.Tensor, timesteps: torch.Tensor) -> torch.Tensor:
+        batch_size, seq_len, _ = actions.shape
+        if timesteps.ndim != 1 or timesteps.shape[0] != batch_size:
+            raise ValueError("timesteps must have shape [batch_size].")
+        timesteps = timesteps.unsqueeze(1).expand(-1, seq_len)
+        action_emb = self.layer1(actions)
+        time_emb = self.pos_encoding(timesteps).to(dtype=action_emb.dtype)
+        return self.layer3(F.silu(self.layer2(torch.cat([action_emb, time_emb], dim=-1))))
+
+
+class TimestepEncoder(nn.Module):
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        require_package("diffusers", extra="vla_jepa")
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
+        projected = self.time_proj(timesteps).to(dtype=next(self.parameters()).dtype)
+        return self.timestep_embedder(projected)
+
+
+class AdaLayerNorm(nn.Module):
+    def __init__(self, embedding_dim: int):
+        super().__init__()
+        self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
+        self.norm = nn.LayerNorm(embedding_dim, eps=1e-5, elementwise_affine=False)
+        self.silu = nn.SiLU()
+
+    def forward(self, x: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
+        scale, shift = self.linear(self.silu(temb)).chunk(2, dim=-1)
+        return self.norm(x) * (1 + scale[:, None]) + shift[:, None]
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout: float,
+        cross_attention_dim: int,
+        is_cross_attention: bool = True,
+    ) -> None:
+        super().__init__()
+        self.is_cross_attention = is_cross_attention
+        self.norm1 = AdaLayerNorm(dim)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=True,
+            cross_attention_dim=cross_attention_dim,
+            out_bias=True,
+        )
+        self.norm2 = nn.LayerNorm(dim, eps=1e-5, elementwise_affine=False)
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn="gelu-approximate", final_dropout=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None,
+        temb: torch.Tensor,
+    ) -> torch.Tensor:
+        attn_input = self.norm1(hidden_states, temb)
+        attention_context = encoder_hidden_states if self.is_cross_attention else None
+        hidden_states = hidden_states + self.attn1(attn_input, encoder_hidden_states=attention_context)
+        hidden_states = hidden_states + self.ff(self.norm2(hidden_states))
+        return hidden_states
+
+
+class DiT(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = False
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        output_dim: int,
+        num_layers: int,
+        dropout: float,
+        cross_attention_dim: int,
+    ) -> None:
+        super().__init__()
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.timestep_encoder = TimestepEncoder(self.inner_dim)
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim if layer_idx % 2 == 0 else self.inner_dim,
+                    is_cross_attention=layer_idx % 2 == 0,
+                )
+                for layer_idx in range(num_layers)
+            ]
+        )
+        self.norm_out = nn.LayerNorm(self.inner_dim, eps=1e-6, elementwise_affine=False)
+        self.proj_out_1 = nn.Linear(self.inner_dim, self.inner_dim * 2)
+        self.proj_out_2 = nn.Linear(self.inner_dim, output_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+    ) -> torch.Tensor:
+        temb = self.timestep_encoder(timestep)
+        x = hidden_states
+        for block in self.transformer_blocks:
+            x = block(x, encoder_hidden_states=encoder_hidden_states, temb=temb)
+        shift, scale = self.proj_out_1(F.silu(temb)).chunk(2, dim=-1)
+        x = self.norm_out(x) * (1 + scale[:, None]) + shift[:, None]
+        return self.proj_out_2(x)
+
+
+@dataclass
+class ActionModelPreset:
+    hidden_size: int
+    attention_head_dim: int
+    num_attention_heads: int
+
+
+DIT_PRESETS = {
+    "DiT-B": ActionModelPreset(hidden_size=768, attention_head_dim=64, num_attention_heads=12),
+    "DiT-L": ActionModelPreset(hidden_size=1536, attention_head_dim=48, num_attention_heads=32),
+    "DiT-test": ActionModelPreset(hidden_size=16, attention_head_dim=8, num_attention_heads=2),
+}
+
+
+class VLAJEPAActionHead(nn.Module):
+    def __init__(self, config: VLAJEPAConfig, cross_attention_dim: int) -> None:
+        super().__init__()
+        preset = DIT_PRESETS[config.action_model_type]
+        self.config = config
+        num_heads = config.action_num_heads or preset.num_attention_heads
+        head_dim = config.action_attention_head_dim or preset.attention_head_dim
+        inner_dim = num_heads * head_dim  # e.g. DiT-B: 12 × 64 = 768
+
+        self.input_embedding_dim = inner_dim
+        self.action_horizon = config.chunk_size
+        self.num_inference_timesteps = config.num_inference_timesteps
+
+        hidden_size = config.action_hidden_size
+        self.model = DiT(
+            num_attention_heads=num_heads,
+            attention_head_dim=head_dim,
+            output_dim=hidden_size,
+            num_layers=config.action_num_layers,
+            dropout=config.action_dropout,
+            cross_attention_dim=cross_attention_dim,
+        )
+        self.action_encoder = ActionEncoder(config.action_dim, inner_dim)
+        self.action_decoder = nn.Sequential(
+            OrderedDict(
+                [
+                    ("layer1", nn.Linear(hidden_size, hidden_size)),
+                    ("relu", nn.ReLU()),
+                    ("layer2", nn.Linear(hidden_size, config.action_dim)),
+                ]
+            )
+        )
+        self.state_encoder = (
+            nn.Sequential(
+                OrderedDict(
+                    [
+                        ("layer1", nn.Linear(config.state_dim, hidden_size)),
+                        ("relu", nn.ReLU()),
+                        ("layer2", nn.Linear(hidden_size, inner_dim)),
+                    ]
+                )
+            )
+            if config.state_dim > 0
+            else None
+        )
+        self.future_tokens = nn.Embedding(config.num_embodied_action_tokens_per_instruction, inner_dim)
+        self.position_embedding = nn.Embedding(
+            max(1024, config.chunk_size + config.num_action_tokens_per_timestep + 4),
+            inner_dim,
+        )
+        self.beta_dist = Beta(config.action_noise_beta_alpha, config.action_noise_beta_beta)
+
+    def sample_time(self, batch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        sample = self.beta_dist.sample([batch_size]).to(device=device, dtype=dtype)
+        return (self.config.action_noise_s - sample) / self.config.action_noise_s
+
+    def _build_inputs(
+        self,
+        conditioning_tokens: torch.Tensor,
+        actions: torch.Tensor,
+        state: torch.Tensor | None,
+        timesteps: torch.Tensor,
+    ) -> torch.Tensor:
+        action_features = self.action_encoder(actions, timesteps)
+        pos_ids = torch.arange(action_features.shape[1], device=actions.device)
+        action_features = action_features + self.position_embedding(pos_ids)[None]
+
+        future_tokens = self.future_tokens.weight.unsqueeze(0).expand(actions.shape[0], -1, -1)
+        seq = [future_tokens, action_features]
+        if state is not None and self.state_encoder is not None:
+            if state.ndim == 2:
+                state = state.unsqueeze(1)
+            seq.insert(0, self.state_encoder(state))
+        return torch.cat(seq, dim=1)
+
+    def forward(
+        self,
+        conditioning_tokens: torch.Tensor,
+        actions: torch.Tensor,
+        state: torch.Tensor | None = None,
+        action_is_pad: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        noise = torch.randn_like(actions)
+        t = self.sample_time(actions.shape[0], actions.device, actions.dtype)
+        noisy_actions = (1 - t[:, None, None]) * noise + t[:, None, None] * actions
+        velocity = actions - noise
+        t_discretized = (t * self.config.action_num_timestep_buckets).long()
+
+        hidden_states = self._build_inputs(conditioning_tokens, noisy_actions, state, t_discretized)
+        pred = self.model(
+            hidden_states=hidden_states,
+            encoder_hidden_states=conditioning_tokens,
+            timestep=t_discretized,
+        )
+        pred_actions = self.action_decoder(pred[:, -actions.shape[1] :])
+
+        if action_is_pad is None:
+            action_is_pad = torch.zeros(actions.shape[:2], dtype=torch.bool, device=actions.device)
+
+        loss = F.mse_loss(pred_actions, velocity, reduction="none")  # [B, T, action_dim]
+        valid_mask = ~action_is_pad.unsqueeze(-1)  # [B, T, 1]
+        num_valid = valid_mask.sum() * loss.shape[-1]
+        return (loss * valid_mask).sum() / num_valid.clamp_min(1)
+
+    @torch.no_grad()
+    def predict_action(
+        self,
+        conditioning_tokens: torch.Tensor,
+        state: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        batch_size = conditioning_tokens.shape[0]
+        actions = torch.randn(
+            batch_size,
+            self.action_horizon,
+            self.config.action_dim,
+            dtype=conditioning_tokens.dtype,
+            device=conditioning_tokens.device,
+        )
+        dt = 1.0 / max(self.num_inference_timesteps, 1)
+        for step in range(self.num_inference_timesteps):
+            t_cont = step / float(max(self.num_inference_timesteps, 1))
+            t_value = int(t_cont * self.config.action_num_timestep_buckets)
+            timesteps = torch.full(
+                (batch_size,), t_value, device=conditioning_tokens.device, dtype=torch.long
+            )
+            hidden_states = self._build_inputs(conditioning_tokens, actions, state, timesteps)
+            pred = self.model(
+                hidden_states=hidden_states,
+                encoder_hidden_states=conditioning_tokens,
+                timestep=timesteps,
+            )
+            pred_velocity = self.action_decoder(pred[:, -self.action_horizon :])
+            actions = actions + dt * pred_velocity
+        return actions
@@ -0,0 +1,154 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import NormalizationMode
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+
+
+@PreTrainedConfig.register_subclass("vla_jepa")
+@dataclass
+class VLAJEPAConfig(PreTrainedConfig):
+    n_obs_steps: int = 1
+    chunk_size: int = 7
+    n_action_steps: int = 7
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MIN_MAX,
+        }
+    )
+
+    qwen_model_name: str = "Qwen/Qwen3-VL-2B-Instruct"
+    jepa_encoder_name: str = "facebook/vjepa2-vitl-fpc64-256"
+    freeze_qwen: bool = False
+    enable_world_model: bool = True
+    # Enables cross-embodiment transfer: when fine-tuning a pretrained model on a robot with a
+    # different action or state dimensionality, the input/output projection layers must be
+    # re-initialised from scratch while the rest of the network keeps its pretrained weights.
+    # List the key prefixes that are allowed to have shape mismatches; anything else raises an error.
+    # e.g. ["model.action_model.action_encoder", "model.action_model.state_encoder"]
+    reinit_modules: list[str] | None = None
+
+    tokenizer_padding_side: str = "left"
+    prompt_template: str = "Your task is {instruction}. Infer the temporal dynamics from frames {actions} and produce the corresponding policy actions {e_actions}."
+    special_action_token: str = "<|action_{}|>"
+    embodied_action_token: str = "<|embodied_action|>"
+
+    action_dim: int = 7
+    state_dim: int = 8
+
+    num_action_tokens_per_timestep: int = 8
+    num_embodied_action_tokens_per_instruction: int = 32
+    num_inference_timesteps: int = 4
+
+    action_hidden_size: int = 1024
+    action_model_type: str = "DiT-B"
+    action_num_layers: int = 16
+    action_num_heads: int | None = None
+    action_attention_head_dim: int | None = None
+    action_dropout: float = 0.2
+    action_num_timestep_buckets: int = 1000
+    action_noise_beta_alpha: float = 1.5
+    action_noise_beta_beta: float = 1.0
+    action_noise_s: float = 0.999
+    num_target_vision_tokens: int = 32
+    action_max_seq_len: int = 1024
+
+    # total video frames loaded per sample
+    num_video_frames: int = 8
+    predictor_depth: int = 12
+    predictor_num_heads: int = 8
+    predictor_mlp_ratio: float = 4.0
+    predictor_dropout: float = 0.0
+    world_model_loss_weight: float = 0.1
+    jepa_tubelet_size: int = 2  # must match the encoder (e.g. 2 for vjepa2-vitl-fpc64-256)
+    repeated_diffusion_steps: int = 8  # independent noise draws per batch item (CogACT-style)
+
+    resize_images_to: tuple[int, int] | None = None
+    binarize_gripper_action: bool = True
+    pre_snap_gripper_action: bool = True
+    clip_normalized_actions: bool = True
+    gripper_dim: int = 6
+    gripper_threshold: float = 0.5
+    torch_dtype: str = "bfloat16"
+
+    optimizer_lr: float = 1e-4
+    optimizer_betas: tuple[float, float] = (0.9, 0.95)
+    optimizer_eps: float = 1e-8
+    optimizer_weight_decay: float = 1e-10
+    optimizer_grad_clip_norm: float = 10.0
+    scheduler_warmup_steps: int = 1_000
+    scheduler_decay_steps: int = 30_000
+    scheduler_decay_lr: float = 2.5e-6
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.freeze_qwen and self.enable_world_model:
+            # freezing qwen backbone makes world model training irrelevant since no grad flows
+            self.enable_world_model = False
+        if self.n_action_steps > self.chunk_size:
+            raise ValueError("`n_action_steps` must be <= `chunk_size`.")
+        if self.num_video_frames < 2 * self.jepa_tubelet_size:
+            raise ValueError(
+                f"`video_horizon` ({self.num_video_frames}) must be >= 2 * `jepa_tubelet_size` "
+                f"({self.jepa_tubelet_size}) to have at least one context and one GT temporal position."
+            )
+
+    def validate_features(self) -> None:
+        if not self.image_features:
+            raise ValueError("VLAJEPA requires at least one visual input feature.")
+        if self.action_feature is None:
+            raise ValueError("VLAJEPA requires an action output feature.")
+        self.action_dim = self.action_feature.shape[0]
+        if self.robot_state_feature is not None:
+            self.state_dim = self.robot_state_feature.shape[0]
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.optimizer_lr,
+            decay_lr=self.scheduler_decay_lr,
+            num_warmup_steps=self.scheduler_warmup_steps,
+            num_decay_steps=self.scheduler_decay_steps,
+        )
+
+    @property
+    def observation_delta_indices(self) -> list[int]:
+        # load video_horizon frames starting from current timestep: [t, t+1, ..., t+video_horizon-1]
+        # matches original repo's observation_indices=list(range(video_horizon))
+        return list(range(self.num_video_frames))
+
+    @property
+    def action_delta_indices(self) -> list[int]:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
@@ -0,0 +1,629 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import logging
+from collections import deque
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F  # noqa: N812
+from PIL import Image
+from torch import Tensor, nn
+
+from lerobot.policies.pretrained import PreTrainedPolicy, T
+from lerobot.policies.utils import populate_queues
+from lerobot.utils.constants import ACTION, OBS_STATE
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoModel, AutoVideoProcessor
+else:
+    AutoModel = None
+    AutoVideoProcessor = None
+
+from .action_head import VLAJEPAActionHead
+from .configuration_vla_jepa import VLAJEPAConfig
+from .qwen_interface import Qwen3VLInterface
+from .world_model import ActionConditionedVideoPredictor
+
+# ============================================================================
+# Native VLA-JEPA Model - follows original starVLA VLA_JEPA.py implementation
+# ============================================================================
+
+
+class VLAJEPAModel(nn.Module):
+    """
+    Native VLA-JEPA model following the original starVLA VLA_JEPA.py.
+
+    Components:
+      - Qwen3-VL: vision-language backbone for fused embeddings
+      - DiT-B: flow-matching action head for future action prediction
+      - V-JEPA: world model for video frame prediction
+
+    Input: List[dict] native format (same as original starVLA)
+      - "image": List[PIL.Image] (multi-view images)
+      - "video": np.ndarray [V, T, H, W, 3]
+      - "lang": str (task instruction)
+      - "action": np.ndarray [T, action_dim] (optional, training only)
+      - "state": np.ndarray [1, state_dim] (optional)
+    """
+
+    def __init__(self, config: VLAJEPAConfig) -> None:
+        super().__init__()
+        require_package("transformers", extra="vla_jepa")
+        self.config = config
+
+        # Vision-language backbone
+        self.qwen = Qwen3VLInterface(config)
+
+        # Tokenizer expansion for special action tokens
+        self.action_tokens, self.action_token_ids, self.embodied_action_token_id = (
+            self.qwen.expand_tokenizer()
+        )
+
+        # Action head (flow-matching DiT)
+        self.action_model = VLAJEPAActionHead(config, cross_attention_dim=self.qwen.model.config.hidden_size)
+
+        # JEPA world model components
+        if config.enable_world_model:
+            self.video_encoder = AutoModel.from_pretrained(
+                config.jepa_encoder_name,
+                torch_dtype=self.qwen._get_torch_dtype(config.torch_dtype),
+            )
+            self.video_processor = AutoVideoProcessor.from_pretrained(config.jepa_encoder_name)
+            num_views = config.jepa_tubelet_size
+            tubelet_size = self.video_encoder.config.tubelet_size
+            image_size = getattr(self.video_encoder.config, "image_size", None)
+            if image_size is None:
+                first_image_shape = next(iter(config.image_features.values())).shape
+                image_size = first_image_shape[-1]
+            self.video_predictor = ActionConditionedVideoPredictor(
+                num_frames=config.num_video_frames // tubelet_size,
+                img_size=(image_size, image_size),
+                patch_size=16,
+                tubelet_size=1,
+                embed_dim=self.video_encoder.config.hidden_size * num_views,
+                action_embed_dim=self.qwen.model.config.hidden_size,
+                predictor_embed_dim=self.video_encoder.config.hidden_size,
+                depth=config.predictor_depth,
+                num_heads=config.predictor_num_heads,
+                mlp_ratio=config.predictor_mlp_ratio,
+                num_action_tokens_per_step=config.num_action_tokens_per_timestep,
+            )
+        else:
+            self.video_encoder = None
+            self.video_processor = None
+            self.video_predictor = None
+
+        if config.freeze_qwen:
+            self.qwen.requires_grad_(False)
+
+        # Build prompt placeholders.
+        # Use the encoder's actual tubelet_size when available (world model enabled),
+        # otherwise fall back to config.
+        _tubelet_size = (
+            self.video_encoder.config.tubelet_size
+            if config.enable_world_model
+            else self.config.jepa_tubelet_size
+        )
+        num_action_prompt_steps = self.config.num_video_frames // _tubelet_size - 1
+        self.replace_prompt = "".join(
+            token * self.config.num_action_tokens_per_timestep
+            for token in self.action_tokens[:num_action_prompt_steps]
+        )
+        self.embodied_replace_prompt = (
+            self.config.embodied_action_token * self.config.num_embodied_action_tokens_per_instruction
+        )
+
+    def _qwen_last_decoder_hidden(self, qwen_inputs: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Return the last decoder hidden state before the final RMSNorm.
+
+        The model was trained with the output of the last transformer block BEFORE
+        the final RMSNorm. In transformers 5.x, `hidden_states[-1]` from
+        `output_hidden_states=True` is post-norm (tied to `last_hidden_state` via
+        `@capture_outputs`). A forward hook on `language_model.layers[-1]` recovers
+        the correct pre-RMSNorm state, matching the training-time representation.
+        """
+        captured: list[torch.Tensor] = []
+
+        def _hook(module, input, output):
+            h = output[0] if isinstance(output, tuple) else output
+            captured.append(h)
+
+        last_layer = self.qwen.model.model.language_model.layers[-1]
+        handle = last_layer.register_forward_hook(_hook)
+        try:
+            self.qwen.model(
+                **qwen_inputs,
+                output_hidden_states=False,
+                output_attentions=False,
+                return_dict=True,
+            )
+        finally:
+            handle.remove()
+
+        return captured[0]  # [B, seq_len, H]
+
+    # ---- Native VLA-JEPA forward (follows original VLA_JEPA.py) ----
+
+    def forward(self, examples: list[dict]) -> dict[str, Tensor]:
+        """
+        Native forward pass following original starVLA VLA_JEPA.forward.
+
+        Args:
+            examples: List of per-sample dicts with keys:
+                "image"  : List[PIL.Image]  — multi-view images
+                "video"  : np.ndarray [V, T, H, W, 3]
+                "lang"   : str — task instruction
+                "action" : np.ndarray [T, action_dim] (optional)
+                "state"  : np.ndarray [1, state_dim] (optional)
+
+        Returns:
+            dict with "action_loss" and "wm_loss" keys (scalar Tensors).
+        """
+        # Unpack native format (same pattern as original VLA_JEPA.py)
+        batch_images = [ex["image"] for ex in examples]  # List[List[PIL.Image]]
+        batch_videos = [ex["video"] for ex in examples]  # List[np.ndarray]
+        instructions = [ex["lang"] for ex in examples]  # List[str]
+        has_action = "action" in examples[0] and examples[0]["action"] is not None
+        actions = [ex["action"] for ex in examples] if has_action else None
+        has_state = "state" in examples[0] and examples[0]["state"] is not None
+        state = [ex["state"] for ex in examples] if has_state else None
+        action_is_pad = (
+            [ex["action_is_pad"] for ex in examples]
+            if has_action and "action_is_pad" in examples[0] and examples[0]["action_is_pad"] is not None
+            else None
+        )
+
+        # Stack videos: [B, V, T, H, W, 3] -> [B, V, T, 3, H, W]
+        batch_videos = np.stack(batch_videos)
+        batch_videos = batch_videos.transpose(0, 1, 2, 5, 3, 4)  # [B, V, T, 3, H, W]
+
+        # Adjust number of views for the world model:
+        # - fewer views than expected: duplicate the first view to fill up
+        # - more views than expected: keep only the first num_views_world_model views
+        num_views_world_model = self.config.jepa_tubelet_size
+        if batch_videos.shape[1] < num_views_world_model:
+            num_missing_views = num_views_world_model - batch_videos.shape[1]
+            first_view = np.repeat(batch_videos[:, :1], num_missing_views, axis=1)
+            batch_videos = np.concatenate([batch_videos, first_view], axis=1)
+        elif batch_videos.shape[1] > num_views_world_model:
+            batch_videos = batch_videos[:, :num_views_world_model]
+
+        # ---- Step 1: QwenVL encode (same as original) ----
+        qwen_inputs = self.qwen.build_inputs(
+            images=batch_images,
+            instructions=instructions,
+            action_prompt=self.replace_prompt,
+            embodied_prompt=self.embodied_replace_prompt,
+        )
+
+        # Locate embodied-action tokens (always needed for action head)
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        # Locate action tokens (only needed for world model predictor)
+        if self.config.enable_world_model:
+            action_mask = torch.isin(
+                qwen_inputs["input_ids"],
+                torch.tensor(self.action_token_ids, device=qwen_inputs["input_ids"].device),
+            )
+            action_indices = action_mask.nonzero(as_tuple=True)
+
+        device_type = next(self.parameters()).device.type
+
+        with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
+            last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
+            b, _, h = last_hidden.shape
+
+            if self.config.enable_world_model:
+                action_tokens = last_hidden[action_indices[0], action_indices[1], :].view(b, -1, h)
+
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
+
+        # ---- Step 2+3: JEPA Encoder + Predictor ----
+        device_wm = last_hidden.device
+        if not self.config.enable_world_model:
+            wm_loss = torch.tensor(0.0, device=device_wm)
+        else:
+            b, v, t_frames, c, h_img, w_img = batch_videos.shape
+            batch_videos_flat = batch_videos.reshape(b * v, t_frames, c, h_img, w_img)
+
+            video_pixels = self.video_processor(videos=list(batch_videos_flat), return_tensors="pt")[
+                "pixel_values_videos"
+            ].to(self.video_encoder.device)  # [B*V, T, C, H, W]
+
+            with torch.no_grad():
+                video_embeddings = self.video_encoder.get_vision_features(pixel_values_videos=video_pixels)
+                # Merge views: [B*V, ...] -> [B, ..., V*embed_dim]
+                video_embeddings = torch.cat(torch.chunk(video_embeddings, chunks=v, dim=0), dim=2)
+
+            tubelet_size = self.video_encoder.config.tubelet_size
+            device_wm = video_embeddings.device
+            # num_video_frames raw frames → t_enc_total temporal positions after tubelet compression
+            t_enc_total = self.config.num_video_frames // tubelet_size
+
+            if t_enc_total < 2:
+                wm_loss = torch.tensor(0.0, device=device_wm)
+            else:
+                # Shift-by-one JEPA split (matches original VLA_JEPA.py lines 231-232):
+                # input_states: positions 0..T-2, gt_states: positions 1..T-1
+                t_enc_ctx = t_enc_total - 1
+                tokens_per_frame = video_embeddings.shape[1] // t_enc_total
+
+                input_states = video_embeddings[:, : tokens_per_frame * t_enc_ctx, :]
+                gt_states = video_embeddings[:, tokens_per_frame:, :]
+
+                expected_actions = t_enc_ctx * self.config.num_action_tokens_per_timestep
+                if action_tokens.shape[1] < expected_actions:
+                    pad = action_tokens[:, -1:].repeat(1, expected_actions - action_tokens.shape[1], 1)
+                    action_tokens = torch.cat([action_tokens, pad], dim=1)
+
+                predicted_states = self.video_predictor(
+                    input_states.float(),
+                    action_tokens[:, :expected_actions].float(),
+                )
+
+                wm_loss = F.l1_loss(predicted_states, gt_states.float(), reduction="mean")
+
+        if not has_action:
+            return {"wm_loss": wm_loss}
+
+        # ---- Step 4: Action Head ----
+        with torch.autocast(device_type=device_type, dtype=torch.float32):
+            actions_tensor = torch.tensor(
+                np.array(actions), device=last_hidden.device, dtype=torch.float32
+            )  # [B, T_full, action_dim]
+            action_horizon = self.config.chunk_size
+            actions_target = actions_tensor[:, -action_horizon:, :]
+
+            state_tensor = None
+            if state is not None:
+                state_tensor = torch.tensor(
+                    np.array(state), device=last_hidden.device, dtype=last_hidden.dtype
+                )  # [B, 1, state_dim]
+
+            repeated_diffusion_steps = self.config.repeated_diffusion_steps
+            actions_target = actions_target.repeat(repeated_diffusion_steps, 1, 1)
+            embodied_action_tokens = embodied_action_tokens.repeat(repeated_diffusion_steps, 1, 1)
+            if state_tensor is not None:
+                state_tensor = state_tensor.repeat(repeated_diffusion_steps, 1, 1)
+
+            action_is_pad_rep = None
+            if action_is_pad is not None:
+                pad_tensor = torch.stack(
+                    [
+                        p.to(actions_target.device)
+                        if isinstance(p, Tensor)
+                        else torch.tensor(p, device=actions_target.device)
+                        for p in action_is_pad
+                    ]
+                )  # [B, T_full]
+                pad_tensor = pad_tensor[:, -action_horizon:]  # [B, action_horizon]
+                action_is_pad_rep = pad_tensor.repeat(repeated_diffusion_steps, 1)  # [B*R, action_horizon]
+
+            action_loss = self.action_model(
+                embodied_action_tokens, actions_target, state_tensor, action_is_pad_rep
+            )
+
+        return {"action_loss": action_loss, "wm_loss": wm_loss * self.config.world_model_loss_weight}
+
+    # ---- Native predict_action (follows original VLA_JEPA.predict_action) ----
+
+    @torch.no_grad()
+    def predict_action(
+        self,
+        batch_images: list[list[Image.Image]],
+        instructions: list[str],
+        state: np.ndarray | None = None,
+    ) -> np.ndarray:
+        """
+        Native action prediction following original VLA_JEPA.predict_action.
+
+        Args:
+            batch_images: List of samples; each is List[PIL.Image] (multi-view).
+            instructions: Task instructions, one per sample.
+            state: Optional [B, state_dim] numpy array.
+
+        Returns:
+            np.ndarray [B, action_horizon, action_dim] — predicted actions.
+        """
+        if self.config.resize_images_to is not None:
+            height, width = self.config.resize_images_to
+            resampling = getattr(Image, "Resampling", Image).BOX
+            batch_images = [
+                [image.resize((width, height), resample=resampling) for image in sample_images]
+                for sample_images in batch_images
+            ]
+
+        qwen_inputs = self.qwen.build_inputs(
+            images=batch_images,
+            instructions=instructions,
+            action_prompt=self.replace_prompt,
+            embodied_prompt=self.embodied_replace_prompt,
+        )
+
+        embodied_mask = qwen_inputs["input_ids"] == self.embodied_action_token_id
+        embodied_indices = embodied_mask.nonzero(as_tuple=True)
+
+        device_type = next(self.parameters()).device.type
+
+        with torch.autocast(device_type=device_type, dtype=torch.bfloat16):
+            last_hidden = self._qwen_last_decoder_hidden(qwen_inputs)  # [B, seq_len, H]
+            b, _, h = last_hidden.shape
+            embodied_action_tokens = last_hidden[embodied_indices[0], embodied_indices[1], :].view(b, -1, h)
+
+        state_tensor = None
+        if state is not None:
+            state_tensor = torch.from_numpy(np.array(state)).to(
+                device=last_hidden.device, dtype=last_hidden.dtype
+            )
+
+        pred_actions = self.action_model.predict_action(
+            embodied_action_tokens.float(), state_tensor.float() if state_tensor is not None else None
+        )  # [B, action_horizon, action_dim]
+
+        return pred_actions.detach().cpu().numpy()
+
+
+# ============================================================================
+# LeRobot Adapter Layer - converts between LeRobot batch format and native VLA-JEPA format
+# ============================================================================
+
+
+class VLAJEPAPolicy(PreTrainedPolicy):
+    """
+    LeRobot adapter for VLA-JEPA.
+
+    Converts LeRobot's standard batch format (dict[str, Tensor]) to the native
+    VLA-JEPA format (List[dict]), calls the native model, and converts outputs
+    back to LeRobot format.
+    """
+
+    config_class = VLAJEPAConfig
+    name = "vla_jepa"
+
+    def __init__(self, config: VLAJEPAConfig, **kwargs) -> None:
+        super().__init__(config)
+        config.validate_features()
+        if dataset_meta := kwargs.get("dataset_meta"):
+            # cfg.input_features keeps the pretrained model's feature keys (needed for rename_map
+            # compatibility), so validate_features() may have read stale dims from a pretrained
+            # config. Override state_dim/action_dim from the actual dataset being used.
+            ds_features = dataset_meta.features
+            if OBS_STATE in ds_features:
+                config.state_dim = ds_features[OBS_STATE]["shape"][0]
+            if ACTION in ds_features:
+                config.action_dim = ds_features[ACTION]["shape"][0]
+
+        self.model = VLAJEPAModel(config)
+        self.reset()
+
+    def reset(self) -> None:
+        self._queues = {ACTION: deque(maxlen=self.config.n_action_steps)}
+
+    # ---- Format Conversion: LeRobot → Native ----
+
+    def _prepare_model_inputs(self, batch: dict[str, Tensor]) -> list[dict]:
+        """
+        Convert LeRobot batch format to native VLA-JEPA examples format.
+
+        LeRobot format:
+            batch = {
+                "observation.images.<key>": Tensor [B, C, H, W] or [B, T, C, H, W],
+                "observation.state": Tensor [B, state_dim] or [B, T, state_dim],
+                "action": Tensor [B, chunk_size, action_dim],  (training only)
+                "task": str | List[str],  (optional instruction)
+            }
+
+        Native format (List[dict]):
+            {
+                "image": List[PIL.Image],       # multi-view images per sample
+                "video": np.ndarray [V, T, H, W, 3],
+                "lang": str,                     # task instruction
+                "action": np.ndarray [T, action_dim],  # optional
+                "state": np.ndarray [1, state_dim],    # optional
+            }
+        """
+        # Determine batch size from the first image feature
+        image_keys = list(self.config.image_features.keys())
+        if not image_keys:
+            raise ValueError("VLAJEPA requires at least one image feature.")
+        first_key = image_keys[0]
+        first_tensor = batch[first_key]
+        batch_size = first_tensor.shape[0]
+
+        # ---- Collect images per sample ----
+        # images_per_sample[b][v] = PIL.Image for view v
+        images_per_sample: list[list[Image.Image]] = [[] for _ in range(batch_size)]
+        for key in image_keys:
+            tensor = batch[key]  # [B, C, H, W] or [B, T, C, H, W]
+            if tensor.ndim == 5:
+                # observation_delta_indices = [0, 1, ..., num_video_frames-1]
+                # index 0 is the current observation (delta=0)
+                tensor = tensor[:, 0]
+            for b in range(batch_size):
+                images_per_sample[b].append(self.model.qwen.tensor_to_pil(tensor[b]))
+
+        # ---- Collect videos per sample ----
+        # Build video arrays: for each sample, stack views as [V, T, H, W, 3]
+        # Check whether any image feature has a time dimension
+        video_source = None
+        for k in image_keys:
+            if k in batch:
+                video_source = batch[k]  # Use first available for shape inspection
+                break
+
+        if video_source is None:
+            raise ValueError("No image data found in batch for video construction.")
+
+        videos_per_sample = []
+        for b in range(batch_size):
+            sample_views = []
+            for k in image_keys:
+                t = batch[k][b]  # [C, H, W] or [T, C, H, W]
+                if t.ndim == 3:
+                    t = t.unsqueeze(0)  # [1, C, H, W]
+                # Convert to [T, H, W, 3] numpy
+                t_np = t.permute(0, 2, 3, 1).detach().cpu().float().numpy()
+                # Clamp to [0, 255]
+                if t_np.max() <= 1.0:
+                    t_np = t_np * 255.0
+                t_np = np.rint(t_np.clip(0, 255)).astype(np.uint8)
+                sample_views.append(t_np)
+            # Stack views: [V, T, H, W, 3]
+            videos_per_sample.append(np.stack(sample_views, axis=0))
+
+        # ---- Collect instructions ----
+        tasks = batch.get("task")
+        if tasks is None:
+            instructions = ["Execute the robot action."] * batch_size
+        elif isinstance(tasks, str):
+            instructions = [tasks] * batch_size
+        else:
+            instructions = list(tasks)
+
+        # ---- Collect actions (training only) ----
+        actions_list = None
+        action_is_pad_list = None
+        actions_tensor = batch.get(ACTION)
+        if actions_tensor is not None:
+            if actions_tensor.ndim == 2:
+                actions_tensor = actions_tensor.unsqueeze(1)
+            actions_list = [actions_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
+            action_is_pad_tensor = batch.get("action_is_pad")
+            if action_is_pad_tensor is not None:
+                action_is_pad_list = [action_is_pad_tensor[b].detach().cpu() for b in range(batch_size)]
+
+        # ---- Collect state ----
+        state_list = None
+        state_tensor = batch.get(OBS_STATE)
+        if state_tensor is not None:
+            if state_tensor.ndim > 2:
+                state_tensor = state_tensor[:, -1, :]
+            if state_tensor.ndim == 2:
+                state_tensor = state_tensor.unsqueeze(1)  # [B, 1, state_dim]
+            state_list = [state_tensor[b].detach().cpu().float().numpy() for b in range(batch_size)]
+
+        # ---- Assemble native examples ----
+        examples = []
+        for b in range(batch_size):
+            example = {
+                "image": images_per_sample[b],
+                "video": videos_per_sample[b],
+                "lang": instructions[b],
+            }
+            if actions_list is not None:
+                example["action"] = actions_list[b]
+            if action_is_pad_list is not None:
+                example["action_is_pad"] = action_is_pad_list[b]
+            if state_list is not None:
+                example["state"] = state_list[b]
+            examples.append(example)
+
+        return examples
+
+    # ---- LeRobot Policy Interface ----
+
+    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
+        """LeRobot train forward: convert → native forward → aggregate losses."""
+        examples = self._prepare_model_inputs(batch)
+        native_output = self.model.forward(examples)
+
+        ref = next(iter(native_output.values()))
+        zero = torch.zeros((), device=ref.device, dtype=ref.dtype)
+        total_loss = native_output.get("action_loss", zero) + native_output.get("wm_loss", zero)
+        logs = {k: v.detach().item() for k, v in native_output.items()}
+        logs["loss"] = total_loss.detach().item()
+        return total_loss, logs
+
+    def get_optim_params(self) -> dict:
+        return self.model.parameters()
+
+    @torch.no_grad()
+    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+        """LeRobot inference: convert → native predict → return as Tensor."""
+        self.eval()
+        self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
+
+        examples = self._prepare_model_inputs(batch)
+        batch_images = [ex["image"] for ex in examples]
+        instructions = [ex["lang"] for ex in examples]
+
+        state_np = None
+        if "state" in examples[0] and examples[0]["state"] is not None:
+            state_np = np.stack([ex["state"] for ex in examples])
+
+        actions_np = self.model.predict_action(batch_images, instructions, state_np)
+        return torch.from_numpy(actions_np).to(device=self.config.device, dtype=torch.float32)
+
+    @torch.no_grad()
+    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+        """LeRobot select_action with action queue caching."""
+        self.eval()
+        self._queues = populate_queues(self._queues, batch, exclude_keys=[ACTION])
+        if len(self._queues[ACTION]) == 0:
+            actions = self.predict_action_chunk(batch)
+            self._queues[ACTION].extend(actions.transpose(0, 1)[: self.config.n_action_steps])
+        return self._queues[ACTION].popleft()
+
+    @classmethod
+    def from_pretrained(
+        cls: type[T],
+        pretrained_name_or_path: str | Path,
+        **kwargs,
+    ):
+        return super().from_pretrained(pretrained_name_or_path, **kwargs)
+
+    @classmethod
+    def _load_as_safetensor(cls, model: T, model_file: str, map_location: str, strict: bool) -> T:
+        reinit_prefixes = model.config.reinit_modules
+        if not reinit_prefixes:
+            return super()._load_as_safetensor(model, model_file, map_location, strict)
+
+        from safetensors.torch import load_file
+
+        state_dict = load_file(model_file, device=map_location)
+        current = model.state_dict()
+
+        reinitialized: list[str] = []
+        filtered: dict = {}
+        for key, value in state_dict.items():
+            if key in current and value.shape != current[key].shape:
+                if not any(key.startswith(p) for p in reinit_prefixes):
+                    raise ValueError(
+                        f"Shape mismatch for '{key}' (checkpoint {tuple(value.shape)} vs model "
+                        f"{tuple(current[key].shape)}) and its prefix is not in `reinit_modules`."
+                    )
+                reinitialized.append(
+                    f"{key}: checkpoint {tuple(value.shape)} → model {tuple(current[key].shape)}"
+                )
+            else:
+                filtered[key] = value
+
+        if reinitialized:
+            logging.warning(
+                f"reinit_modules: skipping {len(reinitialized)} tensor(s) with mismatched shapes "
+                f"(randomly re-initialised):\n  " + "\n  ".join(reinitialized)
+            )
+
+        from lerobot.policies.utils import log_model_loading_keys
+
+        missing_keys, unexpected_keys = model.load_state_dict(filtered, strict=False)
+        log_model_loading_keys(missing_keys, unexpected_keys)
+        return model
@@ -0,0 +1,155 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+
+from lerobot.policies.vla_jepa.configuration_vla_jepa import VLAJEPAConfig
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    EnvTransition,
+    NormalizerProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    RenameObservationsProcessorStep,
+    TransitionKey,
+    UnnormalizerProcessorStep,
+)
+from lerobot.processor.converters import policy_action_to_transition, transition_to_policy_action
+from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
+
+
+@ProcessorStepRegistry.register(name="vla_jepa_clip_actions")
+class ClipActionsProcessorStep(ProcessorStep):
+    """Clips action tensor to [-1, 1] before unnormalization."""
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition.get(TransitionKey.ACTION)
+        if action is not None:
+            transition = dict(transition)
+            transition[TransitionKey.ACTION] = action.clamp(-1.0, 1.0)
+        return transition
+
+    def transform_features(self, features):
+        return features
+
+
+@ProcessorStepRegistry.register(name="vla_jepa_pre_snap_gripper")
+class PreSnapGripperProcessorStep(ProcessorStep):
+    """Snaps a gripper dimension to {0, 1} BEFORE unnormalization.
+
+    Mirrors the original starVLA LIBERO eval:
+      normalized[:, gripper_dim] = np.where(normalized[:, gripper_dim] < threshold, 0, 1)
+    This ensures the unnormalizer receives an exact binary value, which is
+    required when the model was trained with gripper in identity (mask=False)
+    space where 0=open and 1=close.
+    """
+
+    def __init__(self, gripper_dim: int = 6, threshold: float = 0.5):
+        self.gripper_dim = gripper_dim
+        self.threshold = threshold
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition.get(TransitionKey.ACTION)
+        if action is not None and action.shape[-1] > self.gripper_dim:
+            transition = dict(transition)
+            a = action.clone()
+            a[..., self.gripper_dim] = (a[..., self.gripper_dim] >= self.threshold).float()
+            transition[TransitionKey.ACTION] = a
+        return transition
+
+    def transform_features(self, features):
+        return features
+
+
+@ProcessorStepRegistry.register(name="vla_jepa_binarize_gripper")
+class BinarizeGripperProcessorStep(ProcessorStep):
+    """Binarizes a gripper dimension after unnormalization.
+
+    Maps continuous value to {-1, 1}: > threshold → -1, <= threshold → 1 (matches starVLA convention).
+    Only applied when action has more dimensions than gripper_dim.
+    """
+
+    def __init__(self, gripper_dim: int = 6, threshold: float = 0.5):
+        self.gripper_dim = gripper_dim
+        self.threshold = threshold
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        action = transition.get(TransitionKey.ACTION)
+        if action is not None and action.shape[-1] > self.gripper_dim:
+            transition = dict(transition)
+            a = action.clone()
+            a[..., self.gripper_dim] = 1.0 - 2.0 * (a[..., self.gripper_dim] > self.threshold).float()
+            transition[TransitionKey.ACTION] = a
+        return transition
+
+    def transform_features(self, features):
+        return features
+
+
+def make_vla_jepa_pre_post_processors(
+    config: VLAJEPAConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    features = {**config.input_features, **config.output_features}
+    input_steps = [
+        RenameObservationsProcessorStep(rename_map={}),
+        AddBatchDimensionProcessorStep(),
+        DeviceProcessorStep(device=config.device),
+        NormalizerProcessorStep(
+            features=features,
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        ),
+    ]
+    output_steps: list[ProcessorStep] = []
+    if config.clip_normalized_actions:
+        output_steps.append(ClipActionsProcessorStep())
+    if config.pre_snap_gripper_action:
+        output_steps.append(
+            PreSnapGripperProcessorStep(gripper_dim=config.gripper_dim, threshold=config.gripper_threshold)
+        )
+    output_steps.append(
+        UnnormalizerProcessorStep(
+            features=features,
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        )
+    )
+    if config.binarize_gripper_action:
+        output_steps.append(
+            BinarizeGripperProcessorStep(gripper_dim=config.gripper_dim, threshold=config.gripper_threshold)
+        )
+    output_steps.append(DeviceProcessorStep(device="cpu"))
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=input_steps,
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=output_steps,
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )
@@ -0,0 +1,117 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+from PIL import Image
+
+from lerobot.utils.import_utils import _transformers_available
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+else:
+    AutoProcessor = None
+    Qwen3VLForConditionalGeneration = None
+
+from .configuration_vla_jepa import VLAJEPAConfig
+
+
+class Qwen3VLInterface(torch.nn.Module):
+    def __init__(self, config: VLAJEPAConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.model = Qwen3VLForConditionalGeneration.from_pretrained(
+            config.qwen_model_name,
+            torch_dtype=self._get_torch_dtype(config.torch_dtype),
+        )
+        self.processor = AutoProcessor.from_pretrained(config.qwen_model_name)
+        self.processor.tokenizer.padding_side = config.tokenizer_padding_side
+        self.model.config.hidden_size = self.model.config.text_config.hidden_size
+
+    @staticmethod
+    def _get_torch_dtype(dtype_name: str) -> torch.dtype:
+        if dtype_name == "float32":
+            return torch.float32
+        if dtype_name == "float16":
+            return torch.float16
+        return torch.bfloat16
+
+    def expand_tokenizer(self) -> tuple[list[str], list[int], int]:
+        # starVLA/JEVLA checkpoints expand action tokens as action_horizon * 4,
+        # independent of vj2 num_action_tokens_per_timestep. Keeping this count
+        # is required for Qwen embedding/lm_head checkpoint shapes to match.
+        max_action_tokens = self.config.chunk_size * 4
+        tokenizer = self.processor.tokenizer
+        action_tokens = []
+        action_token_ids = []
+        for idx in range(max_action_tokens):
+            token = self.config.special_action_token.format(idx)
+            action_tokens.append(token)
+            if token not in tokenizer.get_vocab():
+                tokenizer.add_tokens([token], special_tokens=True)
+            action_token_ids.append(tokenizer.convert_tokens_to_ids(token))
+
+        embodied_action_token = self.config.embodied_action_token
+        if embodied_action_token not in tokenizer.get_vocab():
+            tokenizer.add_tokens([embodied_action_token], special_tokens=True)
+        embodied_action_token_id = tokenizer.convert_tokens_to_ids(embodied_action_token)
+
+        if self.model.get_input_embeddings().weight.size(0) < len(tokenizer):
+            self.model.resize_token_embeddings(len(tokenizer))
+        return action_tokens, action_token_ids, embodied_action_token_id
+
+    def build_inputs(
+        self,
+        images: Sequence[Sequence[Image.Image]],
+        instructions: Sequence[str],
+        action_prompt: str,
+        embodied_prompt: str,
+    ) -> dict[str, torch.Tensor]:
+        messages = []
+        for sample_images, instruction in zip(images, instructions, strict=True):
+            prompt = self.config.prompt_template.format(
+                instruction=instruction,
+                actions=action_prompt,
+                e_actions=embodied_prompt,
+            )
+            content = [{"type": "image", "image": img} for img in sample_images]
+            content.append({"type": "text", "text": prompt})
+            messages.append([{"role": "user", "content": content}])
+
+        batch_inputs = self.processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            processor_kwargs={"padding": True, "return_tensors": "pt"},
+        )
+        return batch_inputs.to(self.model.device)
+
+    @staticmethod
+    def tensor_to_pil(image_tensor: torch.Tensor) -> Image.Image:
+        image = image_tensor.detach().cpu()
+        if image.ndim == 3 and image.shape[0] in (1, 3):
+            image = image.permute(1, 2, 0)
+        image = image.float()
+        if image.max() <= 1.0:
+            image = image * 255.0
+        image = image.clamp(0, 255).round().to(torch.uint8).numpy()
+        if image.shape[-1] == 1:
+            image = np.repeat(image, 3, axis=-1)
+        return Image.fromarray(image)
@@ -0,0 +1,418 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from torch import nn
+
+
+def build_action_block_causal_attention_mask(
+    num_frames: int, grid_height: int, grid_width: int, add_tokens: int = 1
+) -> torch.Tensor:
+    tokens_per_frame = add_tokens + grid_height * grid_width
+    num_tokens = num_frames * tokens_per_frame
+    mask = torch.zeros(num_tokens, num_tokens, dtype=torch.bool)
+    mask_block = torch.ones(tokens_per_frame, tokens_per_frame, dtype=torch.bool)
+    local_window_time = num_frames
+
+    for current_frame in range(num_frames):
+        first_context_frame = max(0, current_frame - local_window_time + 1)
+        for context_frame in range(first_context_frame, current_frame + 1):
+            row = slice(current_frame * tokens_per_frame, (current_frame + 1) * tokens_per_frame)
+            col = slice(context_frame * tokens_per_frame, (context_frame + 1) * tokens_per_frame)
+            mask[row, col] = mask_block
+    return mask
+
+
+def rotate_queries_or_keys(x: torch.Tensor, pos: torch.Tensor) -> torch.Tensor:
+    _, _, _, dim = x.size()
+    if dim % 2 != 0:
+        raise ValueError("Embedding dimension must be even for rotary position encoding.")
+
+    omega = torch.arange(dim // 2, dtype=x.dtype, device=x.device)
+    omega /= dim / 2.0
+    omega = 1.0 / 10000**omega
+    freqs = torch.einsum("..., f -> ... f", pos, omega)
+    emb_sin = freqs.sin().squeeze(-1).repeat(1, 1, 1, 2)
+    emb_cos = freqs.cos().squeeze(-1).repeat(1, 1, 1, 2)
+
+    y = x.unflatten(-1, (-1, 2))
+    y1, y2 = y.unbind(dim=-1)
+    y = torch.stack((-y2, y1), dim=-1).flatten(-2)
+    return x * emb_cos + y * emb_sin
+
+
+class DropPath(nn.Module):
+    def __init__(self, drop_prob: float = 0.0) -> None:
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.drop_prob == 0.0 or not self.training:
+            return x
+        keep_prob = 1 - self.drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+        random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+        random_tensor.floor_()
+        return x.div(keep_prob) * random_tensor
+
+
+class MLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int | None = None,
+        out_features: int | None = None,
+        act_layer: type[nn.Module] = nn.GELU,
+        drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class ACRoPEAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: float | None = None,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        use_sdpa: bool = True,
+        is_causal: bool = False,
+        grid_size: int = 16,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = qk_scale or self.head_dim**-0.5
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop_prob = proj_drop
+        self.proj_drop = nn.Dropout(proj_drop)
+        self.use_sdpa = use_sdpa
+        self.d_dim = int(2 * ((self.head_dim // 3) // 2))
+        self.h_dim = int(2 * ((self.head_dim // 3) // 2))
+        self.w_dim = int(2 * ((self.head_dim // 3) // 2))
+        self.grid_size = grid_size
+        self.is_causal = is_causal
+
+    @staticmethod
+    def _get_frame_pos(ids: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        return ids // int(height * width)
+
+    def _get_height_pos(self, ids: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        frame_ids = self._get_frame_pos(ids, height, width)
+        ids = ids - int(height * width) * frame_ids
+        return ids // width
+
+    def separate_positions(
+        self, ids: torch.Tensor, height: int, width: int
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        frame_ids = self._get_frame_pos(ids, height, width)
+        height_ids = self._get_height_pos(ids, height, width)
+        width_ids = ids - int(height * width) * frame_ids - width * height_ids
+        return 1.0 * frame_ids, 1.0 * height_ids, 1.0 * width_ids
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: torch.Tensor | None = None,
+        attn_mask: torch.Tensor | None = None,
+        num_frames: int | None = None,
+        grid_height: int | None = None,
+        grid_width: int | None = None,
+        action_tokens: int = 0,
+    ) -> torch.Tensor:
+        batch_size, num_tokens, channels = x.size()
+        if num_frames is None or grid_height is None or grid_width is None:
+            raise ValueError("num_frames, grid_height and grid_width are required.")
+
+        if mask is not None:
+            mask = mask.unsqueeze(1).repeat(1, self.num_heads, 1)
+            d_mask, h_mask, w_mask = self.separate_positions(mask, grid_height, grid_width)
+        else:
+            mask = torch.arange(int(num_frames * grid_height * grid_width), device=x.device)
+            d_mask, h_mask, w_mask = self.separate_positions(mask, grid_height, grid_width)
+
+        h_mask *= self.grid_size / grid_height
+        w_mask *= self.grid_size / grid_width
+
+        if action_tokens > 0:
+            x = x.view(batch_size, -1, action_tokens + grid_height * grid_width, channels)
+            action_q, action_k, action_v = [], [], []
+            for idx in range(action_tokens):
+                action_token = x[:, :, idx : idx + 1, :].flatten(1, 2)
+                qkv = self.qkv(action_token).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
+                q, k, v = qkv[0], qkv[1], qkv[2]
+                qd = rotate_queries_or_keys(
+                    q[..., : self.d_dim], pos=torch.arange(num_frames, device=x.device)
+                )
+                kd = rotate_queries_or_keys(
+                    k[..., : self.d_dim], pos=torch.arange(num_frames, device=x.device)
+                )
+                qr = q[..., self.d_dim :]
+                kr = k[..., self.d_dim :]
+                action_q.append(
+                    torch.cat([qd, qr], dim=-1).view(batch_size, self.num_heads, num_frames, 1, -1)
+                )
+                action_k.append(
+                    torch.cat([kd, kr], dim=-1).view(batch_size, self.num_heads, num_frames, 1, -1)
+                )
+                action_v.append(v.view(batch_size, self.num_heads, num_frames, 1, -1))
+
+            action_q = torch.cat(action_q, dim=3).flatten(2, 3)
+            action_k = torch.cat(action_k, dim=3).flatten(2, 3)
+            action_v = torch.cat(action_v, dim=3).flatten(2, 3)
+            x = x[:, :, action_tokens:, :].flatten(1, 2)
+
+        qkv = self.qkv(x).unflatten(-1, (3, self.num_heads, -1)).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        offset = 0
+        qd = rotate_queries_or_keys(q[..., offset : offset + self.d_dim], pos=d_mask)
+        kd = rotate_queries_or_keys(k[..., offset : offset + self.d_dim], pos=d_mask)
+        offset += self.d_dim
+        qh = rotate_queries_or_keys(q[..., offset : offset + self.h_dim], pos=h_mask)
+        kh = rotate_queries_or_keys(k[..., offset : offset + self.h_dim], pos=h_mask)
+        offset += self.h_dim
+        qw = rotate_queries_or_keys(q[..., offset : offset + self.w_dim], pos=w_mask)
+        kw = rotate_queries_or_keys(k[..., offset : offset + self.w_dim], pos=w_mask)
+        offset += self.w_dim
+
+        if offset < self.head_dim:
+            q = torch.cat([qd, qh, qw, q[..., offset:]], dim=-1)
+            k = torch.cat([kd, kh, kw, k[..., offset:]], dim=-1)
+        else:
+            q = torch.cat([qd, qh, qw], dim=-1)
+            k = torch.cat([kd, kh, kw], dim=-1)
+
+        if action_tokens > 0:
+
+            def merge(frame_tokens: torch.Tensor, action_token_values: torch.Tensor) -> torch.Tensor:
+                frame_tokens = frame_tokens.view(
+                    batch_size, self.num_heads, num_frames, grid_height * grid_width, -1
+                )
+                action_token_values = action_token_values.view(
+                    batch_size, self.num_heads, num_frames, action_tokens, -1
+                )
+                return torch.cat([action_token_values, frame_tokens], dim=3).flatten(2, 3)
+
+            q = merge(q, action_q)
+            k = merge(k, action_k)
+            v = merge(v, action_v)
+
+        if attn_mask is not None or self.use_sdpa:
+            x = F.scaled_dot_product_attention(
+                q, k, v, dropout_p=self.proj_drop_prob, is_causal=self.is_causal, attn_mask=attn_mask
+            )
+        else:
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(batch_size, num_tokens, channels)
+        x = self.proj(x)
+        return self.proj_drop(x)
+
+
+class ACBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        qk_scale: float | None = None,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        drop_path: float = 0.0,
+        norm_layer: type[nn.Module] = nn.LayerNorm,
+        use_sdpa: bool = True,
+        is_causal: bool = False,
+        grid_size: int = 16,
+        use_rope: bool = True,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        if not use_rope:
+            raise ValueError("JEVLA1 world predictor uses AC RoPE attention.")
+        self.attn = ACRoPEAttention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            use_sdpa=use_sdpa,
+            is_causal=is_causal,
+            grid_size=grid_size,
+            proj_drop=drop,
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        self.mlp = MLP(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=nn.GELU,
+            drop=drop,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+        num_frames: int | None = None,
+        grid_height: int | None = None,
+        grid_width: int | None = None,
+        action_tokens: int = 0,
+    ) -> torch.Tensor:
+        y = self.norm1(x)
+        y = self.attn(
+            y,
+            mask=None,
+            attn_mask=attn_mask,
+            num_frames=num_frames,
+            grid_height=grid_height,
+            grid_width=grid_width,
+            action_tokens=action_tokens,
+        )
+        x = x + self.drop_path(y)
+        y = self.norm2(x)
+        return x + self.drop_path(self.mlp(y))
+
+
+class ActionConditionedVideoPredictor(nn.Module):
+    """JEVLA1-compatible action-conditioned V-JEPA predictor."""
+
+    def __init__(
+        self,
+        num_frames: int,
+        img_size: tuple[int, int],
+        patch_size: int,
+        tubelet_size: int,
+        embed_dim: int,
+        action_embed_dim: int,
+        predictor_embed_dim: int,
+        depth: int,
+        num_heads: int,
+        mlp_ratio: float,
+        num_action_tokens_per_step: int,
+        use_extrinsics: bool = False,
+    ) -> None:
+        super().__init__()
+        self.is_frame_causal = True
+        self.use_extrinsics = use_extrinsics
+        self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True)
+        self.action_encoder = nn.Linear(action_embed_dim, predictor_embed_dim, bias=True)
+        self.state_encoder = nn.Linear(action_embed_dim, predictor_embed_dim, bias=True)
+        self.extrinsics_encoder = nn.Linear(action_embed_dim - 1, predictor_embed_dim, bias=True)
+
+        self.img_height, self.img_width = img_size
+        self.patch_size = patch_size
+        self.num_frames = num_frames
+        self.tubelet_size = tubelet_size
+        self.grid_height = self.img_height // self.patch_size
+        self.grid_width = self.img_width // self.patch_size
+
+        self.predictor_blocks = nn.ModuleList(
+            [
+                ACBlock(
+                    dim=predictor_embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=True,
+                    drop=0.0,
+                    attn_drop=0.0,
+                    drop_path=0.0,
+                    norm_layer=lambda dim: nn.LayerNorm(dim, eps=1e-6),
+                    grid_size=self.grid_height,
+                    use_rope=True,
+                )
+                for _ in range(depth)
+            ]
+        )
+        self.predictor_norm = nn.LayerNorm(predictor_embed_dim, eps=1e-6)
+        self.predictor_proj = nn.Linear(predictor_embed_dim, embed_dim, bias=True)
+        self.num_action_tokens_per_step = num_action_tokens_per_step
+
+    @property
+    def norm(self) -> nn.LayerNorm:
+        return self.predictor_norm
+
+    @property
+    def proj(self) -> nn.Linear:
+        return self.predictor_proj
+
+    def forward(
+        self,
+        frame_tokens: torch.Tensor,
+        action_tokens: torch.Tensor,
+        extrinsics: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        # starVLA input convention: frame_tokens [B, T*H*W, D], actions [B, T*A, D].
+        x = self.predictor_embed(frame_tokens)
+        batch_size, num_context_tokens, hidden_dim = x.size()
+        num_frames = num_context_tokens // (self.grid_height * self.grid_width)
+
+        actions = self.action_encoder(action_tokens)
+        actions = actions.view(batch_size, num_frames, -1, hidden_dim)
+        cond_tokens = actions.shape[2]
+
+        x = x.view(batch_size, num_frames, self.grid_height * self.grid_width, hidden_dim)
+        if self.use_extrinsics:
+            if extrinsics is None:
+                raise ValueError("extrinsics are required when use_extrinsics=True.")
+            cond_tokens += 1
+            extrinsic_tokens = self.extrinsics_encoder(extrinsics).unsqueeze(2)
+            x = torch.cat([actions, extrinsic_tokens, x], dim=2).flatten(1, 2)
+        else:
+            x = torch.cat([actions, x], dim=2).flatten(1, 2)
+
+        attn_mask = build_action_block_causal_attention_mask(
+            num_frames, self.grid_height, self.grid_width, add_tokens=cond_tokens
+        )
+        attn_mask = attn_mask[: x.size(1), : x.size(1)].to(x.device, non_blocking=True)
+
+        for block in self.predictor_blocks:
+            x = block(
+                x,
+                attn_mask=attn_mask,
+                num_frames=num_frames,
+                grid_height=self.grid_height,
+                grid_width=self.grid_width,
+                action_tokens=cond_tokens,
+            )
+
+        x = x.view(batch_size, num_frames, cond_tokens + self.grid_height * self.grid_width, hidden_dim)
+        x = x[:, :, cond_tokens:, :].flatten(1, 2)
+        x = self.predictor_norm(x)
+        return self.predictor_proj(x)
@@ -81,7 +81,7 @@ def to_absolute_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) ->
    return actions


-@ProcessorStepRegistry.register("delta_actions_processor")
+@ProcessorStepRegistry.register("relative_actions_processor")
@dataclass
 class RelativeActionsProcessorStep(ProcessorStep):
    """Converts absolute actions to relative actions (action -= state) for masked dimensions.
@@ -20,12 +20,16 @@ from .factory import (
    make_reward_pre_post_processors as make_reward_pre_post_processors,
 )
 from .pretrained import PreTrainedRewardModel as PreTrainedRewardModel
+from .robometer.configuration_robometer import RobometerConfig as RobometerConfig
 from .sarm.configuration_sarm import SARMConfig as SARMConfig
+from .topreward.configuration_topreward import TOPRewardConfig as TOPRewardConfig

 __all__ = [
    # Configuration classes
    "RewardClassifierConfig",
+    "RobometerConfig",
    "SARMConfig",
+    "TOPRewardConfig",
    # Base class
    "PreTrainedRewardModel",
    # Factory functions
@@ -25,7 +25,9 @@ from lerobot.processor import PolicyAction, PolicyProcessorPipeline

 from .classifier.configuration_classifier import RewardClassifierConfig
 from .pretrained import PreTrainedRewardModel
+from .robometer.configuration_robometer import RobometerConfig
 from .sarm.configuration_sarm import SARMConfig
+from .topreward.configuration_topreward import TOPRewardConfig


 def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
@@ -37,7 +39,7 @@ def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:

    Args:
        name: The name of the reward model. Supported names are "reward_classifier",
-              "sarm".
+              "sarm", "robometer", "topreward".

    Returns:
        The reward model class corresponding to the given name.
@@ -53,6 +55,14 @@ def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
        from lerobot.rewards.sarm.modeling_sarm import SARMRewardModel

        return SARMRewardModel
+    elif name == "robometer":
+        from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
+
+        return RobometerRewardModel
+    elif name == "topreward":
+        from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+        return TOPRewardModel
    else:
        try:
            return _get_reward_model_cls_from_name(name=name)
@@ -69,7 +79,7 @@ def make_reward_model_config(reward_type: str, **kwargs) -> RewardModelConfig:

    Args:
        reward_type: The type of the reward model. Supported types include
-                     "reward_classifier", "sarm".
+                     "reward_classifier", "sarm", "robometer", "topreward".
        **kwargs: Keyword arguments to be passed to the configuration class constructor.

    Returns:
@@ -82,6 +92,10 @@ def make_reward_model_config(reward_type: str, **kwargs) -> RewardModelConfig:
        return RewardClassifierConfig(**kwargs)
    elif reward_type == "sarm":
        return SARMConfig(**kwargs)
+    elif reward_type == "robometer":
+        return RobometerConfig(**kwargs)
+    elif reward_type == "topreward":
+        return TOPRewardConfig(**kwargs)
    else:
        try:
            config_cls = RewardModelConfig.get_choice_class(reward_type)
@@ -161,6 +175,21 @@ def make_reward_pre_post_processors(
            dataset_stats=kwargs.get("dataset_stats"),
            dataset_meta=kwargs.get("dataset_meta"),
        )
+    elif isinstance(reward_cfg, RobometerConfig):
+        from lerobot.rewards.robometer.processor_robometer import make_robometer_pre_post_processors
+
+        return make_robometer_pre_post_processors(
+            config=reward_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
+
+    elif isinstance(reward_cfg, TOPRewardConfig):
+        from lerobot.rewards.topreward.processor_topreward import make_topreward_pre_post_processors
+
+        return make_topreward_pre_post_processors(
+            config=reward_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )

    else:
        try:
@@ -0,0 +1,19 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_robometer import RobometerConfig
+from .modeling_robometer import RobometerRewardModel
+from .processor_robometer import make_robometer_pre_post_processors
+
+__all__ = ["RobometerConfig", "RobometerRewardModel", "make_robometer_pre_post_processors"]
@@ -0,0 +1,320 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Compute per-frame Robometer progress and success curves for a LeRobot dataset.
+
+For each episode, builds per-frame sub-samples using the frame-steps
+strategy from the Robometer eval server: for each original frame ``t``,
+linspace-subsample ``[0, t]`` into ``K`` frames (default 4, matching
+``NUM_SUBSAMPLED_FRAMES`` in the eval server), run one forward through
+the Robometer processor + model, and keep the last-frame progress value.
+All sub-samples are the same size ``K`` so they batch cleanly.
+
+The parquet uses the same schema as SARM's
+:mod:`lerobot.rewards.sarm.compute_rabc_weights` so existing consumers —
+:class:`lerobot.rewards.sarm.rabc.RABCWeights` (which reads
+``progress_sparse``) and the progress-overlay script in
+``examples/dataset/create_progress_videos.py`` — work without modification.
+
+Usage:
+    # Dense per-frame progress for one episode
+    python -m lerobot.rewards.robometer.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --reward-model-path lerobot/Robometer-4B \\
+        --episodes 0
+
+    # All episodes with batching
+    python -m lerobot.rewards.robometer.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --reward-model-path lerobot/Robometer-4B \\
+        --batch-size 16
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets import LeRobotDataset
+from lerobot.rewards.robometer.configuration_robometer import RobometerConfig
+from lerobot.rewards.robometer.modeling_robometer import RobometerRewardModel
+from lerobot.rewards.robometer.processor_robometer import RobometerEncoderProcessorStep
+from lerobot.types import TransitionKey
+
+DEFAULT_OUTPUT_FILENAME = "robometer_progress.parquet"
+
+# Upstream Robometer eval server uses K=4 for frame-steps sub-samples.
+DEFAULT_NUM_SUBSAMPLED_FRAMES = 4
+
+
+def get_reward_model_path_from_parquet(parquet_path: Path) -> str | None:
+    """Read ``reward_model_path`` from parquet metadata if available."""
+    if not parquet_path.exists():
+        return None
+    try:
+        metadata = pq.read_metadata(parquet_path).schema.to_arrow_schema().metadata
+        if metadata and b"reward_model_path" in metadata:
+            return metadata[b"reward_model_path"].decode()
+    except Exception:  # nosec B110
+        return None
+    return None
+
+
+def _resolve_task(sample: dict[str, Any], default: str) -> str:
+    """Best-effort task extraction from a dataset sample."""
+    task = sample.get("task")
+    if isinstance(task, str) and task:
+        return task
+    return default
+
+
+def _build_subsample_indices(num_frames: int, num_subsampled_frames: int) -> list[np.ndarray]:
+    """Frame-steps linspace expansion.
+
+    For each ``t in [0, num_frames - 1]`` returns ``num_subsampled_frames``
+    indices from ``np.linspace(0, t, num_subsampled_frames)`` — the first
+    and last frames are always included. Each entry is a fixed-size array
+    so the model can batch them.
+    """
+    return [np.linspace(0, t, num_subsampled_frames).round().astype(np.int64) for t in range(num_frames)]
+
+
+def compute_robometer_progress(
+    dataset_repo_id: str,
+    reward_model_path: str,
+    output_path: str | None = None,
+    device: str = "cuda",
+    batch_size: int = 32,
+    num_subsampled_frames: int = DEFAULT_NUM_SUBSAMPLED_FRAMES,
+    episodes: list[int] | None = None,
+    image_key: str | None = None,
+) -> Path:
+    """Run Robometer over a dataset and write per-frame progress + success."""
+    logging.info(f"Loading Robometer: {reward_model_path}")
+    config = RobometerConfig(pretrained_path=reward_model_path, device=device)
+    if image_key is not None:
+        config.image_key = image_key
+    model = RobometerRewardModel.from_pretrained(reward_model_path, config=config)
+    model.to(device).eval()
+
+    encoder = RobometerEncoderProcessorStep(
+        base_model_id=config.base_model_id,
+        image_key=config.image_key,
+        task_key=config.task_key,
+        default_task=config.default_task,
+        max_frames=num_subsampled_frames,
+        use_multi_image=config.use_multi_image,
+        use_per_frame_progress_token=config.use_per_frame_progress_token,
+    )
+
+    image_key = config.image_key
+
+    logging.info(f"Loading dataset: {dataset_repo_id}")
+    dataset = LeRobotDataset(dataset_repo_id, download_videos=True)
+    logging.info(f"Dataset: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+
+    episode_indices = list(range(dataset.num_episodes)) if episodes is None else episodes
+    logging.info(f"Processing {len(episode_indices)} episode(s)")
+
+    all_index: list[int] = []
+    all_episode: list[int] = []
+    all_frame: list[int] = []
+    all_progress: list[float] = []
+
+    for episode_idx in tqdm(episode_indices, desc="Episodes"):
+        ep = dataset.meta.episodes[episode_idx]
+        ep_start = int(ep["dataset_from_index"])
+        ep_end = int(ep["dataset_to_index"])
+        num_frames = ep_end - ep_start
+        if num_frames <= 0:
+            continue
+
+        first_sample = dataset[ep_start]
+        task = _resolve_task(first_sample, default=config.default_task or "perform the task")
+
+        ep_frames = torch.stack([dataset[ep_start + i][image_key] for i in range(num_frames)])
+
+        sub_indices = _build_subsample_indices(num_frames, num_subsampled_frames)
+
+        progress_per_frame = np.zeros(num_frames, dtype=np.float32)
+
+        for start in tqdm(range(0, num_frames, batch_size), desc=f"  Ep {episode_idx}", leave=False):
+            end = min(start + batch_size, num_frames)
+            frames_batch = torch.stack([ep_frames[sub_indices[i]] for i in range(start, end)])
+
+            transition = {
+                TransitionKey.OBSERVATION: {image_key: frames_batch},
+                TransitionKey.COMPLEMENTARY_DATA: {"task": task},
+            }
+            encoded = encoder(transition)
+            obs = encoded[TransitionKey.OBSERVATION]
+            batch = {
+                key: value.to(device) if isinstance(value, torch.Tensor) else value
+                for key, value in obs.items()
+            }
+
+            with torch.no_grad():
+                rewards = model.compute_reward(batch)
+            progress_per_frame[start:end] = rewards.cpu().numpy()
+
+        for local in range(num_frames):
+            all_index.append(ep_start + local)
+            all_episode.append(episode_idx)
+            all_frame.append(local)
+            all_progress.append(float(progress_per_frame[local]))
+
+        if device.startswith("cuda"):
+            torch.cuda.empty_cache()
+
+    table = pa.table(
+        {
+            "index": np.asarray(all_index, dtype=np.int64),
+            "episode_index": np.asarray(all_episode, dtype=np.int64),
+            "frame_index": np.asarray(all_frame, dtype=np.int64),
+            "progress_sparse": np.asarray(all_progress, dtype=np.float32),
+        }
+    ).replace_schema_metadata({b"reward_model_path": reward_model_path.encode()})
+
+    out = Path(dataset.root) / DEFAULT_OUTPUT_FILENAME if output_path is None else Path(output_path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    pq.write_table(table, out)
+    logging.info(f"Saved {len(table)} frame values to {out}")
+
+    progress_arr = np.asarray(all_progress, dtype=np.float32)
+    if progress_arr.size:
+        logging.info(
+            f"Progress: mean={float(progress_arr.mean()):.4f}, "
+            f"std={float(progress_arr.std()):.4f}, "
+            f"min={float(progress_arr.min()):.4f}, "
+            f"max={float(progress_arr.max()):.4f}"
+        )
+    return out
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Compute per-frame Robometer progress curves for RA-BC weighting.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+    # Dense per-frame progress for one episode
+    python -m lerobot.rewards.robometer.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --reward-model-path lerobot/Robometer-4B \\
+        --episodes 0
+
+    # All episodes, smaller batches for memory-constrained GPUs
+    python -m lerobot.rewards.robometer.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --reward-model-path lerobot/Robometer-4B \\
+        --batch-size 16
+        """,
+    )
+    parser.add_argument(
+        "--dataset-repo-id", type=str, required=True, help="HuggingFace dataset repo id or local path."
+    )
+    parser.add_argument(
+        "--reward-model-path", type=str, default=None, help="Robometer checkpoint repo id or local path."
+    )
+    parser.add_argument("--output-path", type=str, default=None, help="Output parquet path.")
+    parser.add_argument("--device", type=str, default="cuda", help="Device to use (default: cuda).")
+    parser.add_argument(
+        "--batch-size", type=int, default=32, help="Sub-samples per Qwen forward (default: 32)."
+    )
+    parser.add_argument(
+        "--num-subsampled-frames",
+        type=int,
+        default=DEFAULT_NUM_SUBSAMPLED_FRAMES,
+        help=f"Frames per sub-sample (default: {DEFAULT_NUM_SUBSAMPLED_FRAMES}, matches eval server).",
+    )
+    parser.add_argument(
+        "--episodes", type=int, nargs="+", default=None, help="Process only these episode indices."
+    )
+    parser.add_argument(
+        "--image-key", type=str, default=None, help="Image observation key (default: from config)."
+    )
+    parser.add_argument(
+        "--push-to-hub", action="store_true", help="Upload to the dataset repo on HuggingFace Hub."
+    )
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+    reward_model_path = args.reward_model_path
+    if reward_model_path is None:
+        temp_dataset = LeRobotDataset(args.dataset_repo_id, download_videos=False)
+        parquet_path = Path(temp_dataset.root) / DEFAULT_OUTPUT_FILENAME
+        reward_model_path = get_reward_model_path_from_parquet(parquet_path)
+        if reward_model_path:
+            logging.info(f"Using reward model from parquet metadata: {reward_model_path}")
+        else:
+            raise ValueError(
+                "--reward-model-path is required (no existing parquet with model metadata found)."
+            )
+
+    output_path = compute_robometer_progress(
+        dataset_repo_id=args.dataset_repo_id,
+        reward_model_path=reward_model_path,
+        output_path=args.output_path,
+        device=args.device,
+        batch_size=args.batch_size,
+        num_subsampled_frames=args.num_subsampled_frames,
+        episodes=args.episodes,
+        image_key=args.image_key,
+    )
+
+    print(f"\nRobometer progress saved to: {output_path}")
+
+    if args.push_to_hub:
+        from huggingface_hub import HfApi
+
+        api = HfApi()
+        hub_path = DEFAULT_OUTPUT_FILENAME
+
+        print(f"\nUploading to Hub: {args.dataset_repo_id}/{hub_path}")
+        api.upload_file(
+            path_or_fileobj=str(output_path),
+            path_in_repo=hub_path,
+            repo_id=args.dataset_repo_id,
+            repo_type="dataset",
+        )
+        print(
+            "Successfully uploaded to: "
+            f"https://huggingface.co/datasets/{args.dataset_repo_id}/blob/main/{hub_path}"
+        )
+
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: hf://datasets/{args.dataset_repo_id}/{hub_path}")
+        print("  rabc_head_mode: sparse")
+    else:
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: {output_path}")
+        print("  rabc_head_mode: sparse")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,158 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from copy import deepcopy
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.configs.rewards import RewardModelConfig
+from lerobot.utils.constants import OBS_IMAGES
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoConfig, AutoTokenizer
+else:
+    AutoConfig = None  # type: ignore[assignment]
+    AutoTokenizer = None  # type: ignore[assignment]
+
+
+# Special tokens Robometer adds to the Qwen-VL tokenizer at construction time.
+# The order is part of the data contract: upstream resized ``embed_tokens``
+# after adding these tokens in this exact order, so changing the set or order
+# would silently misalign the saved embedding rows with their token ids.
+# ``<|reward_token|>`` and ``<|sim_token|>`` are leftover from earlier upstream
+# heads (never read at inference) but still occupy rows the checkpoint expects.
+ROBOMETER_SPECIAL_TOKENS = (
+    "<|split_token|>",
+    "<|reward_token|>",
+    "<|pref_token|>",
+    "<|sim_token|>",
+    "<|prog_token|>",
+)
+
+
+@RewardModelConfig.register_subclass("robometer")
+@dataclass
+class RobometerConfig(RewardModelConfig):
+    """Configuration for the Robometer reward model."""
+
+    pretrained_path: str | None = "lerobot/Robometer-4B"
+    image_key: str = OBS_IMAGES + ".top"
+    task_key: str = "task"
+    default_task: str | None = None
+
+    max_frames: int | None = 8
+    reward_output: str = "progress"  # "progress" or "success"
+    success_threshold: float = 0.5
+
+    license: str | None = "apache-2.0"
+    tags: list[str] | None = field(
+        default_factory=lambda: ["reward-model", "vision-language", "qwen3-vl", "zero-shot"]
+    )
+
+    base_model_id: str = "Qwen/Qwen3-VL-4B-Instruct"
+    torch_dtype: str = "bfloat16"
+    use_multi_image: bool = True
+    use_per_frame_progress_token: bool = True
+    average_temporal_patches: bool = True
+    frame_pooling: str = "mean"  # "mean" | "boundary" | "attention"
+    frame_pooling_attn_temperature: float = 1.0
+    progress_loss_type: str = "discrete"  # "l1" | "l2" | "discrete"
+    progress_discrete_bins: int = 10
+
+    # Serialised Qwen backbone config (post-resize). Always populated by
+    # ``__post_init__`` from ``base_model_id`` + ``len(tokenizer) + 5``, so it
+    # is non-empty after construction. Saved into ``config.json`` automatically
+    # by the base ``_save_pretrained``.
+    vlm_config: dict[str, Any] = field(default_factory=dict)
+
+    input_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    output_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "REWARD": NormalizationMode.IDENTITY,
+        }
+    )
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.reward_output not in {"progress", "success"}:
+            raise ValueError(f"reward_output must be 'progress' or 'success', got {self.reward_output!r}")
+        if self.max_frames is not None and self.max_frames < 1:
+            raise ValueError(f"max_frames must be >= 1, got {self.max_frames}")
+        if self.frame_pooling not in {"mean", "boundary", "attention"}:
+            raise ValueError(f"frame_pooling must be mean/boundary/attention; got {self.frame_pooling!r}")
+        if self.frame_pooling_attn_temperature <= 0:
+            raise ValueError("frame_pooling_attn_temperature must be > 0")
+        if self.progress_loss_type not in {"l1", "l2", "discrete"}:
+            raise ValueError(f"progress_loss_type must be l1/l2/discrete; got {self.progress_loss_type!r}")
+        if self.use_per_frame_progress_token and not self.use_multi_image:
+            raise ValueError("use_per_frame_progress_token=True requires use_multi_image=True")
+
+        if self.image_key not in self.input_features:
+            self.input_features[self.image_key] = PolicyFeature(shape=(3, 224, 224), type=FeatureType.VISUAL)
+        self.output_features.setdefault("progress", PolicyFeature(shape=(1,), type=FeatureType.REWARD))
+        self.output_features.setdefault("success", PolicyFeature(shape=(1,), type=FeatureType.REWARD))
+
+        # Deterministically populate ``vlm_config`` so it is non-empty after
+        # construction. For ``Qwen/Qwen3-VL-4B-Instruct`` this gives
+        # ``len(tokenizer) + 5 = 151,669 + 5 = 151,674`` — the exact post-resize
+        # vocab the published ``Robometer-4B`` checkpoint was saved with.
+        if not self.vlm_config:
+            require_package("transformers", extra="robometer")
+            vlm = AutoConfig.from_pretrained(self.base_model_id).to_dict()
+            tokenizer = AutoTokenizer.from_pretrained(self.base_model_id)
+            text_config = vlm.get("text_config")
+            if not isinstance(text_config, dict):
+                raise ValueError(
+                    f"Backbone config for {self.base_model_id!r} has no nested `text_config`; "
+                    "Robometer expects a Qwen-VL-style config."
+                )
+            text_config["vocab_size"] = len(tokenizer) + len(ROBOMETER_SPECIAL_TOKENS)
+            self.vlm_config = vlm
+
+    @property
+    def use_discrete_progress(self) -> bool:
+        """Whether the progress head outputs distribution logits over bins."""
+        return self.progress_loss_type.lower() == "discrete"
+
+    @property
+    def vlm_backbone_config(self):
+        """Reconstruct the Qwen backbone config from :attr:`vlm_config`."""
+        require_package("transformers", extra="robometer")
+        config_dict = deepcopy(self.vlm_config)
+        model_type = config_dict.pop("model_type", None)
+        if model_type is None:
+            raise ValueError("vlm_config must include `model_type` to reconstruct the backbone config")
+        return AutoConfig.for_model(model_type, **config_dict)
+
+    @property
+    def observation_delta_indices(self) -> list[int] | None:
+        return None
+
+    @property
+    def action_delta_indices(self) -> None:
+        return None
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
+
+    def validate_features(self) -> None:
+        if self.image_key not in self.input_features:
+            raise ValueError(f"Robometer requires image input feature {self.image_key!r}")
@@ -0,0 +1,481 @@
+# Copyright 2026 Anthony Liang, Yigit Korkmaz, Stephen Tu, Erdem Bıyık, Jesse Zhang
+# and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""ROBOMETER: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons.
+
+Paper:         https://arxiv.org/abs/2603.02115
+Project:       https://robometer.github.io
+Original code: https://github.com/aliang8/robometer
+Model:         https://huggingface.co/robometer/Robometer-4B
+
+Robometer is a general-purpose, video-language-input reward model built on
+``Qwen/Qwen3-VL-4B-Instruct``. It is trained with a dual reward-prediction
+objective:
+
+- A frame-level progress loss anchoring reward magnitude on expert data.
+- A trajectory-comparison preference loss imposing global ordering constraints
+  across trajectories sharing the same instruction.
+
+To support downstream RL it also predicts a frame-level binary success. The
+training prompt inserts three learnable tokens:
+
+- ``<|prog_token|>`` after each frame to read per-frame progress and success.
+- ``<|pref_token|>`` at the end to read pairwise preference (training-only).
+- ``<|split_token|>`` between two trajectories in preference samples
+  (training-only).
+
+Progress is modeled as a categorical distribution over ``progress_discrete_bins``
+uniformly-spaced centers in ``[0, 1]`` (C51-style), and the continuous estimate
+is recovered as the softmax-weighted mean of those centers — see
+:func:`convert_bins_to_continuous`.
+
+This LeRobot port is **inference-only**: the preference head is preserved in
+the state dict for byte-equivalence with the published ``Robometer-4B``
+checkpoint but is not queried by :meth:`RobometerRewardModel.compute_reward`,
+which returns the last-frame progress (clamped to ``[0, 1]``) or sigmoid'd
+success probability depending on :attr:`RobometerConfig.reward_output`.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any
+
+import torch
+from torch import Tensor, nn
+
+from lerobot.rewards.pretrained import PreTrainedRewardModel
+from lerobot.rewards.robometer.configuration_robometer import RobometerConfig
+from lerobot.utils.constants import OBS_PREFIX
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoModelForImageTextToText
+else:
+    AutoModelForImageTextToText = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
+
+# Namespace for Robometer's pre-encoded Qwen-VL observation tensors.
+ROBOMETER_FEATURE_PREFIX = f"{OBS_PREFIX}robometer."
+ROBOMETER_QWEN_INPUT_KEYS = (
+    "input_ids",
+    "attention_mask",
+    "pixel_values",
+    "pixel_values_videos",
+    "image_grid_thw",
+    "video_grid_thw",
+    "second_per_grid_ts",
+    "mm_token_type_ids",
+)
+ROBOMETER_METADATA_KEYS = (
+    "prog_token_id",
+    "vision_start_token_id",
+    "vision_end_token_id",
+    "video_merge_size",
+)
+ROBOMETER_INPUT_KEYS = ROBOMETER_QWEN_INPUT_KEYS + ROBOMETER_METADATA_KEYS
+
+
+def convert_bins_to_continuous(bin_logits: Tensor) -> Tensor:
+    """Collapse per-bin logits into a single value in ``[0, 1]``.
+
+    The discrete progress head outputs ``num_bins`` logits per frame. Bins are
+    evenly spaced centers in ``[0, 1]``; the continuous prediction is the
+    softmax-weighted mean of those centers.
+    """
+    bin_probs = torch.softmax(bin_logits, dim=-1)
+    num_bins = bin_logits.shape[-1]
+    bin_centers = torch.linspace(0.0, 1.0, num_bins, device=bin_logits.device, dtype=bin_logits.dtype)
+    return (bin_probs * bin_centers).sum(dim=-1)
+
+
+def _squeeze_last_safe(x: Tensor) -> Tensor:
+    """Drop a trailing singleton dim only when present."""
+    return x.squeeze(-1) if x.ndim > 1 and x.shape[-1] == 1 else x
+
+
+def _torch_dtype(name: str) -> torch.dtype:
+    dtype = getattr(torch, name, None)
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    raise ValueError(f"Unknown torch dtype: {name!r}")
+
+
+class RobometerPredictionHead(nn.Sequential):
+    """Small MLP head used for Robometer's progress / success / preference outputs."""
+
+    def __init__(self, hidden_dim: int, output_size: int, *, dropout: float, with_sigmoid: bool) -> None:
+        layers: list[nn.Module] = [
+            nn.Linear(hidden_dim, hidden_dim // 2),
+            nn.LayerNorm(hidden_dim // 2),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim // 2, output_size),
+        ]
+        if with_sigmoid:
+            layers.append(nn.Sigmoid())
+        super().__init__(*layers)
+
+
+def decode_progress_outputs(
+    progress_logits: Tensor | None,
+    success_logits: Tensor | None,
+    *,
+    is_discrete_mode: bool,
+) -> dict[str, list[list[float]]]:
+    """Decode RBM head outputs into per-frame floats.
+
+    Args:
+        progress_logits: ``(B, T)`` (continuous) or ``(B, T, num_bins)`` (discrete).
+        success_logits: ``(B, T)`` raw logits, ``sigmoid``-ed to probabilities.
+        is_discrete_mode: if True the progress logits get a softmax over bins
+            and are projected onto bin centers via :func:`convert_bins_to_continuous`.
+
+    Returns:
+        Dict with ``progress_pred`` and ``success_probs``, each a list of
+        length ``B`` of per-frame float lists.
+    """
+    progress_pred: list[list[float]] = []
+    success_probs: list[list[float]] = []
+
+    if progress_logits is not None:
+        for sample_logits in progress_logits:
+            if is_discrete_mode:
+                continuous = convert_bins_to_continuous(sample_logits.detach().float().cpu())
+                progress_pred.append(continuous.flatten().tolist())
+            else:
+                progress_pred.append(sample_logits.detach().float().cpu().flatten().tolist())
+
+    if success_logits is not None:
+        for sample_logits in success_logits:
+            success_probs.append(torch.sigmoid(sample_logits.detach().float().cpu()).flatten().tolist())
+
+    return {"progress_pred": progress_pred, "success_probs": success_probs}
+
+
+class RobometerRewardModel(PreTrainedRewardModel):
+    """Robometer (RBM) reward model — inference-only LeRobot port.
+
+    Wraps a Qwen-VL backbone (default: ``Qwen/Qwen3-VL-4B-Instruct``) with three
+    prediction heads from the paper (progress, success, preference). At
+    inference time only the progress and success heads are queried; the
+    preference head is kept on the module so the published ``Robometer-4B``
+    safetensors load unchanged.
+    """
+
+    name = "robometer"
+    config_class = RobometerConfig
+
+    def __init__(self, config: RobometerConfig, *, dropout: float = 0.1) -> None:
+        require_package("transformers", extra="robometer")
+        super().__init__(config)
+        self.config = config
+
+        # Two backbone-build paths (EO-1 style, branched on ``pretrained_path``):
+        #
+        #   - Fresh training (``pretrained_path is None``): download the base
+        #     Qwen weights and resize the embed table to match
+        #     ``vlm_config.text_config.vocab_size`` — populated deterministically
+        #     in ``RobometerConfig.__post_init__`` as
+        #     ``len(tokenizer) + len(ROBOMETER_SPECIAL_TOKENS)``
+        #
+        #   - Loading a saved checkpoint (``pretrained_path`` is set): rebuild
+        #     the empty architecture from ``vlm_config`` via
+        #     ``AutoModelForImageTextToText.from_config`` so the subsequent
+        #     ``model.safetensors`` load is a direct fill of the right shape —
+        #     no redundant Qwen weight download.
+        torch_dtype = _torch_dtype(config.torch_dtype)
+        if config.pretrained_path is None:
+            self.model = AutoModelForImageTextToText.from_pretrained(
+                config.base_model_id,
+                dtype=torch_dtype,
+                trust_remote_code=True,
+            )
+            target_vocab = config.vlm_config["text_config"]["vocab_size"]
+            self.model.resize_token_embeddings(target_vocab)
+        else:
+            self.model = AutoModelForImageTextToText.from_config(
+                config.vlm_backbone_config,
+                dtype=torch_dtype,
+                trust_remote_code=True,
+            )
+
+        # All Qwen-VL backbones Robometer supports expose `text_config.hidden_size`.
+        # Falls back to the top-level `hidden_size` so future non-multimodal
+        # variants would still resolve.
+        backbone_config = self.model.config
+        text_config = getattr(backbone_config, "text_config", None)
+        hidden_size = getattr(text_config, "hidden_size", None) if text_config is not None else None
+        if hidden_size is None:
+            hidden_size = getattr(backbone_config, "hidden_size", None)
+        if hidden_size is None:
+            raise AttributeError(
+                f"Could not infer hidden_size from backbone config of {config.base_model_id}"
+            )
+        hidden_dim = int(hidden_size)
+
+        # Robometer's three prediction heads + frame-pool attention.
+        progress_output = config.progress_discrete_bins if config.use_discrete_progress else 1
+        self.progress_head = RobometerPredictionHead(
+            hidden_dim,
+            progress_output,
+            dropout=dropout,
+            with_sigmoid=not config.use_discrete_progress,
+        )
+        self.preference_head = RobometerPredictionHead(hidden_dim, 1, dropout=dropout, with_sigmoid=False)
+        self.success_head = RobometerPredictionHead(hidden_dim, 1, dropout=dropout, with_sigmoid=False)
+        self.frame_pool_attn = nn.Linear(hidden_dim, 1, bias=False)
+
+        # Match the dtype of the loaded base model so weight loading is a no-op cast.
+        model_dtype = next(self.model.parameters()).dtype
+        self.progress_head.to(dtype=model_dtype)
+        self.preference_head.to(dtype=model_dtype)
+        self.success_head.to(dtype=model_dtype)
+        self.frame_pool_attn.to(dtype=model_dtype)
+
+    def compute_reward(self, batch: dict[str, Tensor]) -> Tensor:
+        inputs = {
+            key: batch[f"{ROBOMETER_FEATURE_PREFIX}{key}"]
+            for key in ROBOMETER_INPUT_KEYS
+            if f"{ROBOMETER_FEATURE_PREFIX}{key}" in batch
+        }
+        if "input_ids" not in inputs:
+            raise KeyError(
+                f"Robometer batch missing pre-encoded inputs (expected "
+                f"`{ROBOMETER_FEATURE_PREFIX}input_ids`). Make sure the "
+                "RobometerEncoderProcessorStep ran before `compute_reward`."
+            )
+
+        device = next(self.model.parameters()).device
+        inputs = {key: value.to(device) if hasattr(value, "to") else value for key, value in inputs.items()}
+
+        self.eval()
+        with torch.no_grad():
+            progress_logits, success_logits = self._compute_rbm_logits(inputs)
+
+        decoded = decode_progress_outputs(
+            progress_logits,
+            success_logits,
+            is_discrete_mode=self.config.use_discrete_progress,
+        )
+        values = (
+            decoded["success_probs"] if self.config.reward_output == "success" else decoded["progress_pred"]
+        )
+
+        rewards = torch.stack([torch.as_tensor(seq, dtype=torch.float32)[-1] for seq in values])
+        if self.config.reward_output == "success":
+            rewards = (rewards > self.config.success_threshold).float()
+        else:
+            # Match upstream Robometer's ``extract_rewards_from_output``: per-frame
+            # progress predictions are clamped to ``[0, 1]`` before being returned.
+            rewards = rewards.clamp(0.0, 1.0)
+        return rewards.to(self.config.device or "cpu")
+
+    def _compute_rbm_logits(
+        self,
+        inputs: dict[str, Any],
+    ) -> tuple[Tensor, Tensor]:
+        """Run the Qwen3-VL backbone and apply Robometer's heads.
+
+        ``inputs`` is the encoded batch produced by
+        :class:`RobometerEncoderProcessorStep`. It carries Qwen tensors as well
+        as Robometer-specific metadata (``prog_token_id``,
+        ``vision_start_token_id``, ``vision_end_token_id``, ``video_merge_size``)
+        — the metadata is popped here so the rest can be forwarded straight to
+        the Qwen model.
+
+        Returns ``(progress_logits, success_logits)``. Shapes:
+
+        - ``progress_logits``: ``(B, T)`` (continuous) or ``(B, T, num_bins)`` (discrete).
+        - ``success_logits``: ``(B, T)`` raw logits (sigmoid happens at decode time).
+        """
+        prog_token_id = inputs.pop("prog_token_id", None)
+        vision_start_token_id = inputs.pop("vision_start_token_id", None)
+        vision_end_token_id = inputs.pop("vision_end_token_id", None)
+        video_merge_size = inputs.pop("video_merge_size", 14)
+
+        # Qwen3-VL doesn't reliably populate `last_hidden_state`; ask for the
+        # full hidden-state tuple and take the last layer. This matches the
+        # `is_qwen3` path in upstream Robometer's `RBM.forward_qwen` (main).
+        outputs = self.model(**inputs, output_hidden_states=True, return_dict=True)
+        hidden_state = (
+            outputs.hidden_states[-1]
+            if getattr(outputs, "hidden_states", None)
+            else outputs.last_hidden_state
+        )
+
+        input_ids = inputs["input_ids"]
+        if self.config.use_per_frame_progress_token:
+            if prog_token_id is None:
+                raise KeyError("`prog_token_id` missing in batch (run RobometerEncoderProcessorStep first)")
+            return self._process_token_extraction(hidden_state, input_ids, prog_token_id=prog_token_id)
+        if self.config.use_multi_image:
+            if vision_start_token_id is None or vision_end_token_id is None:
+                raise KeyError(
+                    "`vision_start_token_id` / `vision_end_token_id` missing in batch "
+                    "(run RobometerEncoderProcessorStep first)"
+                )
+            return self._process_multi_image_frames(
+                hidden_state,
+                input_ids,
+                start_id=vision_start_token_id,
+                end_id=vision_end_token_id,
+            )
+        video_grid_thw = inputs.get("video_grid_thw")
+        if video_grid_thw is None:
+            raise ValueError("video_grid_thw is required for video-mode Robometer inference")
+        if vision_start_token_id is None:
+            raise KeyError("`vision_start_token_id` missing in batch")
+        return self._process_video_frames(
+            hidden_state,
+            input_ids,
+            video_grid_thw,
+            start_id=vision_start_token_id,
+            merge_size=video_merge_size,
+        )
+
+    def _apply_heads_to_hidden_states(self, frame_embeddings: Tensor) -> tuple[Tensor, Tensor]:
+        """Apply progress + success heads to a tensor of frame embeddings."""
+        progress_out = self.progress_head(frame_embeddings)
+        progress = progress_out if self.config.use_discrete_progress else _squeeze_last_safe(progress_out)
+        success = _squeeze_last_safe(self.success_head(frame_embeddings))
+        return progress, success
+
+    def _process_token_extraction(
+        self,
+        hidden_state: Tensor,
+        input_ids: Tensor,
+        *,
+        prog_token_id: int,
+    ) -> tuple[Tensor, Tensor]:
+        """Per-frame progress/success from ``<|prog_token|>`` positions."""
+        token_mask = input_ids == prog_token_id
+        batch_indices, positions = token_mask.nonzero(as_tuple=True)
+        if positions.numel() == 0:
+            raise ValueError("`<|prog_token|>` not found in any sequence")
+
+        per_sample_hidden = [
+            hidden_state[i, positions[batch_indices == i]] for i in range(input_ids.shape[0])
+        ]
+        progress_list, success_list = [], []
+        for embeddings in per_sample_hidden:
+            if embeddings.shape[0] == 0:
+                raise ValueError("`<|prog_token|>` missing in a sequence")
+            progress, success = self._apply_heads_to_hidden_states(embeddings)
+            progress_list.append(progress)
+            success_list.append(success)
+
+        return torch.stack(progress_list), torch.stack(success_list)
+
+    def _process_multi_image_frames(
+        self,
+        hidden_state: Tensor,
+        input_ids: Tensor,
+        *,
+        start_id: int,
+        end_id: int,
+    ) -> tuple[Tensor, Tensor]:
+        """Per-frame progress/success in multi-image mode (Qwen-VL)."""
+        progress_list, success_list = [], []
+        for batch_idx in range(input_ids.shape[0]):
+            seq_ids = input_ids[batch_idx]
+            seq_hidden = hidden_state[batch_idx]
+            frame_embeddings = self._extract_hidden_states_from_token_pairs(
+                seq_hidden, seq_ids, start_id, end_id
+            )
+            progress, success = self._apply_heads_to_hidden_states(frame_embeddings)
+            progress_list.append(progress)
+            success_list.append(success)
+
+        return torch.stack(progress_list), torch.stack(success_list)
+
+    def _extract_hidden_states_from_token_pairs(
+        self,
+        hidden_state: Tensor,
+        input_ids: Tensor,
+        start_id: int,
+        end_id: int,
+    ) -> Tensor:
+        start_positions = (input_ids == start_id).nonzero(as_tuple=True)[0]
+        end_positions = (input_ids == end_id).nonzero(as_tuple=True)[0]
+        if start_positions.numel() == 0:
+            raise ValueError("`<|vision_start|>` not found in sequence")
+        if start_positions.numel() != end_positions.numel():
+            raise ValueError(
+                f"Mismatched vision token counts: {start_positions.numel()} start vs "
+                f"{end_positions.numel()} end"
+            )
+
+        frames: list[Tensor] = []
+        for start, end in zip(start_positions.tolist(), end_positions.tolist(), strict=True):
+            if start >= end:
+                raise ValueError(f"Invalid vision token pair: start={start} end={end}")
+            patch_tokens = hidden_state[start + 1 : end]
+            if patch_tokens.shape[0] == 0:
+                frames.append((hidden_state[start] + hidden_state[end]) / 2.0)
+                continue
+
+            pooling = self.config.frame_pooling
+            if pooling == "mean":
+                frames.append(patch_tokens.mean(dim=0))
+            elif pooling == "boundary":
+                frames.append(patch_tokens[-1])
+            else:  # attention
+                scores = (
+                    self.frame_pool_attn(patch_tokens).squeeze(-1)
+                    / self.config.frame_pooling_attn_temperature
+                )
+                weights = torch.softmax(scores, dim=0).unsqueeze(-1)
+                frames.append((weights * patch_tokens).sum(dim=0))
+
+        return torch.stack(frames)
+
+    def _process_video_frames(
+        self,
+        hidden_state: Tensor,
+        input_ids: Tensor,
+        video_grid_thw: Tensor,
+        *,
+        start_id: int,
+        merge_size: int,
+    ) -> tuple[Tensor, Tensor]:
+        """Per-frame progress/success in video mode (Qwen-VL)."""
+        progress_list, success_list = [], []
+        for batch_idx in range(input_ids.shape[0]):
+            seq_ids = input_ids[batch_idx]
+            seq_hidden = hidden_state[batch_idx]
+            start_positions = (seq_ids == start_id).nonzero(as_tuple=True)[0]
+            if start_positions.numel() == 0:
+                raise ValueError("`<|vision_start|>` not found in sequence")
+            t_dim, h_dim, w_dim = (int(x) for x in video_grid_thw[batch_idx].tolist())
+            tokens_per_frame = (h_dim * w_dim) // (merge_size**2)
+
+            cursor = start_positions[0].item()
+            frame_embeddings: list[Tensor] = []
+            for _ in range(t_dim):
+                if self.config.average_temporal_patches:
+                    patch = seq_hidden[cursor : cursor + tokens_per_frame]
+                    frame_embeddings.append(patch.mean(dim=0))
+                else:
+                    frame_embeddings.append(seq_hidden[cursor + tokens_per_frame])
+                cursor += tokens_per_frame
+
+            stacked = torch.stack(frame_embeddings)
+            progress, success = self._apply_heads_to_hidden_states(stacked)
+            progress_list.append(progress)
+            success_list.append(success)
+
+        return torch.stack(progress_list), torch.stack(success_list)
@@ -0,0 +1,338 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Robometer pre/post processing pipelines."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+import torch
+from PIL import Image
+from torch import Tensor
+
+from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    policy_action_to_transition,
+)
+from lerobot.rewards.robometer.configuration_robometer import (
+    ROBOMETER_SPECIAL_TOKENS,
+    RobometerConfig,
+)
+from lerobot.rewards.robometer.modeling_robometer import ROBOMETER_FEATURE_PREFIX
+from lerobot.types import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    OBS_IMAGES,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+)
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoProcessor
+else:
+    AutoProcessor = None
+
+PROGRESS_PROMPT = (
+    "The task for the robot is '{task}'. Given the trajectory video, predict "
+    "the task progress at each frame, how far along the robot is towards "
+    "completing the task, a float between 0 and 1, where 0 is the starting "
+    "state and 1 is when the task is completed. If the robot is not "
+    "performing the same task, predict 0 progress."
+)
+
+
+def _frames_to_pil(frames: np.ndarray) -> list[Image.Image]:
+    """Convert ``(T, H, W, C)`` uint8 frames to a list of PIL images."""
+    if frames.ndim != 4:
+        raise ValueError(f"Expected (T,H,W,C) frames; got shape {frames.shape}")
+    if frames.dtype != np.uint8:
+        frames = np.clip(frames, 0, 255).astype(np.uint8)
+    return [Image.fromarray(frames[i]) for i in range(frames.shape[0])]
+
+
+def _video_to_numpy(video: Tensor, *, max_frames: int | None) -> np.ndarray:
+    """Convert one trajectory tensor to a ``(T, H, W, C) uint8`` numpy array."""
+    if max_frames is not None:
+        video = video[-max_frames:]
+    if video.shape[1] in (1, 3):
+        video = video.permute(0, 2, 3, 1)
+    elif video.shape[-1] not in (1, 3):
+        raise ValueError(f"Expected channel dim of size 1 or 3, got shape {tuple(video.shape)}")
+
+    array = video.detach().cpu().numpy()
+    if np.issubdtype(array.dtype, np.floating) and array.size > 0 and array.max() <= 1.0:
+        array = array * 255.0
+    return np.clip(array, 0, 255).astype(np.uint8)
+
+
+def _expand_tasks(task: Any, *, batch_size: int, default: str | None) -> list[str]:
+    if task is None:
+        task = default
+    if task is None:
+        raise KeyError("Robometer expected a task description in complementary data")
+    if isinstance(task, str):
+        return [task] * batch_size
+    if isinstance(task, tuple):
+        task = list(task)
+    if not (isinstance(task, list) and all(isinstance(item, str) for item in task)):
+        raise TypeError(f"Robometer task must be a string or list of strings, got {type(task)}")
+    if len(task) == 1 and batch_size > 1:
+        return task * batch_size
+    if len(task) != batch_size:
+        raise ValueError(f"Expected {batch_size} tasks, got {len(task)}")
+    return task
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="robometer_encoder")
+class RobometerEncoderProcessorStep(ProcessorStep):
+    """Encode raw frames + task into Qwen-VL tensors for the Robometer model.
+
+    Loads a :class:`~transformers.AutoProcessor` matching ``base_model_id`` and
+    registers Robometer's special tokens on the tokenizer. The matching
+    embedding resize happens model-side in
+    :meth:`RobometerRewardModel.__init__`.
+
+    At call time the step reads:
+
+    - ``observation[image_key]``: ``(B, T, C, H, W)`` or ``(B, C, H, W)`` frames.
+    - ``complementary_data[task_key]``: a string or list of strings.
+
+    and writes ``observation[f"{ROBOMETER_FEATURE_PREFIX}<name>"]`` for:
+
+    - the Qwen-VL processor outputs: ``input_ids``, ``attention_mask``,
+      ``pixel_values``, ``image_grid_thw``, ``video_grid_thw``, ...
+    - Robometer-specific token ids consumed by the model heads:
+      ``prog_token_id``, ``vision_start_token_id``, ``vision_end_token_id``,
+      ``video_merge_size``.
+    """
+
+    base_model_id: str = "Qwen/Qwen3-VL-4B-Instruct"
+    image_key: str = OBS_IMAGES + ".top"
+    task_key: str = "task"
+    default_task: str | None = None
+    max_frames: int | None = 8
+    use_multi_image: bool = True
+    use_per_frame_progress_token: bool = True
+    max_length: int = 1024
+
+    _processor: Any = field(default=None, init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        require_package("transformers", extra="robometer")
+        require_package("qwen-vl-utils", extra="robometer", import_name="qwen_vl_utils")
+
+        self._processor = AutoProcessor.from_pretrained(
+            self.base_model_id,
+            trust_remote_code=True,
+            do_sample_frames=False,
+            padding_side="right",
+        )
+
+        # Register Robometer's special tokens on the tokenizer. The matching
+        # embedding resize happens model-side in `RobometerRewardModel.__init__`.
+        tokenizer = self._processor.tokenizer
+        # Qwen tokenizers may not define a pad token, but batched prompts/videos
+        # require padding, so reuse EOS as the padding token.
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        for token in ROBOMETER_SPECIAL_TOKENS:
+            if token not in tokenizer.get_vocab():
+                tokenizer.add_special_tokens({"additional_special_tokens": [token]})
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        observation = transition.get(TransitionKey.OBSERVATION)
+        complementary = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
+        if not isinstance(observation, dict):
+            raise ValueError("RobometerEncoderProcessorStep requires an observation dict")
+
+        if self.image_key not in observation:
+            raise KeyError(f"Robometer expected image key {self.image_key!r} in observation")
+
+        frames = observation[self.image_key]
+        tensor = frames.detach().cpu() if isinstance(frames, Tensor) else torch.as_tensor(frames)
+        if tensor.ndim == 4:
+            tensor = tensor.unsqueeze(1)
+        elif tensor.ndim != 5:
+            raise ValueError(
+                f"Expected Robometer frames with shape (B,C,H,W) or (B,T,C,H,W); got {tuple(tensor.shape)}"
+            )
+
+        batch_size = tensor.shape[0]
+        tasks = _expand_tasks(
+            complementary.get(self.task_key, self.default_task),
+            batch_size=batch_size,
+            default=self.default_task,
+        )
+
+        samples = [
+            (_video_to_numpy(tensor[i], max_frames=self.max_frames), tasks[i]) for i in range(batch_size)
+        ]
+        encoded = self.encode_samples(samples)
+
+        new_observation = dict(observation)
+        for key, value in encoded.items():
+            new_observation[f"{ROBOMETER_FEATURE_PREFIX}{key}"] = value
+
+        new_transition = transition.copy()
+        new_transition[TransitionKey.OBSERVATION] = new_observation
+        return new_transition
+
+    def encode_samples(self, samples: list[tuple[np.ndarray, str]]) -> dict[str, Tensor]:
+        """Run the Qwen-VL processor on a list of ``(frames, task)`` samples."""
+        from qwen_vl_utils import process_vision_info
+
+        conversations = [self._build_conversation(frames, task) for frames, task in samples]
+
+        texts = [
+            self._processor.apply_chat_template(
+                msg,
+                tokenize=False,
+                add_generation_prompt=False,
+                add_vision_id=True,
+                enable_thinking=False,
+                fps=1,
+            )
+            for msg in conversations
+        ]
+
+        process_kwargs: dict[str, Any] = {
+            "return_video_kwargs": True,
+            "return_video_metadata": True,
+        }
+        image_processor = getattr(self._processor, "image_processor", None)
+        if image_processor is not None and hasattr(image_processor, "patch_size"):
+            process_kwargs["image_patch_size"] = image_processor.patch_size
+
+        image_inputs, video_inputs, video_kwargs = process_vision_info(conversations, **process_kwargs)
+
+        videos: list[Any] | None = None
+        video_metadatas: list[Any] | None = None
+        if video_inputs:
+            if isinstance(video_inputs[0], tuple) and len(video_inputs[0]) == 2:
+                videos_seq, metadatas_seq = zip(*video_inputs, strict=False)
+                videos = list(videos_seq)
+                video_metadatas = list(metadatas_seq)
+            else:
+                videos = list(video_inputs)
+
+        processor_kwargs: dict[str, Any] = {
+            "text": texts,
+            "images": image_inputs,
+            "padding": True,
+            "truncation": False,
+            "max_length": self.max_length,
+            "return_tensors": "pt",
+            "do_resize": False,
+        }
+        if videos is not None:
+            processor_kwargs["videos"] = videos
+        if video_metadatas is not None:
+            processor_kwargs["video_metadata"] = video_metadatas
+        if video_kwargs:
+            processor_kwargs.update(video_kwargs)
+
+        encoded = self._processor(**processor_kwargs)
+
+        # Write Robometer-specific token ids and the video patch merge size into
+        # the encoded batch so `RobometerRewardModel` doesn't need its own
+        # tokenizer at inference (EO1-style separation: the processor owns the
+        # tokenizer, the model owns the backbone and heads).
+        tokenizer = self._processor.tokenizer
+        encoded["prog_token_id"] = tokenizer.convert_tokens_to_ids("<|prog_token|>")
+        encoded["vision_start_token_id"] = tokenizer.convert_tokens_to_ids("<|vision_start|>")
+        encoded["vision_end_token_id"] = tokenizer.convert_tokens_to_ids("<|vision_end|>")
+        video_processor = getattr(self._processor, "video_processor", None)
+        encoded["video_merge_size"] = int(getattr(video_processor, "merge_size", 14))
+        return encoded
+
+    def _build_conversation(self, frames: np.ndarray, task: str) -> list[dict[str, Any]]:
+        pil_frames = _frames_to_pil(frames)
+        prompt = PROGRESS_PROMPT.format(task=task)
+        content: list[dict[str, Any]] = [{"type": "text", "text": prompt}]
+
+        if self.use_multi_image:
+            for image in pil_frames:
+                content.append({"type": "image", "image": image})
+                if self.use_per_frame_progress_token:
+                    content.append({"type": "text", "text": "<|prog_token|>"})
+        else:
+            content.append({"type": "video", "video": pil_frames, "sample_fps": 1.0})
+
+        return [{"role": "user", "content": content}]
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+    def get_config(self) -> dict[str, Any]:
+        return {
+            "base_model_id": self.base_model_id,
+            "image_key": self.image_key,
+            "task_key": self.task_key,
+            "default_task": self.default_task,
+            "max_frames": self.max_frames,
+            "use_multi_image": self.use_multi_image,
+            "use_per_frame_progress_token": self.use_per_frame_progress_token,
+            "max_length": self.max_length,
+        }
+
+
+def make_robometer_pre_post_processors(
+    config: RobometerConfig,
+    dataset_stats: dict[str, dict[str, Any]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """Pipeline that pre-encodes frames + task into Qwen-VL tensors.
+
+    The preprocessor adds a batch dimension if needed, runs Robometer's
+    encoder, and moves everything to the configured device. The
+    postprocessor is the identity since Robometer outputs a single reward
+    tensor.
+    """
+    del dataset_stats  # Robometer has its own normalisation inside the Qwen-VL processor.
+
+    preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+        steps=[
+            AddBatchDimensionProcessorStep(),
+            RobometerEncoderProcessorStep(
+                base_model_id=config.base_model_id,
+                image_key=config.image_key,
+                task_key=config.task_key,
+                default_task=config.default_task,
+                max_frames=config.max_frames,
+                use_multi_image=config.use_multi_image,
+                use_per_frame_progress_token=config.use_per_frame_progress_token,
+            ),
+            DeviceProcessorStep(device=config.device or "cpu"),
+        ],
+        name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+    )
+    postprocessor = PolicyProcessorPipeline(
+        name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+        to_transition=policy_action_to_transition,
+    )
+    return preprocessor, postprocessor
@@ -0,0 +1,19 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration_topreward import TOPRewardConfig
+from .modeling_topreward import TOPRewardModel
+from .processor_topreward import make_topreward_pre_post_processors
+
+__all__ = ["TOPRewardConfig", "TOPRewardModel", "make_topreward_pre_post_processors"]
@@ -0,0 +1,353 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Compute per-frame TOPReward progress curves for a LeRobot dataset.
+
+For each episode, scores trajectory prefixes of increasing length using
+the TOPReward reward model, min-max normalises the raw log-prob rewards per episode,
+and writes a parquet file with one row per frame.
+
+The parquet uses the same schema as SARM's :mod:`lerobot.rewards.sarm.compute_rabc_weights`.
+
+Usage:
+    # Sparse-dense mode (15 anchors per episode, matches upstream)
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --num-samples 15
+
+    # Use a different VLM backbone
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --vlm-name Qwen/Qwen3-VL-4B-Instruct
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets import LeRobotDataset
+from lerobot.rewards.topreward.configuration_topreward import TOPRewardConfig
+from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+from lerobot.rewards.topreward.processor_topreward import TOPRewardEncoderProcessorStep
+from lerobot.types import TransitionKey
+
+DEFAULT_OUTPUT_FILENAME = "topreward_progress.parquet"
+
+
+def get_reward_model_path_from_parquet(parquet_path: Path) -> str | None:
+    """Read ``reward_model_path`` from parquet metadata if available."""
+    if not parquet_path.exists():
+        return None
+    try:
+        metadata = pq.read_metadata(parquet_path).schema.to_arrow_schema().metadata
+        if metadata and b"reward_model_path" in metadata:
+            return metadata[b"reward_model_path"].decode()
+    except Exception:  # nosec B110
+        return None
+    return None
+
+
+def _resolve_task(sample: dict[str, Any], default: str) -> str:
+    """Best-effort task extraction from a dataset sample."""
+    task = sample.get("task")
+    if isinstance(task, str) and task:
+        return task
+    return default
+
+
+def normalize_rewards(rewards: list[float] | np.ndarray) -> np.ndarray:
+    """Min-max normalise raw log-prob rewards into ``[0, 1]``."""
+    rewards_arr = np.asarray(rewards, dtype=np.float64)
+    if rewards_arr.size == 0:
+        return rewards_arr.astype(np.float32)
+    if rewards_arr.size == 1:
+        return np.array([1.0], dtype=np.float32)
+    r_min, r_max = rewards_arr.min(), rewards_arr.max()
+    if r_max == r_min:
+        return np.ones_like(rewards_arr, dtype=np.float32)
+    return ((rewards_arr - r_min) / (r_max - r_min)).astype(np.float32)
+
+
+def compute_instruction_rewards_for_prefixes(
+    model: TOPRewardModel,
+    encoder: TOPRewardEncoderProcessorStep,
+    dataset: LeRobotDataset,
+    ep_start: int,
+    num_frames: int,
+    task: str,
+    image_key: str,
+    num_samples: int | None,
+    device: str,
+) -> np.ndarray:
+    """Score an episode via prefix sweep and return a per-frame normalised curve."""
+    if num_samples is None or num_samples >= num_frames:
+        prefix_lengths = np.arange(1, num_frames + 1, dtype=np.int64)
+    else:
+        prefix_lengths = np.unique(np.linspace(1, num_frames, num_samples).round().astype(np.int64))
+
+    episode_frames = torch.stack([dataset[ep_start + i][image_key] for i in range(num_frames)])
+    rewards: list[float] = []
+    for length in prefix_lengths:
+        frames = episode_frames[: int(length)].unsqueeze(0)  # (1, T, C, H, W)
+
+        transition = {
+            TransitionKey.OBSERVATION: {image_key: frames},
+            TransitionKey.COMPLEMENTARY_DATA: {"task": task},
+        }
+        encoded = encoder(transition)
+        obs = encoded[TransitionKey.OBSERVATION]
+        batch = {
+            key: value.to(device) if isinstance(value, torch.Tensor) else value for key, value in obs.items()
+        }
+
+        with torch.no_grad():
+            reward = model.compute_reward(batch)
+        rewards.append(float(reward.item()))
+
+    normalized_rewards = normalize_rewards(rewards)
+
+    if prefix_lengths.shape[0] == num_frames:
+        return normalized_rewards
+
+    return np.interp(
+        np.arange(1, num_frames + 1, dtype=np.float64),
+        prefix_lengths.astype(np.float64),
+        normalized_rewards.astype(np.float64),
+    ).astype(np.float32)
+
+
+def compute_topreward_progress(
+    dataset_repo_id: str,
+    reward_model_path: str | None = None,
+    vlm_name: str | None = None,
+    output_path: str | None = None,
+    device: str = "cuda",
+    num_samples: int | None = None,
+    fps: float | None = None,
+    episodes: list[int] | None = None,
+) -> Path:
+    """Run TOPReward over a dataset and write per-frame progress."""
+    if reward_model_path is not None:
+        logging.info(f"Loading TOPReward config from: {reward_model_path}")
+        model = TOPRewardModel.from_pretrained(reward_model_path)
+        config = model.config
+        config.device = device
+        if vlm_name is not None and vlm_name != config.vlm_name:
+            logging.info(f"Overriding vlm_name from config: {config.vlm_name} -> {vlm_name}")
+            config.vlm_name = vlm_name
+            model = TOPRewardModel(config)
+    else:
+        config_kwargs: dict[str, Any] = {"device": device}
+        if vlm_name is not None:
+            config_kwargs["vlm_name"] = vlm_name
+        if fps is not None:
+            config_kwargs["fps"] = fps
+        config = TOPRewardConfig(**config_kwargs)
+        logging.info(f"Constructing TOPReward with VLM: {config.vlm_name}")
+        model = TOPRewardModel(config)
+
+    model.to(device).eval()
+
+    encoder = TOPRewardEncoderProcessorStep(
+        vlm_name=config.vlm_name,
+        image_key=config.image_key,
+        task_key=config.task_key,
+        default_task=config.default_task,
+        max_frames=None,  # no tail-crop: we control prefix length explicitly
+        fps=config.fps,
+        prompt_prefix=config.prompt_prefix,
+        prompt_suffix_template=config.prompt_suffix_template,
+        add_chat_template=config.add_chat_template,
+        max_length=config.max_input_length,
+    )
+
+    image_key = config.image_key
+
+    logging.info(f"Loading dataset: {dataset_repo_id}")
+    dataset = LeRobotDataset(dataset_repo_id, download_videos=True)
+    logging.info(f"Dataset: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+
+    episode_indices = list(range(dataset.num_episodes)) if episodes is None else episodes
+    logging.info(f"Processing {len(episode_indices)} episode(s)")
+
+    all_index: list[int] = []
+    all_episode: list[int] = []
+    all_frame: list[int] = []
+    all_progress: list[float] = []
+
+    for episode_idx in tqdm(episode_indices, desc="Episodes"):
+        ep = dataset.meta.episodes[episode_idx]
+        ep_start = int(ep["dataset_from_index"])
+        ep_end = int(ep["dataset_to_index"])
+        num_frames = ep_end - ep_start
+        if num_frames <= 0:
+            continue
+
+        first_sample = dataset[ep_start]
+        task = _resolve_task(first_sample, default=config.default_task or "perform the task")
+
+        per_frame = compute_instruction_rewards_for_prefixes(
+            model=model,
+            encoder=encoder,
+            dataset=dataset,
+            ep_start=ep_start,
+            num_frames=num_frames,
+            task=task,
+            image_key=image_key,
+            num_samples=num_samples,
+            device=device,
+        )
+
+        for local in range(num_frames):
+            all_index.append(ep_start + local)
+            all_episode.append(episode_idx)
+            all_frame.append(local)
+            all_progress.append(float(per_frame[local]))
+
+        if device.startswith("cuda"):
+            torch.cuda.empty_cache()
+
+    table = pa.table(
+        {
+            "index": np.asarray(all_index, dtype=np.int64),
+            "episode_index": np.asarray(all_episode, dtype=np.int64),
+            "frame_index": np.asarray(all_frame, dtype=np.int64),
+            "progress_sparse": np.asarray(all_progress, dtype=np.float32),
+        }
+    )
+
+    schema_metadata: dict[bytes, bytes] = {b"vlm_name": config.vlm_name.encode()}
+    if reward_model_path is not None:
+        schema_metadata[b"reward_model_path"] = reward_model_path.encode()
+    table = table.replace_schema_metadata(schema_metadata)
+
+    out = Path(dataset.root) / DEFAULT_OUTPUT_FILENAME if output_path is None else Path(output_path)
+    out.parent.mkdir(parents=True, exist_ok=True)
+    pq.write_table(table, out)
+    logging.info(f"Saved {len(table)} frame values to {out}")
+
+    progress_arr = np.asarray(all_progress, dtype=np.float32)
+    if progress_arr.size:
+        logging.info(
+            f"Progress: mean={float(progress_arr.mean()):.4f}, "
+            f"std={float(progress_arr.std()):.4f}, "
+            f"min={float(progress_arr.min()):.4f}, "
+            f"max={float(progress_arr.max()):.4f}"
+        )
+    return out
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Compute per-frame TOPReward progress curves for RA-BC weighting.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+    # Sparse-dense mode (matches upstream TOPReward num_samples=15)
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --num-samples 15
+
+    # Use a smaller VLM
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --vlm-name Qwen/Qwen3-VL-4B-Instruct
+        """,
+    )
+    parser.add_argument(
+        "--dataset-repo-id", type=str, required=True, help="HuggingFace dataset repo id or local path."
+    )
+    parser.add_argument(
+        "--reward-model-path", type=str, default=None, help="Optional TOPReward LeRobot config."
+    )
+    parser.add_argument("--vlm-name", type=str, default=None, help="Override the VLM backbone (HF Hub id).")
+    parser.add_argument("--output-path", type=str, default=None, help="Output parquet path.")
+    parser.add_argument("--device", type=str, default="cuda", help="Device to use (default: cuda).")
+    parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=None,
+        help="Anchor prefix samples per episode. None = dense. 15 matches upstream.",
+    )
+    parser.add_argument(
+        "--episodes",
+        type=int,
+        nargs="+",
+        default=None,
+        help="Process only these episode indices (e.g. --episodes 0 or --episodes 0 5 10).",
+    )
+    parser.add_argument("--fps", type=float, default=None, help="Override TOPRewardConfig.fps.")
+    parser.add_argument(
+        "--push-to-hub", action="store_true", help="Upload to the dataset repo on HuggingFace Hub."
+    )
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+    output_path = compute_topreward_progress(
+        dataset_repo_id=args.dataset_repo_id,
+        reward_model_path=args.reward_model_path,
+        vlm_name=args.vlm_name,
+        output_path=args.output_path,
+        device=args.device,
+        num_samples=args.num_samples,
+        fps=args.fps,
+        episodes=args.episodes,
+    )
+
+    print(f"\nTOPReward progress saved to: {output_path}")
+
+    if args.push_to_hub:
+        from huggingface_hub import HfApi
+
+        api = HfApi()
+        hub_path = DEFAULT_OUTPUT_FILENAME
+
+        print(f"\nUploading to Hub: {args.dataset_repo_id}/{hub_path}")
+        api.upload_file(
+            path_or_fileobj=str(output_path),
+            path_in_repo=hub_path,
+            repo_id=args.dataset_repo_id,
+            repo_type="dataset",
+        )
+        print(
+            "Successfully uploaded to: "
+            f"https://huggingface.co/datasets/{args.dataset_repo_id}/blob/main/{hub_path}"
+        )
+
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: hf://datasets/{args.dataset_repo_id}/{hub_path}")
+        print("  rabc_head_mode: sparse")
+    else:
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: {output_path}")
+        print("  rabc_head_mode: sparse")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,146 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+from lerobot.configs import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.configs.rewards import RewardModelConfig
+from lerobot.utils.constants import OBS_IMAGES
+
+# Default prompt scaffolding from the upstream TOPReward paper / reference
+# implementation (``QwenClient.compute_instruction_reward``). The prompt
+# scores the terminal ``True`` token in ``f"{instruction} ... True"``
+# given the video.
+DEFAULT_PROMPT_PREFIX = (
+    "The above video shows a robot manipulation trajectory that completes the following task: "
+)
+DEFAULT_PROMPT_SUFFIX_TEMPLATE = (
+    "{instruction} Decide whether the above statement is True or not. The answer is: True"
+)
+
+
+@RewardModelConfig.register_subclass("topreward")
+@dataclass
+class TOPRewardConfig(RewardModelConfig):
+    """Configuration for the TOPReward zero-shot reward model.
+
+    TOPReward is **zero-shot**: it has no learnable parameters of its own.
+    The "model" is a generic vision-language model (default
+    ``Qwen/Qwen3-VL-8B-Instruct``) used with a fixed prompt to extract
+    token log-probabilities as a reward signal. There is therefore no
+    fine-tuned checkpoint to host: ``pretrained_path`` is unused at
+    runtime — the model identity is :attr:`vlm_name` (an HF Hub id).
+
+    Args:
+        vlm_name: Hugging Face Hub id of the underlying VLM. Must be a
+            Qwen3-VL family model (the only client implemented in this
+            LeRobot port).
+        torch_dtype: Torch dtype name passed to the VLM loader
+            (``"auto"``, ``"bfloat16"``, ``"float16"``, ...).
+        attn_implementation: ``transformers`` attention implementation
+            (e.g. ``"flash_attention_2"``, ``"sdpa"``). Defaults to
+            ``None`` so the upstream picks the best available.
+        image_key: Observation key that holds the trajectory frames.
+        task_key: Complementary-data key that holds the task instruction.
+        default_task: Fallback instruction when ``task_key`` is absent.
+        max_frames: Cap on the number of frames fed to the VLM per
+            sample. ``None`` = use all frames.
+        fps: Frames-per-second metadata for the Qwen video processor.
+        prompt_prefix: Text shown to the VLM right after the video and
+            before the suffix template.
+        prompt_suffix_template: Suffix appended after ``prompt_prefix``.
+            Must contain ``{instruction}``; the VLM scores the
+            log-likelihood of the tokens that follow the prefix.
+        add_chat_template: If ``True``, wrap the full prompt with the
+            tokenizer's chat template before tokenisation (matches
+            upstream ``add_chat_template=True``).
+        success_threshold: Optional log-prob threshold. If finite,
+            :meth:`TOPRewardModel.compute_reward` returns
+            ``(reward > success_threshold).float()`` instead of the raw
+            log-prob.
+        max_input_length: Hard limit on the total tokenized input length;
+            samples that exceed it raise a ``ValueError``.
+    """
+
+    # Path to a local LeRobot dir or HF repo that holds a ``config.json``
+    # snapshot of this TOPRewardConfig. The VLM weights themselves are
+    # always identified by ``vlm_name``.
+    pretrained_path: str | None = None
+
+    vlm_name: str = "Qwen/Qwen3-VL-8B-Instruct"
+    torch_dtype: str = "auto"
+    attn_implementation: str | None = None
+
+    image_key: str = OBS_IMAGES + ".top"
+    task_key: str = "task"
+    default_task: str | None = None
+    max_frames: int | None = 16
+    fps: float = 2.0
+
+    prompt_prefix: str = DEFAULT_PROMPT_PREFIX
+    prompt_suffix_template: str = DEFAULT_PROMPT_SUFFIX_TEMPLATE
+    add_chat_template: bool = False
+
+    success_threshold: float = float("-inf")
+    max_input_length: int = 32768
+
+    license: str | None = "mit"  # matches upstream TOPReward
+    tags: list[str] | None = field(
+        default_factory=lambda: ["reward-model", "vision-language", "qwen3-vl", "zero-shot"]
+    )
+
+    input_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    output_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "REWARD": NormalizationMode.IDENTITY,
+        }
+    )
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.max_frames is not None and self.max_frames < 1:
+            raise ValueError(f"max_frames must be >= 1, got {self.max_frames}")
+        if self.fps <= 0:
+            raise ValueError(f"fps must be > 0, got {self.fps}")
+        if "{instruction}" not in self.prompt_suffix_template:
+            raise ValueError(
+                "prompt_suffix_template must contain `{instruction}` so the model "
+                "scores the log-likelihood of the task suffix."
+            )
+        if self.max_input_length <= 0:
+            raise ValueError(f"max_input_length must be > 0, got {self.max_input_length}")
+
+        if self.image_key not in self.input_features:
+            self.input_features[self.image_key] = PolicyFeature(shape=(3, 224, 224), type=FeatureType.VISUAL)
+        self.output_features.setdefault("reward", PolicyFeature(shape=(1,), type=FeatureType.REWARD))
+
+    @property
+    def observation_delta_indices(self) -> list[int] | None:
+        return None
+
+    @property
+    def action_delta_indices(self) -> None:
+        return None
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
+
+    def validate_features(self) -> None:
+        if self.image_key not in self.input_features:
+            raise ValueError(f"TOPReward requires image input feature {self.image_key!r}")
@@ -0,0 +1,238 @@
+# Copyright 2026 Shirui Chen, Cole Harrison, Ying-Chun Lee, Angela Jin Yang,
+# Zhongzheng Ren, Lillian J. Ratliff, Jiafei Duan, Dieter Fox, Ranjay Krishna
+# and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics.
+
+Paper:         https://arxiv.org/abs/2602.19313
+Project:       https://topreward.github.io/webpage/
+Original code: https://github.com/TOPReward/TOPReward
+Backbone:      https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct  (default)
+
+TOPReward is a **zero-shot** reward model: it has no fine-tuned weights of
+its own. Given a video trajectory and a task instruction, it asks an
+off-the-shelf VLM how likely the instruction is, conditioned on the video,
+and returns that log-likelihood as the reward signal.
+
+Inference recipe:
+
+1. The processor builds a chat-style prompt, tokenises it, and emits
+   ``input_ids``, ``attention_mask``, vision tensors, and ``labels``.
+   The processor label-masks everything except the terminal answer token with
+   ``-100``.
+2. Forward the full token sequence through the VLM.
+3. Read the terminal answer token log-probability from the logits as the
+   scalar reward.
+
+With the default ``prompt_suffix_template``, the only unmasked token is the
+literal ``"True"`` at the end — the reward is
+``log P("True" | video + prompt + instruction)``.
+
+This LeRobot port is **inference-only and not trainable** — :meth:`forward`
+is intentionally inherited from :class:`PreTrainedRewardModel` and raises
+``NotImplementedError``, making :attr:`PreTrainedRewardModel.is_trainable`
+return ``False``.
+
+Because the VLM weights live on the Hugging Face Hub under their canonical
+id (``Qwen/Qwen3-VL-8B-Instruct`` etc.) and TOPReward never modifies them,
+:meth:`_save_pretrained` and :meth:`from_pretrained` are overridden so a
+TOPReward LeRobot "checkpoint" is a single ``config.json`` (the VLM is
+re-fetched from the Hub at load time).
+"""
+
+from __future__ import annotations
+
+import builtins
+import logging
+import os
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import TYPE_CHECKING, Any, TypeVar
+
+import numpy as np
+import torch
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.constants import CONFIG_NAME
+from huggingface_hub.errors import HfHubHTTPError
+from torch import Tensor
+from torch.nn.functional import cross_entropy
+
+from lerobot.configs.rewards import RewardModelConfig
+from lerobot.rewards.pretrained import PreTrainedRewardModel
+from lerobot.rewards.topreward.configuration_topreward import TOPRewardConfig
+from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX, TOPREWARD_INPUT_KEYS
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING:
+    from lerobot.configs.train import TrainPipelineConfig
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import Qwen3VLForConditionalGeneration
+else:
+    Qwen3VLForConditionalGeneration = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T", bound="TOPRewardModel")
+
+
+def _torch_dtype(name: str) -> torch.dtype | str:
+    """Resolve a torch dtype name; ``"auto"`` is passed through verbatim."""
+    if name == "auto":
+        return "auto"
+    dtype = getattr(torch, name, None)
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    raise ValueError(f"Unknown torch dtype: {name!r}")
+
+
+class TOPRewardModel(PreTrainedRewardModel):
+    """TOPReward zero-shot reward model."""
+
+    name = "topreward"
+    config_class = TOPRewardConfig
+
+    def __init__(self, config: TOPRewardConfig) -> None:
+        require_package("transformers", extra="topreward")
+        super().__init__(config)
+        self.config = config
+
+        torch_dtype = _torch_dtype(config.torch_dtype)
+        model_kwargs: dict[str, Any] = {"dtype": torch_dtype, "trust_remote_code": True}
+        if config.attn_implementation is not None:
+            model_kwargs["attn_implementation"] = config.attn_implementation
+
+        self.model = Qwen3VLForConditionalGeneration.from_pretrained(config.vlm_name, **model_kwargs)
+
+    def compute_reward(self, batch: dict[str, Any]) -> Tensor:
+        """Return one log-prob reward per sample in the batch."""
+        inputs: dict[str, Any] = {}
+        for key in TOPREWARD_INPUT_KEYS:
+            batch_key = f"{TOPREWARD_FEATURE_PREFIX}{key}"
+            if batch_key not in batch:
+                raise KeyError(
+                    f"TOPReward batch missing `{batch_key}`. Make sure the "
+                    "TOPRewardEncoderProcessorStep ran before `compute_reward`."
+                )
+            inputs[key] = batch[batch_key]
+
+        device = next(self.model.parameters()).device
+        inputs = {key: value.to(device) if hasattr(value, "to") else value for key, value in inputs.items()}
+        labels = inputs.pop("labels")
+        inputs["logits_to_keep"] = 2
+
+        self.eval()
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        logits = outputs.logits
+        rewards = -cross_entropy(logits[:, -2, :].float(), labels[:, -1], reduction="none")
+        if np.isfinite(self.config.success_threshold):
+            rewards = (rewards > self.config.success_threshold).float()
+        return rewards.to(self.config.device or "cpu")
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Save ``config.json`` only."""
+        self.config._save_pretrained(save_directory)
+
+    @classmethod
+    def from_pretrained(
+        cls: builtins.type[T],
+        pretrained_name_or_path: str | Path,
+        *,
+        config: RewardModelConfig | None = None,
+        force_download: bool = False,
+        resume_download: bool | None = None,
+        proxies: dict | None = None,
+        token: str | bool | None = None,
+        cache_dir: str | Path | None = None,
+        local_files_only: bool = False,
+        revision: str | None = None,
+        strict: bool = False,  # noqa: ARG003 — accepted for API parity; unused (no safetensors to load)
+        **kwargs: Any,
+    ) -> T:
+        """Load a TOPReward configuration and instantiate the wrapped VLM."""
+        if config is None:
+            config = RewardModelConfig.from_pretrained(
+                pretrained_name_or_path=pretrained_name_or_path,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                token=token,
+                cache_dir=cache_dir,
+                local_files_only=local_files_only,
+                revision=revision,
+                **kwargs,
+            )
+        if not isinstance(config, TOPRewardConfig):
+            raise TypeError(
+                f"Expected a TOPRewardConfig, got {type(config).__name__}. Make sure "
+                f"`pretrained_name_or_path={pretrained_name_or_path!r}` points at a "
+                "TOPReward checkpoint."
+            )
+
+        model_id = str(pretrained_name_or_path)
+        if not os.path.isdir(model_id):
+            try:
+                hf_hub_download(
+                    repo_id=model_id,
+                    filename=CONFIG_NAME,
+                    revision=revision,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    token=token,
+                    local_files_only=local_files_only,
+                )
+            except HfHubHTTPError as e:
+                raise FileNotFoundError(
+                    f"{CONFIG_NAME} not found on the HuggingFace Hub in {model_id}"
+                ) from e
+
+        instance = cls(config, **kwargs)
+        instance.to(config.device)
+        instance.eval()
+        return instance
+
+    def push_model_to_hub(self, cfg: TrainPipelineConfig):
+        """Push the TOPReward ``config.json`` + model card to the Hub."""
+        api = HfApi()
+        repo_id = api.create_repo(
+            repo_id=self.config.repo_id, private=self.config.private, exist_ok=True
+        ).repo_id
+
+        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
+            saved_path = Path(tmp) / repo_id
+            saved_path.mkdir(parents=True, exist_ok=True)
+
+            self.config._save_pretrained(saved_path)
+
+            card = self.generate_model_card(
+                cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags
+            )
+            card.save(str(saved_path / "README.md"))
+
+            cfg.save_pretrained(saved_path)
+
+            commit_info = api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message="Upload TOPReward config and readme",
+                allow_patterns=["*.json", "*.yaml", "*.md"],
+                ignore_patterns=["*.tmp", "*.log", "*.safetensors"],
+            )
+
+            logger.info(f"Model pushed to {commit_info.repo_url.url}")
@@ -0,0 +1,305 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""TOPReward pre/post processing pipeline."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import torch
+from torch import Tensor
+
+from lerobot.configs import PipelineFeatureType, PolicyFeature
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    ProcessorStepRegistry,
+    policy_action_to_transition,
+)
+from lerobot.rewards.topreward.configuration_topreward import (
+    DEFAULT_PROMPT_PREFIX,
+    DEFAULT_PROMPT_SUFFIX_TEMPLATE,
+    TOPRewardConfig,
+)
+from lerobot.types import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    OBS_IMAGES,
+    OBS_PREFIX,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+)
+from lerobot.utils.import_utils import _transformers_available, require_package
+
+if TYPE_CHECKING or _transformers_available:
+    from transformers import AutoProcessor
+else:
+    AutoProcessor = None
+
+TOPREWARD_FEATURE_PREFIX = f"{OBS_PREFIX}topreward."
+
+_TRUE_ANSWER = "True"
+
+TOPREWARD_VLM_INPUT_KEYS = (
+    "input_ids",
+    "attention_mask",
+    "pixel_values_videos",
+    "video_grid_thw",
+    "mm_token_type_ids",
+)
+TOPREWARD_INPUT_KEYS = TOPREWARD_VLM_INPUT_KEYS + ("labels",)
+
+
+def _prepare_video_batch(video: Tensor, *, max_frames: int | None) -> Tensor:
+    """Return videos as ``(B, T, C, H, W)`` uint8 tensors for Qwen3-VL."""
+    if video.ndim == 4:
+        video = video.unsqueeze(1)
+    elif video.ndim != 5:
+        raise ValueError(
+            f"Expected TOPReward frames with shape (B,C,H,W) or (B,T,C,H,W); got {tuple(video.shape)}"
+        )
+
+    if max_frames is not None:
+        video = video[:, -max_frames:]
+    if video.shape[-1] in (1, 3):
+        video = video.permute(0, 1, 4, 2, 3)
+    elif video.shape[2] not in (1, 3):
+        raise ValueError(f"Expected channel dim of size 1 or 3, got shape {tuple(video.shape)}")
+
+    if video.is_floating_point():
+        video = video * 255.0
+
+    return video.clamp(0, 255).to(torch.uint8).contiguous()
+
+
+def _expand_tasks(task: Any, *, batch_size: int, default: str | None) -> list[str]:
+    if task is None:
+        task = default
+    if task is None:
+        raise KeyError("TOPReward expected a task description in complementary data")
+    if isinstance(task, str):
+        return [task] * batch_size
+    if isinstance(task, tuple):
+        task = list(task)
+    if not (isinstance(task, list) and all(isinstance(item, str) for item in task)):
+        raise TypeError(f"TOPReward task must be a string or list of strings, got {type(task)}")
+    if len(task) == 1 and batch_size > 1:
+        return task * batch_size
+    if len(task) != batch_size:
+        raise ValueError(f"Expected {batch_size} tasks, got {len(task)}")
+    return task
+
+
+@dataclass
+@ProcessorStepRegistry.register(name="topreward_encoder")
+class TOPRewardEncoderProcessorStep(ProcessorStep):
+    """Encode raw frames + task into Qwen-VL tensors for the TOPReward model.
+
+    Loads a :class:`~transformers.AutoProcessor` matching ``vlm_name`` and
+    builds the full chat prompt including the instruction suffix. The
+    resulting ``input_ids``, ``attention_mask``, vision tensors, and
+    ``labels`` are written under the ``observation.topreward.*`` namespace
+    so the model can score without re-tokenising.
+
+    At call time the step reads:
+
+    - ``observation[image_key]``: ``(B, T, C, H, W)`` or ``(B, C, H, W)`` frames.
+    - ``complementary_data[task_key]``: a string or list of strings.
+
+    and writes ``observation[f"{TOPREWARD_FEATURE_PREFIX}<name>"]`` for the
+    Qwen-VL tensors plus ``labels``.
+    """
+
+    vlm_name: str = "Qwen/Qwen3-VL-8B-Instruct"
+    image_key: str = OBS_IMAGES + ".top"
+    task_key: str = "task"
+    default_task: str | None = None
+    max_frames: int | None = 16
+    fps: float = 2.0
+    prompt_prefix: str = DEFAULT_PROMPT_PREFIX
+    prompt_suffix_template: str = DEFAULT_PROMPT_SUFFIX_TEMPLATE
+    add_chat_template: bool = False
+    max_length: int = 32768
+
+    _processor: Any = field(default=None, init=False, repr=False)
+
+    def __post_init__(self) -> None:
+        require_package("transformers", extra="topreward")
+        self._processor = AutoProcessor.from_pretrained(self.vlm_name, trust_remote_code=True)
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        observation = transition.get(TransitionKey.OBSERVATION)
+        complementary = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
+        if self.image_key not in observation:
+            raise KeyError(f"TOPReward expected image key {self.image_key!r} in observation")
+
+        frames = observation[self.image_key]
+        videos = frames.detach().cpu() if isinstance(frames, Tensor) else torch.as_tensor(frames)
+        videos = _prepare_video_batch(videos, max_frames=self.max_frames)
+
+        batch_size = videos.shape[0]
+        tasks = _expand_tasks(
+            complementary.get(self.task_key, self.default_task),
+            batch_size=batch_size,
+            default=self.default_task,
+        )
+
+        encoded = self._encode_batch(videos, tasks, batch_size)
+
+        new_observation = dict(observation)
+        for key, value in encoded.items():
+            new_observation[f"{TOPREWARD_FEATURE_PREFIX}{key}"] = value
+
+        new_transition = transition.copy()
+        new_transition[TransitionKey.OBSERVATION] = new_observation
+        return new_transition
+
+    def _encode_batch(self, videos: Tensor, tasks: list[str], batch_size) -> dict[str, Any]:
+        """Tokenise a batch of (frames, task) pairs into Qwen-VL tensors.
+
+        The loop only builds per-sample chat strings. Tokenisation, padding,
+        video preprocessing, and label construction are batched.
+        """
+
+        texts: list[str] = []
+        video_metadata = [
+            {
+                "total_num_frames": int(videos.shape[1]),
+                "fps": float(self.fps),
+                "frames_indices": list(range(int(videos.shape[1]))),
+            }
+            for _ in range(batch_size)
+        ]
+        eos_token = self._processor.tokenizer.eos_token
+
+        for i in range(batch_size):
+            instruction_suffix = self.prompt_suffix_template.format(instruction=tasks[i])
+            if self.add_chat_template:
+                suffix_for_template = instruction_suffix.removesuffix(_TRUE_ANSWER).rstrip()
+                templated_messages = [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "video", "video": videos[i], "fps": self.fps},
+                            {"type": "text", "text": f"{self.prompt_prefix}{suffix_for_template}"},
+                        ],
+                    }
+                ]
+                prompt_chat = self._processor.apply_chat_template(
+                    templated_messages, tokenize=False, add_generation_prompt=True
+                )
+                full_text = f"{prompt_chat}{_TRUE_ANSWER}"
+            else:
+                user_messages = [
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "video", "video": videos[i], "fps": self.fps},
+                            {"type": "text", "text": self.prompt_prefix},
+                        ],
+                    }
+                ]
+                prompt_chat = self._processor.apply_chat_template(
+                    user_messages, tokenize=False, add_generation_prompt=False
+                )
+                if eos_token is not None:
+                    prompt_chat = prompt_chat.split(eos_token)[0]
+                full_text = f"{prompt_chat}{instruction_suffix}"
+
+            texts.append(full_text)
+
+        result = self._processor(
+            text=texts,
+            videos=videos,
+            video_metadata=video_metadata,
+            do_sample_frames=False,
+            padding=True,
+            padding_side="left",
+            return_tensors="pt",
+        )
+        input_ids = result["input_ids"]
+
+        if input_ids.shape[-1] > self.max_length:
+            raise ValueError(
+                f"TOPReward input length {input_ids.shape[-1]} exceeds max_length "
+                f"{self.max_length}; lower `max_frames` or raise `max_length`."
+            )
+
+        labels = torch.full_like(input_ids, -100)
+        labels[:, -1] = input_ids[:, -1]
+        result["labels"] = labels
+        return result
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+    def get_config(self) -> dict[str, Any]:
+        return {
+            "vlm_name": self.vlm_name,
+            "image_key": self.image_key,
+            "task_key": self.task_key,
+            "default_task": self.default_task,
+            "max_frames": self.max_frames,
+            "fps": self.fps,
+            "prompt_prefix": self.prompt_prefix,
+            "prompt_suffix_template": self.prompt_suffix_template,
+            "add_chat_template": self.add_chat_template,
+            "max_length": self.max_length,
+        }
+
+
+def make_topreward_pre_post_processors(
+    config: TOPRewardConfig,
+    dataset_stats: dict[str, dict[str, Any]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """Pipeline that pre-encodes frames + task into Qwen-VL tensors.
+
+    The preprocessor adds a batch dimension if needed, runs TOPReward's
+    encoder (which tokenises the full prompt and emits ``labels``), and
+    moves everything to the configured device. The postprocessor is
+    the identity since TOPReward outputs a single reward tensor.
+    """
+    preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+        steps=[
+            AddBatchDimensionProcessorStep(),
+            TOPRewardEncoderProcessorStep(
+                vlm_name=config.vlm_name,
+                image_key=config.image_key,
+                task_key=config.task_key,
+                default_task=config.default_task,
+                max_frames=config.max_frames,
+                fps=config.fps,
+                prompt_prefix=config.prompt_prefix,
+                prompt_suffix_template=config.prompt_suffix_template,
+                add_chat_template=config.add_chat_template,
+                max_length=config.max_input_length,
+            ),
+            DeviceProcessorStep(device=config.device or "cpu"),
+        ],
+        name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+    )
+    postprocessor = PolicyProcessorPipeline(
+        name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+        to_transition=policy_action_to_transition,
+    )
+    return preprocessor, postprocessor
@@ -292,19 +292,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):

    active_cfg = cfg.trainable_config
    processor_pretrained_path = active_cfg.pretrained_path
-    if (
-        getattr(active_cfg, "use_relative_actions", False)
-        and processor_pretrained_path is not None
-        and not cfg.resume
-    ):
-        logging.warning(
-            "use_relative_actions=true with pretrained processors can skip relative transforms if "
-            "the checkpoint processors do not define them. Building processors from current policy config."
-        )
-        processor_pretrained_path = None

    processor_kwargs = {}
-    postprocessor_kwargs = {}
    if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
        processor_kwargs["dataset_stats"] = dataset.meta.stats

@@ -312,24 +301,31 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        processor_kwargs["dataset_meta"] = dataset.meta

    if not cfg.is_reward_model_training and processor_pretrained_path is not None:
-        processor_kwargs["preprocessor_overrides"] = {
+        preprocessor_overrides = {
            "device_processor": {"device": device.type},
            "normalizer_processor": {
                "stats": dataset.meta.stats,
                "features": {**policy.config.input_features, **policy.config.output_features},
                "norm_map": policy.config.normalization_mapping,
            },
+            "rename_observations_processor": {"rename_map": cfg.rename_map},
        }
-        processor_kwargs["preprocessor_overrides"]["rename_observations_processor"] = {
-            "rename_map": cfg.rename_map
-        }
-        postprocessor_kwargs["postprocessor_overrides"] = {
+        postprocessor_overrides = {
            "unnormalizer_processor": {
                "stats": dataset.meta.stats,
                "features": policy.config.output_features,
                "norm_map": policy.config.normalization_mapping,
            },
        }
+        if getattr(active_cfg, "use_relative_actions", False):
+            preprocessor_overrides["relative_actions_processor"] = {
+                "enabled": True,
+                "exclude_joints": getattr(active_cfg, "relative_exclude_joints", []),
+                "action_names": getattr(active_cfg, "action_feature_names", None),
+            }
+            postprocessor_overrides["absolute_actions_processor"] = {"enabled": True}
+        processor_kwargs["preprocessor_overrides"] = preprocessor_overrides
+        processor_kwargs["postprocessor_overrides"] = postprocessor_overrides

    if cfg.is_reward_model_training:
        preprocessor, postprocessor = make_reward_pre_post_processors(
@@ -341,7 +337,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
            policy_cfg=cfg.policy,
            pretrained_path=processor_pretrained_path,
            **processor_kwargs,
-            **postprocessor_kwargs,
        )

    if is_main_process:
@@ -13,6 +13,10 @@
 A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.
 {% elif model_name == "sarm" %}
 A Success-Aware Reward Model (SARM) predicts a dense reward signal from observations, typically used downstream for reinforcement learning or human-in-the-loop fine-tuning when task success is not directly observable.
+{% elif model_name == "robometer" %}
+ROBOMETER is a general-purpose video-language robotic reward model built on a fine-tuned Qwen3-VL-4B backbone with progress, preference, and success heads. Given a trajectory video and a task description, it predicts dense, frame-level task progress in [0, 1] and frame-level success probabilities for downstream robot learning, including offline RL, online RL, data filtering and retrieval, and automated failure detection.
+{% elif model_name == "topreward" %}
+TOPReward is a **zero-shot** reward model that extracts token log-probabilities from an off-the-shelf vision-language model (default Qwen3-VL) as a reward signal. Given a video trajectory and a task instruction, it returns the VLM's log-likelihood of the instruction being true, with no fine-tuning required.
 {% else %}
 _Reward model type not recognized — please update this template._
 {% endif %}
@@ -0,0 +1,2 @@
+# Local-only parity artifacts (regenerated via dump_original_n1_7.py); never committed.
+*.npz
@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-"""Test script for LeRobot's Groot policy forward and inference passes."""
+"""Test script for LeRobot's GR00T N1.7 policy forward and inference passes."""

 import gc
 import os
@@ -41,13 +41,20 @@ pytestmark = pytest.mark.skipif(
 )


-# Define constants for dummy data
+# Define constants for dummy data (GR00T N1.7 native conventions).
+# N1.7 internally uses a 40-step action chunk, 132-dim state/action, and 256px images
+# (see GrootConfig.__post_init__). Use a chunk-sized action horizon so the dummy batch
+# matches the model's native action space.
 DUMMY_STATE_DIM = 44
 DUMMY_ACTION_DIM = 44
-DUMMY_ACTION_HORIZON = 16
+DUMMY_ACTION_HORIZON = 40
 IMAGE_SIZE = 256
 DEVICE = auto_select_torch_device()
-MODEL_PATH = "aractingi/bimanual-handover-groot-10k"
+# GR00T N1.7 checkpoint (N1.5 is no longer supported). The N1.7-3B base model loads
+# via GrootPolicy.from_pretrained with root-level sharded safetensors.
+MODEL_PATH = "nvidia/GR00T-N1.7-3B"
+# Valid N1.7 embodiment tag carried by the checkpoint metadata.
+EMBODIMENT_TAG = "gr1_unified"


 def cleanup_memory():
@@ -88,13 +95,13 @@ def instantiate_lerobot_groot(
    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
    PolicyProcessorPipeline[PolicyAction, PolicyAction],
 ]:
-    """Instantiate LeRobot Groot policy with preprocessor and postprocessor."""
+    """Instantiate LeRobot GR00T N1.7 policy with preprocessor and postprocessor."""
    if from_pretrained:
        policy = GrootPolicy.from_pretrained(
            pretrained_name_or_path=model_path,
            strict=False,
        )
-        policy.config.embodiment_tag = "gr1"
+        policy.config.embodiment_tag = EMBODIMENT_TAG
    else:
        config = GrootConfig(
            base_model_path=model_path,
@@ -102,7 +109,7 @@ def instantiate_lerobot_groot(
            chunk_size=DUMMY_ACTION_HORIZON,
            image_size=[IMAGE_SIZE, IMAGE_SIZE],
            device=DEVICE,
-            embodiment_tag="gr1",
+            embodiment_tag=EMBODIMENT_TAG,
        )
        policy = GrootPolicy(config)

@@ -148,8 +155,8 @@ def create_dummy_data(device=DEVICE):

@require_cuda
 def test_lerobot_groot_inference():
-    """Test the inference pass (select_action) of LeRobot's Groot policy."""
-    print("Test: LeRobot Groot Inference Pass")
+    """Test the inference pass (select_action) of LeRobot's GR00T N1.7 policy."""
+    print("Test: LeRobot GR00T N1.7 Inference Pass")

    set_seed_all(42)

@@ -181,9 +188,9 @@ def test_lerobot_groot_inference():

@require_cuda
 def test_lerobot_groot_forward_pass():
-    """Test the forward pass of LeRobot's Groot policy."""
+    """Test the forward pass of LeRobot's GR00T N1.7 policy."""
    print("\n" + "=" * 50)
-    print("Test: LeRobot Groot Forward Pass (Training Mode)")
+    print("Test: LeRobot GR00T N1.7 Forward Pass (Training Mode)")

    set_seed_all(42)

@@ -200,6 +207,11 @@ def test_lerobot_groot_forward_pass():
    with torch.no_grad():
        lerobot_loss, lerobot_metrics = lerobot_policy.forward(batch_lerobot_processed)

+    assert isinstance(lerobot_loss, torch.Tensor)
+    assert torch.isfinite(lerobot_loss).all()
+    assert "loss" in lerobot_metrics
+    assert np.isfinite(lerobot_metrics["loss"])
+
    print("\nForward pass successful.")
    print(f"  - Loss: {lerobot_loss.item():.6f}")
    print(f"  - Metrics: {lerobot_metrics}")
@@ -1,6 +1,6 @@
 #!/usr/bin/env python

-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,431 +14,343 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-"""Test script to verify Groot policy integration with LeRobot vs the original implementation, only meant to be run locally!"""
+"""Parity tests: original NVIDIA GR00T N1.7 vs the GR00T N1.7 integration in LeRobot.
+
+Two comparisons run per embodiment tag, against per-tag ``.npz`` artifacts produced
+once in the original ``gr00t`` env by the companion script
+``utils/dump_original_n1_7.py`` (in the ``utils`` package next to this file):
+
+1. **Model parity** -- the self-contained LeRobot reimplementation of the GR00T N1.7
+   action head + Qwen3-VL backbone must produce the SAME raw model output
+   (``action_pred``, the normalized flow-matching prediction before any action
+   decoding) as NVIDIA's original ``gr00t`` package, given byte-identical
+   pre-processed inputs and the flow-matching seed recorded in the artifact.
+2. **Preprocessor parity** -- LeRobot's own preprocessor pipeline (real Qwen3-VL chat
+   template / tokenizer / image packing + state normalization, no mocks) must produce
+   the SAME collated model inputs (``input_ids``, ``pixel_values``, ``state``, ...)
+   as the original package's processor, given the identical raw observations
+   (images, state, language) recorded in the artifact. Artifacts written by older
+   versions of the dump script carry no raw observations; this case then SKIPS with
+   a regeneration hint.
+
+These tests are LOCAL-only and skip on CI, when ``gr00t``-side prerequisites are not
+present, or when no artifact has been generated. By default they look for artifacts in
+``<this dir>/artifacts/``; override with ``GROOT_N1_7_PARITY_DIR``. See the
+"Original-vs-LeRobot parity test" section of ``src/lerobot/policies/groot/README.md``
+for the full run procedure.
+"""

-import gc
 import os
-from copy import deepcopy
+import warnings
+from pathlib import Path
 from typing import Any

 import numpy as np
 import pytest
 import torch

-from lerobot.policies.groot.configuration_groot import GrootConfig
-from lerobot.policies.groot.modeling_groot import GrootPolicy
-from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
-from lerobot.processor import PolicyProcessorPipeline
-from lerobot.types import PolicyAction
-
-pytest.importorskip("gr00t")
-pytest.importorskip("transformers")
-
 pytestmark = pytest.mark.skipif(
    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="This test requires local Groot installation and is not meant for CI",
+    reason="Requires a local GR00T N1.7 checkpoint + pre-generated artifacts; not for CI.",
 )

+from lerobot.policies.groot.configuration_groot import GROOT_N1_7  # noqa: E402,F401
+from lerobot.utils.constants import OBS_IMAGES, OBS_STATE  # noqa: E402

-from gr00t.data.dataset import ModalityConfig  # noqa: E402
-from gr00t.data.embodiment_tags import EmbodimentTag  # noqa: E402
-from gr00t.data.transform.base import ComposedModalityTransform  # noqa: E402
-from gr00t.model.policy import Gr00tPolicy  # noqa: E402
+# Fallback flow-matching seed for artifacts predating the recorded ``seed`` field.
+SEED = 42
+DEVICE = os.environ.get("GROOT_PARITY_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
+ATOL = float(os.environ.get("GROOT_PARITY_ATOL", "1e-3"))
+RTOL = float(os.environ.get("GROOT_PARITY_RTOL", "1e-3"))

-# GR1 humanoid dimensions (from pretrained model metadata)
-# The actual GR1 robot has 44 dimensions for both state and action
-# GR00TTransform will pad state to 64 and truncate action to 32
-DUMMY_STATE_DIM = 44
-DUMMY_ACTION_DIM = 44
-DUMMY_ACTION_HORIZON = 16
-IMAGE_SIZE = 256
-DEVICE = "cpu"
-MODEL_PATH = "nvidia/GR00T-N1.5-3B"
+# Artifact filenames are original_n1_7_<embodiment_tag>.npz
+_ARTIFACT_PREFIX = "original_n1_7_"
+_ARTIFACT_SUFFIX = ".npz"

-GR1_BODY_PARTS = {
-    "left_arm": 7,
-    "left_hand": 6,
-    "left_leg": 6,
-    "neck": 3,
-    "right_arm": 7,
-    "right_hand": 6,
-    "right_leg": 6,
-    "waist": 3,
-}
+# Collated keys compared by the preprocessor parity case: integer/id tensors must
+# match exactly; float tensors within ATOL/RTOL.
+_COLLATED_EXACT_KEYS = ("input_ids", "attention_mask", "image_grid_thw", "embodiment_id")
+_COLLATED_CLOSE_KEYS = ("pixel_values", "state")


-def cleanup_memory():
-    """Clean up GPU/MPS memory to prevent OOM errors between tests."""
-    print("\nCleaning up memory...")
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        torch.cuda.synchronize()
-    if torch.backends.mps.is_available():
-        torch.mps.empty_cache()
-    print("Memory cleanup complete.")
+def _artifact_dir() -> Path:
+    """Directory holding the per-embodiment .npz artifacts.
+
+    Self-contained by default: a sibling ``artifacts/`` directory next to this test.
+    Override with ``GROOT_N1_7_PARITY_DIR`` (e.g. to point at a scratch location).
+    The directory is read-only here -- it is populated by ``utils/dump_original_n1_7.py``
+    run in the original gr00t environment; the test never creates it.
+    """
+    env = os.environ.get("GROOT_N1_7_PARITY_DIR")
+    if env:
+        return Path(env)
+    return Path(__file__).resolve().parent / "artifacts"


-def set_seed_all(seed: int):
-    """Set random seed for all RNG sources to ensure reproducibility."""
-    import random
-
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-
-    # Set deterministic behavior
-    torch.backends.cudnn.deterministic = True
-    torch.backends.cudnn.benchmark = False
-    torch.use_deterministic_algorithms(True, warn_only=True)
+def _discover_artifacts() -> list[tuple[str, Path]]:
+    """Return [(embodiment_tag, npz_path), ...] for every dumped artifact."""
+    d = _artifact_dir()
+    if not d.is_dir():
+        return []
+    out = []
+    for p in sorted(d.glob(f"{_ARTIFACT_PREFIX}*{_ARTIFACT_SUFFIX}")):
+        tag = p.name[len(_ARTIFACT_PREFIX) : -len(_ARTIFACT_SUFFIX)]
+        out.append((tag, p))
+    return out


-def instantiate_lerobot_groot(
-    from_pretrained: bool = False,
-    model_path: str = MODEL_PATH,
-) -> tuple[
-    GrootPolicy,
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Instantiate LeRobot Groot policy with preprocessor and postprocessor."""
-    if from_pretrained:
-        policy = GrootPolicy.from_pretrained(
-            pretrained_name_or_path=model_path,
-            strict=False,
+def _resolve_checkpoint() -> str:
+    env = os.environ.get("GROOT_N1_7_LIBERO_CKPT")
+    if env:
+        if not Path(env).exists():
+            pytest.skip(f"GROOT_N1_7_LIBERO_CKPT={env} does not exist")
+        return env
+    try:
+        from huggingface_hub import snapshot_download
+
+        root = snapshot_download(
+            "nvidia/GR00T-N1.7-LIBERO",
+            local_files_only=True,
+            allow_patterns=["libero_10/*"],
        )
-        policy.config.embodiment_tag = "gr1"
+    except Exception as exc:  # noqa: BLE001
+        pytest.skip(f"GR00T N1.7 LIBERO checkpoint not available locally: {exc}")
+    ckpt = Path(root) / "libero_10"
+    if not (ckpt / "config.json").exists():
+        pytest.skip(f"GR00T N1.7 LIBERO checkpoint incomplete at {ckpt}")
+    return str(ckpt)
+
+
+def _load_artifact(path: Path) -> tuple[torch.Tensor, dict[str, torch.Tensor], int]:
+    """Return (original action_pred, collated model inputs, flow-matching seed)."""
+    data = np.load(path, allow_pickle=True)
+    original_action = torch.from_numpy(data["action_pred"]).float()
+    if "seed" in data.files:
+        seed = int(data["seed"])
    else:
-        config = GrootConfig(
-            base_model_path=model_path,
-            n_action_steps=DUMMY_ACTION_HORIZON,
-            chunk_size=DUMMY_ACTION_HORIZON,
-            image_size=[IMAGE_SIZE, IMAGE_SIZE],
-            device=DEVICE,
-            embodiment_tag="gr1",
+        warnings.warn(
+            f"Artifact '{path.name}' does not record the producer seed (it predates the current "
+            f"dump_original_n1_7.py); falling back to seed={SEED}. If the parity comparison fails, "
+            "regenerate the artifact with the current dump script.",
+            stacklevel=2,
        )
-        policy = GrootPolicy(config)
-
-    policy.to(DEVICE)
-    policy.config.device = DEVICE
-
-    preprocessor, postprocessor = make_groot_pre_post_processors(
-        config=policy.config,
-        dataset_stats=None,  # Pass None for dataset_stats to disable normalization (original GR00T doesn't normalize)
-    )
-
-    return (policy, preprocessor, postprocessor)
+        seed = SEED
+    dtypes = dict(zip(data["meta_keys"].tolist(), data["meta_dtypes"].tolist(), strict=False))
+    inputs = {}
+    for key in data.files:
+        if not key.startswith("in::"):
+            continue
+        name = key[4:]
+        arr = data[key]
+        t = torch.from_numpy(np.asarray(arr))
+        declared = dtypes.get(key, "")
+        if "int" in declared or "long" in declared:
+            t = t.long()
+        inputs[name] = t
+    return original_action, inputs, seed


-def instantiate_original_groot(
-    from_pretrained: bool = False,
-    model_path: str = MODEL_PATH,
-):
-    """Instantiate original Groot policy from NVIDIA's implementation."""
-    from gr00t.data.transform.concat import ConcatTransform
-    from gr00t.data.transform.state_action import StateActionToTensor
-    from gr00t.data.transform.video import VideoToNumpy, VideoToTensor
-    from gr00t.model.transforms import GR00TTransform
+def _load_raw_observation(path: Path) -> dict[str, Any] | None:
+    """Return the raw observation recorded in the artifact, or None for old artifacts.

-    video_keys = ["video.ego_view"]
-    state_keys = [
-        "state"
-    ]  # Important: Use single concatenated "state" key (not split body parts) to match preprocessing
-    action_keys = [
-        "action.left_arm",
-        "action.right_arm",
-        "action.left_hand",
-        "action.right_hand",
-        "action.left_leg",
-        "action.right_leg",
-        "action.neck",
-        "action.waist",
-    ]
-    language_keys = ["annotation.human.action.task_description"]
-
-    modality_config = {
-        "video": ModalityConfig(
-            delta_indices=[0],  # Current frame only
-            modality_keys=video_keys,
-        ),
-        "state": ModalityConfig(
-            delta_indices=[0],
-            modality_keys=state_keys,
-        ),
-        "action": ModalityConfig(
-            delta_indices=list(range(DUMMY_ACTION_HORIZON)),
-            modality_keys=action_keys,
-        ),
-        "language": ModalityConfig(
-            delta_indices=[0],
-            modality_keys=language_keys,
-        ),
+    Artifacts produced by the current ``dump_original_n1_7.py`` additionally store the
+    exact raw observation the producer fed to the original processor: per-camera uint8
+    frames (``raw::video.<key>``, (B, T, H, W, C)), per-key state vectors
+    (``raw::state.<key>``, (B, T, dim)) and the language instruction
+    (``raw::language``, one string per batch element). ``raw_video_keys`` /
+    ``raw_state_keys`` record the checkpoint modality-key order.
+    """
+    data = np.load(path, allow_pickle=True)
+    markers = ("raw_video_keys", "raw_state_keys", "raw::language")
+    if any(marker not in data.files for marker in markers):
+        return None
+    video_keys = [str(k) for k in data["raw_video_keys"].tolist()]
+    state_keys = [str(k) for k in data["raw_state_keys"].tolist()]
+    return {
+        "video": {k: data[f"raw::video.{k}"] for k in video_keys},
+        "state": {k: data[f"raw::state.{k}"] for k in state_keys},
+        "language": [str(t) for t in data["raw::language"].tolist()],
    }

-    modality_transform = ComposedModalityTransform(
-        transforms=[
-            VideoToTensor(apply_to=video_keys),
-            VideoToNumpy(apply_to=video_keys),  # Convert to numpy (GR00TTransform expects numpy arrays)
-            # State is already a single concatenated key, so no StateActionToTensor needed
-            # Convert action from numpy to tensor
-            StateActionToTensor(apply_to=action_keys),
-            # Concatenate only video and actions (state is already single key)
-            ConcatTransform(
-                video_concat_order=video_keys,
-                state_concat_order=[],  # Empty:state is already single key
-                action_concat_order=action_keys,
-            ),
-            GR00TTransform(
-                max_state_dim=64,
-                max_action_dim=32,
-                state_horizon=1,
-                action_horizon=DUMMY_ACTION_HORIZON,
-                training=False,
-            ),
-        ]
-    )
-
-    policy = Gr00tPolicy(
-        model_path=model_path,
-        embodiment_tag=EmbodimentTag.GR1,
-        modality_config=modality_config,
-        modality_transform=modality_transform,
-        device=DEVICE,
-    )
-
-    return policy, modality_config, modality_transform
-
-
-def create_dummy_data(device=DEVICE):
-    """Create dummy data for testing both implementations."""
-    batch_size = 2
-    prompt = "Pick up the red cube and place it in the bin"
-    state = torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device)
-
-    batch = {
-        "observation.state": state,
-        "action": torch.randn(
-            batch_size,
-            DUMMY_ACTION_HORIZON,
-            DUMMY_ACTION_DIM,
-            dtype=torch.float32,
-            device=device,  # Action ground truth (for training)
-        ),
-        "observation.images.ego_view": torch.rand(
-            batch_size,
-            3,
-            IMAGE_SIZE,
-            IMAGE_SIZE,
-            dtype=torch.float32,
-            device=device,  # Images in [0, 1] range as expected by LeRobot
-        ),
-        "task": [prompt for _ in range(batch_size)],
-    }

+def _raw_observation_to_lerobot_batch(raw: dict[str, Any]) -> dict[str, Any]:
+    """Convert the producer's raw observation into a LeRobot policy batch."""
+    batch: dict[str, Any] = {}
+    for key, frames in raw["video"].items():
+        # (B, T, H, W, C) uint8 -> (B, T, C, H, W); the pack step converts back losslessly.
+        batch[f"{OBS_IMAGES}.{key}"] = torch.from_numpy(frames).permute(0, 1, 4, 2, 3).contiguous()
+    # observation.state is the per-key state vectors (latest frame) concatenated in
+    # checkpoint modality-key order -- the layout the LeRobot pack step and the
+    # flattened checkpoint statistics expect.
+    state_parts = [torch.from_numpy(np.asarray(arr)[:, -1, :]).float() for arr in raw["state"].values()]
+    batch[OBS_STATE] = torch.cat(state_parts, dim=-1)
+    batch["task"] = list(raw["language"])
    return batch


-def convert_lerobot_to_original_format(batch, modality_config):
-    """Convert LeRobot batch format to original Groot format.
+def _unflatten(inputs: dict[str, torch.Tensor]) -> dict:
+    """Rebuild the nested model-input dict from dot-prefixed flat keys."""
+    nested: dict = {}
+    for dotted, value in inputs.items():
+        parts = dotted.split(".")
+        cur = nested
+        for p in parts[:-1]:
+            cur = cur.setdefault(p, {})
+        cur[parts[-1]] = value
+    return nested.get("inputs", nested)

-    The original Groot expects observations in this format:
-    {
-        "video.<camera_name>": np.ndarray (T, H, W, C) or (B, T, H, W, C)
-        "state.<state_component>": np.ndarray (T, D) or (B, T, D)
-        "action.<action_component>": np.ndarray (T, D) or (B, T, D)
-        "annotation.<annotation_type>": str or list[str]
-    }
+
+def _assert_collated_parity(
+    embodiment_tag: str, name: str, lerobot_value: Any, original_value: torch.Tensor, *, exact: bool
+) -> None:
+    """Compare one collated tensor produced by LeRobot against the original's."""
+    assert isinstance(lerobot_value, torch.Tensor), (
+        f"[{embodiment_tag}] LeRobot preprocessor output '{name}' is "
+        f"{type(lerobot_value).__name__}, expected a tensor."
+    )
+    lerobot_t = lerobot_value.detach().cpu()
+    original_t = original_value.detach().cpu()
+    assert lerobot_t.shape == original_t.shape, (
+        f"[{embodiment_tag}] collated '{name}' shape mismatch: lerobot={tuple(lerobot_t.shape)} vs "
+        f"original={tuple(original_t.shape)}."
+    )
+    if exact:
+        mismatched = int((lerobot_t.long() != original_t.long()).sum())
+        assert mismatched == 0, (
+            f"[{embodiment_tag}] collated '{name}' differs from the original processor output: "
+            f"{mismatched}/{original_t.numel()} elements mismatch."
+        )
+    else:
+        lerobot_f, original_f = lerobot_t.float(), original_t.float()
+        max_diff = (lerobot_f - original_f).abs().max().item()
+        print(f"[{embodiment_tag}] {name}: shape {tuple(lerobot_t.shape)} max|diff|={max_diff:.6e}")
+        assert torch.allclose(lerobot_f, original_f, atol=ATOL, rtol=RTOL), (
+            f"[{embodiment_tag}] collated '{name}' differs from the original processor output beyond "
+            f"atol={ATOL}, rtol={RTOL}: max|diff|={max_diff:.6e}."
+        )
+
+
+@pytest.fixture(scope="module")
+def lerobot_model():
+    """Load the LeRobot GR00T N1.7 model once (fp32 + SDPA) and reuse across tags."""
+    ckpt = _resolve_checkpoint()
+    from lerobot.policies.groot.groot_n1_7 import GR00TN17
+
+    model = GR00TN17.from_pretrained(
+        ckpt,
+        tune_llm=False,
+        tune_visual=False,
+        tune_projector=False,
+        tune_diffusion_model=False,
+        tune_vlln=False,
+        transformers_loading_kwargs={"trust_remote_code": True},
+    )
+    # fp32 + SDPA on both sides: bf16 + differing attention kernels otherwise introduce
+    # ~1e-2 numerical noise unrelated to the implementations.
+    model.compute_dtype = "float32"
+    model.config.compute_dtype = model.compute_dtype
+    model.to(device=DEVICE, dtype=torch.float32)
+    model.eval()
+    return model
+
+
+_ARTIFACTS = _discover_artifacts()
+
+_requires_artifacts = pytest.mark.skipif(
+    not _ARTIFACTS,
+    reason=(
+        "No GR00T N1.7 parity artifacts found. Generate them first in the original gr00t "
+        "env:\n  .venv-original/bin/python tests/policies/groot/utils/dump_original_n1_7.py "
+        "--ckpt <ckpt> --out-dir tests/policies/groot/artifacts --device cuda"
+    ),
+)
+
+
+@_requires_artifacts
+@pytest.mark.parametrize("embodiment_tag,artifact", _ARTIFACTS, ids=[t for t, _ in _ARTIFACTS])
+def test_groot_get_action_parity(embodiment_tag, artifact, lerobot_model):
+    """Raw model.get_action(action_pred) parity per embodiment: original vs LeRobot."""
+    original_action, flat_inputs, seed = _load_artifact(artifact)
+    model_inputs = _unflatten(flat_inputs)
+
+    # Align the flow-matching RNG exactly as the producer did (seed right before sampling).
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    with torch.inference_mode():
+        out = lerobot_model.get_action(model_inputs)
+    lerobot_action = out["action_pred"].float().cpu()
+
+    assert lerobot_action.shape == original_action.shape, (
+        f"GR00T N1.7 action_pred shape mismatch for embodiment '{embodiment_tag}': "
+        f"lerobot={tuple(lerobot_action.shape)} vs original={tuple(original_action.shape)}. "
+        "The same checkpoint and inputs must produce identical shapes; this indicates an "
+        "action-horizon or action-dim regression (or a stale artifact -- regenerate it with "
+        "utils/dump_original_n1_7.py)."
+    )
+
+    diff = torch.abs(lerobot_action - original_action)
+    max_diff = diff.max().item()
+    print(
+        f"\n[{embodiment_tag}] shapes lerobot={tuple(lerobot_action.shape)} "
+        f"original={tuple(original_action.shape)}  "
+        f"max|diff|={max_diff:.6e}  mean|diff|={diff.mean().item():.6e}"
+    )
+
+    assert torch.allclose(lerobot_action, original_action, atol=ATOL, rtol=RTOL), (
+        f"GR00T N1.7 raw action_pred differs for embodiment '{embodiment_tag}' beyond "
+        f"atol={ATOL}, rtol={RTOL}: max|diff|={max_diff:.6e}"
+    )
+
+
+@_requires_artifacts
+@pytest.mark.parametrize("embodiment_tag,artifact", _ARTIFACTS, ids=[t for t, _ in _ARTIFACTS])
+def test_groot_preprocessor_parity(embodiment_tag, artifact):
+    """LeRobot's real preprocessor vs the original's collated tensors, from identical raw obs.
+
+    Runs LeRobot's full preprocessor pipeline -- including the real Qwen3-VL chat
+    template, tokenizer and image packing plus the checkpoint-driven state
+    normalization (no mocks) -- on the raw observations recorded in the artifact, and
+    compares every collated model input against the ones the original ``gr00t``
+    processor produced from the same raw observations.
    """
-    # Original Groot expects (T, H, W, C) format for images
-    # LeRobot has (B, C, H, W) format, so we need to convert
-    observation = {}
+    raw = _load_raw_observation(artifact)
+    if raw is None:
+        pytest.skip(
+            f"Artifact '{artifact.name}' was produced by an older dump_original_n1_7.py that does "
+            "not record raw observations; regenerate it with the current dump script to run the "
+            "preprocessor parity case."
+        )
+    _, flat_inputs, _ = _load_artifact(artifact)
+    original_inputs = _unflatten(flat_inputs)

-    for img_key in ["ego_view"]:
-        lerobot_key = f"observation.images.{img_key}"
-        if lerobot_key in batch:
-            img = batch[lerobot_key]
-            # Convert from (B, C, H, W) to (B, T=1, H, W, C)
-            img_np = img.permute(0, 2, 3, 1).unsqueeze(1).cpu().numpy()
-            # Convert [0, 1] to [0, 255] uint8 as expected by original
-            img_np = (img_np * 255).astype(np.uint8)
-            observation[f"video.{img_key}"] = img_np
+    ckpt = _resolve_checkpoint()
+    from lerobot.policies.groot.configuration_groot import GrootConfig
+    from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors

-    # Important: The Original's GR00TTransform expects "state" as (B, T, D), not split body parts
-    if "observation.state" in batch:
-        state = batch["observation.state"]
-        state_np = state.unsqueeze(1).cpu().numpy()  # (B, 1, D)
-        observation["state"] = state_np
+    # CPU keeps this case runnable without a GPU; the preprocessor is deterministic.
+    config = GrootConfig(base_model_path=ckpt, embodiment_tag=embodiment_tag, device="cpu")
+    preprocessor, _ = make_groot_pre_post_processors(config)

-    if "action" in batch:
-        action = batch["action"]
-        action_np = action.cpu().numpy()
+    processed = preprocessor(_raw_observation_to_lerobot_batch(raw))

-        start_idx = 0
-        for part_name, part_dim in GR1_BODY_PARTS.items():
-            end_idx = start_idx + part_dim
-            observation[f"action.{part_name}"] = action_np[:, :, start_idx:end_idx]
-            start_idx = end_idx
-
-    if "task" in batch:
-        task_list = batch["task"]
-        # GR00TTransform expects language with (B, T) shape for batched data
-        # Create a (B, T=1) array where each element is the string directly
-        bsz = len(task_list)
-        task_array = np.empty((bsz, 1), dtype=object)
-        for i in range(bsz):
-            task_array[i, 0] = task_list[i]  # Assign string directly to each (i, 0) position
-        observation["annotation.human.action.task_description"] = task_array
-
-    return observation
-
-
-def test_groot_original_vs_lerobot_pretrained():
-    """Test Groot original implementation vs LeRobot implementation with pretrained weights."""
-    print("Test: Groot Original vs LeRobot with Pretrained Weights (Inference)")
-
-    set_seed_all(42)
-
-    lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_groot(
-        from_pretrained=True
+    compared_keys = (*_COLLATED_EXACT_KEYS, *_COLLATED_CLOSE_KEYS)
+    missing_original = [k for k in compared_keys if k not in original_inputs]
+    missing_lerobot = [k for k in compared_keys if k not in processed]
+    assert not missing_original, (
+        f"[{embodiment_tag}] artifact collated inputs miss {missing_original} "
+        f"(available: {sorted(original_inputs)}); regenerate the artifact with the current dump script."
    )
-    original_policy, modality_config, modality_transform = instantiate_original_groot(from_pretrained=True)
-
-    batch = create_dummy_data()
-    batch_lerobot = deepcopy(batch)
-
-    print("\n[LeRobot] Running inference...")
-    lerobot_policy.eval()
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
-
-    # Important: Reset seed immediately before inference to ensure identical RNG state
-    torch.manual_seed(42)
-
-    with torch.no_grad():
-        lerobot_actions = lerobot_policy.select_action(batch_lerobot_processed)
-
-    print("\n[Original] Running inference...")
-    original_policy.model.eval()
-    observation = convert_lerobot_to_original_format(batch, modality_config)
-    original_obs_transformed = modality_transform(deepcopy(observation))
-
-    # Important: Reset seed immediately before inference to ensure identical RNG state
-    torch.manual_seed(42)
-
-    with torch.no_grad():
-        original_model_output = original_policy.model.get_action(original_obs_transformed)
-        original_actions_raw = original_model_output["action_pred"]  # [2, 16, 32]
-    # Take first timestep
-    original_actions = original_actions_raw[:, 0, :].to(lerobot_actions.device).to(lerobot_actions.dtype)
-
-    print("Action Comparison:")
-    diff = lerobot_actions - original_actions
-    abs_diff = torch.abs(diff)
-
-    for batch_idx in range(lerobot_actions.shape[0]):
-        print(f"\n{'=' * 60}")
-        print(f"Batch {batch_idx}")
-        print(f"{'=' * 60}")
-        print(f"{'Idx':<5} {'LeRobot':<14} {'Original':<14} {'Difference':<14}")
-        print("-" * 60)
-        for action_idx in range(lerobot_actions.shape[1]):
-            lr_val = lerobot_actions[batch_idx, action_idx].item()
-            orig_val = original_actions[batch_idx, action_idx].item()
-            diff_val = abs(lr_val - orig_val)
-            sign = "+" if (lr_val - orig_val) > 0 else "-"
-            print(f"{action_idx:<5} {lr_val:>13.6f} {orig_val:>13.6f} {sign}{diff_val:>12.6f}")
-
-    max_diff = abs_diff.max().item()
-    tolerance = 0.001
-    assert torch.allclose(lerobot_actions, original_actions, atol=tolerance), (
-        f"Actions differ by more than tolerance ({tolerance}): max diff = {max_diff:.6f}"
+    assert not missing_lerobot, (
+        f"[{embodiment_tag}] LeRobot preprocessor output misses {missing_lerobot} (tensor keys "
+        f"available: {sorted(k for k, v in processed.items() if isinstance(v, torch.Tensor))})."
    )
-    print(f"\nSuccess: Actions match within tolerance ({tolerance})!")

-    del lerobot_policy, lerobot_preprocessor, lerobot_postprocessor
-    del original_policy, modality_config, modality_transform
-    del batch, batch_lerobot, observation
-    cleanup_memory()
-
-
-def test_groot_forward_pass_comparison():
-    """Test forward pass comparison between LeRobot and Original Groot implementations."""
-    print("Test: Forward Pass Comparison (Training Mode)")
-
-    set_seed_all(42)
-
-    lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_groot(
-        from_pretrained=True
-    )
-    original_policy, modality_config, modality_transform = instantiate_original_groot(from_pretrained=True)
-
-    batch = create_dummy_data()
-    lerobot_policy.eval()
-    original_policy.model.eval()
-
-    print("\n[LeRobot] Running forward pass...")
-    batch_lerobot = deepcopy(batch)
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
-
-    set_seed_all(42)
-    with torch.no_grad():
-        lerobot_loss, lerobot_metrics = lerobot_policy.forward(batch_lerobot_processed)
-
-    print(f"  Loss: {lerobot_loss.item():.6f}")
-
-    print("\n[Original] Running forward pass...")
-    observation = convert_lerobot_to_original_format(batch, modality_config)
-    transformed_obs = modality_transform(observation)
-
-    if "action" not in transformed_obs:
-        action_for_forward = batch_lerobot_processed["action"]
-        action_mask_for_forward = batch_lerobot_processed["action_mask"]
-
-        # Match action horizon if needed
-        if action_for_forward.shape[1] != original_policy.model.action_horizon:
-            if action_for_forward.shape[1] < original_policy.model.action_horizon:
-                pad_size = original_policy.model.action_horizon - action_for_forward.shape[1]
-                last_action = action_for_forward[:, -1:, :]
-                padding = last_action.repeat(1, pad_size, 1)
-                action_for_forward = torch.cat([action_for_forward, padding], dim=1)
-
-                mask_padding = torch.zeros(
-                    action_mask_for_forward.shape[0],
-                    pad_size,
-                    action_mask_for_forward.shape[2],
-                    dtype=action_mask_for_forward.dtype,
-                    device=action_mask_for_forward.device,
-                )
-                action_mask_for_forward = torch.cat([action_mask_for_forward, mask_padding], dim=1)
-            else:
-                action_for_forward = action_for_forward[:, : original_policy.model.action_horizon, :]
-                action_mask_for_forward = action_mask_for_forward[
-                    :, : original_policy.model.action_horizon, :
-                ]
-
-        transformed_obs["action"] = action_for_forward
-        transformed_obs["action_mask"] = action_mask_for_forward
-
-    set_seed_all(42)
-    with torch.no_grad():
-        original_outputs = original_policy.model.forward(transformed_obs)
-
-    original_loss = original_outputs["loss"]
-    print(f"  Loss: {original_loss.item():.6f}")
-
-    loss_diff = abs(lerobot_loss.item() - original_loss.item())
-    loss_rel_diff = loss_diff / (abs(original_loss.item()) + 1e-8) * 100
-
-    print("\nLoss Values:")
-    print(f"  LeRobot: {lerobot_loss.item():.6f}")
-    print(f"  Original: {original_loss.item():.6f}")
-    print(f"  Absolute difference: {loss_diff:.6f}")
-    print(f"  Relative difference: {loss_rel_diff:.2f}%")
-
-    del lerobot_policy, lerobot_preprocessor, lerobot_postprocessor
-    del original_policy, modality_config, modality_transform
-    del batch, batch_lerobot, observation, transformed_obs
-    cleanup_memory()
+    for name in compared_keys:
+        _assert_collated_parity(
+            embodiment_tag,
+            name,
+            processed[name],
+            original_inputs[name],
+            exact=name in _COLLATED_EXACT_KEYS,
+        )
@@ -0,0 +1 @@
+"""Utilities shared by GR00T policy tests."""
@@ -0,0 +1,225 @@
+#!/usr/bin/env python
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License").
+"""Producer (run in the ORIGINAL gr00t env): dump original GR00T N1.7 outputs + inputs.
+
+The original NVIDIA ``gr00t`` package pins ``transformers==4.57.3`` (py3.10) and its
+model-config dataclasses are incompatible with the ``transformers==5.x`` that the
+LeRobot GR00T N1.7 integration requires. The two implementations therefore cannot be
+imported in the same Python process. To keep the parity comparison FAIR, we run the
+original model in its native env here and serialize, PER EMBODIMENT TAG:
+
+  * the RAW observation fed to the original processor (per-camera uint8 frames,
+    per-key state vectors, the language instruction), so the LeRobot side can also
+    run its OWN preprocessor on identical raw inputs and compare collated tensors,
+  * the exact pre-processed/collated model inputs (so the LeRobot side consumes the
+    byte-identical tensors -- same image preprocessing, tokenization, normalization),
+  * the random seed used right before the flow-matching sampler,
+  * the raw ``action_pred`` tensor returned by ``model.get_action`` (normalized space,
+    before any per-implementation action decoding).
+
+Inputs are built GENERICALLY from the checkpoint metadata (no per-tag hardcoding):
+state keys + dims come from ``statistics.json``; video + language keys come from the
+processor's per-embodiment modality configs. This lets us test many embodiment tags
+from the SAME checkpoint and confirm the LeRobot integration is not overfit to
+``libero_sim``.
+
+The companion pytest (run in the LeRobot env) loads each .npz and asserts parity
+twice: the collated inputs + seed are replayed through the LeRobot GR00T N1.7 model
+(model parity), and the raw observation is replayed through LeRobot's own
+preprocessor pipeline and compared against the collated inputs (preprocessor parity).
+
+Usage:
+    .venv-original/bin/python tests/policies/groot/utils/dump_original_n1_7.py \
+        --ckpt <path-to-GR00T-N1.7-LIBERO/libero_10> \
+        --out-dir tests/policies/groot/artifacts \
+        [--tags libero_sim,oxe_droid_relative_eef_relative_joint,...] \
+        [--device cuda] [--seed 42]
+
+If --tags is omitted, every embodiment present in the checkpoint statistics is dumped.
+"""
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import numpy as np
+import torch
+
+IMAGE_SIZE = 256
+BATCH_SIZE = 2
+PROMPT = "pick up the black bowl and place it on the plate"
+
+
+def load_statistics(ckpt: str) -> dict:
+    with open(os.path.join(ckpt, "statistics.json")) as f:
+        return json.load(f)
+
+
+def make_observation(seed: int, video_keys, lang_key, state_spec):
+    """Build a dummy observation dict generically from the embodiment metadata."""
+    rng = np.random.default_rng(seed)
+    video = {
+        k: rng.integers(0, 256, (BATCH_SIZE, 1, IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.uint8)
+        for k in video_keys
+    }
+    # One ndarray per state key, shape (B, T=1, key_dim); dim taken from statistics.
+    # Keys with dim 0 (e.g. disabled eef on some embodiments) are still emitted as
+    # present-but-empty so the processor's state transform finds every expected key.
+    state = {k: rng.standard_normal((BATCH_SIZE, 1, dim)).astype(np.float32) for k, dim in state_spec}
+    language = {lang_key: [[PROMPT] for _ in range(BATCH_SIZE)]}
+    return {"video": video, "state": state, "language": language}
+
+
+def dump_one_tag(policy, fair_model, tag, modality_cfg, state_spec, args, out_path):
+    from gr00t.data.types import MessageType
+
+    video_keys = modality_cfg["video"].modality_keys
+    lang_key = modality_cfg["language"].modality_keys[0]
+    observation = make_observation(args.seed, video_keys, lang_key, state_spec)
+
+    # Snapshot the RAW observation exactly as fed to the original processor below. The
+    # consumer's preprocessor-parity case replays it through LeRobot's own preprocessor
+    # and compares the resulting collated tensors against the "in::" ones saved further
+    # down. raw_state_keys records the checkpoint modality-key order, which is the
+    # concatenation order of the flat LeRobot ``observation.state`` vector.
+    spec_keys = [key for key, _ in state_spec]
+    state_modality = modality_cfg.get("state")
+    state_keys = [key for key in state_modality.modality_keys if key in spec_keys] if state_modality else []
+    state_keys += [key for key in spec_keys if key not in state_keys]
+    raw_language = [
+        str(item[0]) if isinstance(item, (list, tuple)) else str(item)
+        for item in observation["language"][lang_key]
+    ]
+    raw_flat = {f"raw::video.{key}": arr.copy() for key, arr in observation["video"].items()}
+    raw_flat.update({f"raw::state.{key}": arr.copy() for key, arr in observation["state"].items()})
+    raw_flat["raw::language"] = np.array(raw_language, dtype=object)
+    raw_flat["raw_video_keys"] = np.array([str(key) for key in video_keys], dtype=object)
+    raw_flat["raw_state_keys"] = np.array([str(key) for key in state_keys], dtype=object)
+
+    # Point the policy preprocessing at this embodiment (mirrors Gr00tPolicy.__init__).
+    policy.embodiment_tag = type(policy.embodiment_tag)(tag)
+    policy.modality_configs = {
+        k: v for k, v in policy.processor.get_modality_configs()[tag].items() if k != "rl_info"
+    }
+    policy.language_key = policy.modality_configs["language"].modality_keys[0]
+
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+
+    unbatched = policy._unbatch_observation(observation)
+    processed = []
+    for obs in unbatched:
+        vla = policy._to_vla_step_data(obs)
+        processed.append(policy.processor([{"type": MessageType.EPISODE_STEP.value, "content": vla}]))
+    collated = policy.collate_fn(processed)
+
+    def to_dev(x):
+        if isinstance(x, torch.Tensor) and torch.is_floating_point(x):
+            return x.to(args.device, torch.float32)
+        if isinstance(x, torch.Tensor):
+            return x.to(args.device)
+        if isinstance(x, dict):
+            return {k: to_dev(v) for k, v in x.items()}
+        return x
+
+    collated = {k: to_dev(v) for k, v in collated.items()}
+
+    torch.manual_seed(args.seed)
+    with torch.inference_mode():
+        out = fair_model.get_action(**collated)
+    action_pred = out["action_pred"].float().cpu().numpy()
+
+    flat, meta = {}, {}
+
+    def flatten(prefix, obj):
+        if isinstance(obj, torch.Tensor):
+            arr = obj.float().cpu().numpy() if torch.is_floating_point(obj) else obj.cpu().numpy()
+            flat[f"in::{prefix}"] = arr
+            meta[f"in::{prefix}"] = str(obj.dtype)
+        elif isinstance(obj, dict):
+            for k, v in obj.items():
+                flatten(f"{prefix}.{k}" if prefix else k, v)
+        elif isinstance(obj, (list, tuple)):
+            flat[f"in::{prefix}"] = np.array(obj, dtype=object)
+        else:
+            flat[f"in::{prefix}"] = np.array(obj)
+
+    flatten("", collated)
+
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    np.savez(
+        out_path,
+        action_pred=action_pred,
+        seed=np.array(args.seed),
+        device=np.array(args.device),
+        embodiment_tag=np.array(tag),
+        meta_keys=np.array(list(meta.keys()), dtype=object),
+        meta_dtypes=np.array(list(meta.values()), dtype=object),
+        **raw_flat,
+        **flat,
+    )
+    print(f"[{tag}] action_pred {action_pred.shape} -> {out_path.name} ({os.path.getsize(out_path)} B)")
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--ckpt", required=True)
+    ap.add_argument("--out-dir", required=True, help="directory for per-tag .npz files")
+    ap.add_argument("--tags", default="", help="comma-separated embodiment tags (default: all in stats)")
+    ap.add_argument("--device", default="cuda")
+    ap.add_argument("--seed", type=int, default=42)
+    args = ap.parse_args()
+
+    from gr00t.policy.gr00t_policy import Gr00tPolicy
+    from transformers import AutoConfig, AutoModel
+
+    stats = load_statistics(args.ckpt)
+    requested = [t.strip() for t in args.tags.split(",") if t.strip()] or list(stats.keys())
+
+    # Load the policy once (for its processor/preprocessing) on any valid tag.
+    bootstrap_tag = "libero_sim" if "libero_sim" in stats else requested[0]
+    policy = Gr00tPolicy(embodiment_tag=bootstrap_tag, model_path=args.ckpt, device=args.device)
+    all_modality = policy.processor.get_modality_configs()
+
+    # Load a FAIR model (SDPA + fp32) once and reuse across tags. Otherwise the
+    # original checkpoint default (flash_attention_2 + bf16) introduces kernel/rounding
+    # noise vs the LeRobot env (which has no flash_attn and runs SDPA).
+    cfg = AutoConfig.from_pretrained(args.ckpt, trust_remote_code=True)
+    cfg.use_flash_attention = False
+    cfg.load_bf16 = False
+    fair_model = AutoModel.from_pretrained(args.ckpt, config=cfg, trust_remote_code=True)
+    fair_model.to(device=args.device, dtype=torch.float32)
+    fair_model.eval()
+
+    out_dir = Path(args.out_dir)
+    done, skipped = [], []
+    for tag in requested:
+        if tag not in stats or tag not in all_modality:
+            print(f"[skip] {tag}: not present in checkpoint statistics/modality configs")
+            skipped.append(tag)
+            continue
+        state_spec = [(k, len(v["min"])) for k, v in stats[tag]["state"].items()]
+        try:
+            dump_one_tag(
+                policy,
+                fair_model,
+                tag,
+                all_modality[tag],
+                state_spec,
+                args,
+                out_dir / f"original_n1_7_{tag}.npz",
+            )
+            done.append(tag)
+        except Exception as exc:  # noqa: BLE001
+            print(f"[fail] {tag}: {type(exc).__name__}: {exc}")
+            skipped.append(tag)
+
+    print(f"\nDumped {len(done)} tags: {done}")
+    if skipped:
+        print(f"Skipped/failed {len(skipped)} tags: {skipped}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1 @@
+"""Lightweight vendored OpenPI PyTorch modules for PI0/PI05 parity tests."""
@@ -0,0 +1,22 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class Config:
+    width: int
+    depth: int
+    mlp_dim: int
+    num_heads: int
+    num_kv_heads: int
+    head_dim: int
+
+
+def get_config(variant: str) -> Config:
+    """Return the Gemma shape config needed by the OpenPI PyTorch model."""
+    if variant == "dummy":
+        return Config(width=64, depth=4, mlp_dim=128, num_heads=8, num_kv_heads=1, head_dim=16)
+    if variant == "gemma_300m":
+        return Config(width=1024, depth=18, mlp_dim=4096, num_heads=8, num_kv_heads=1, head_dim=256)
+    if variant == "gemma_2b":
+        return Config(width=2048, depth=18, mlp_dim=16_384, num_heads=8, num_kv_heads=1, head_dim=256)
+    raise ValueError(f"Unknown variant: {variant}")
@@ -0,0 +1,300 @@
+from typing import Literal
+
+import torch
+from torch import nn
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.models.gemma import modeling_gemma
+
+from lerobot.policies.pi_gemma import (
+    PaliGemmaForConditionalGenerationWithPiGemma,
+    PiGemmaForCausalLM,
+    _gated_residual,
+    layernorm_forward,
+)
+
+
+class PaliGemmaWithExpertModel(nn.Module):
+    def __init__(
+        self,
+        vlm_config,
+        action_expert_config,
+        use_adarms=None,
+        precision: Literal["bfloat16", "float32"] = "bfloat16",
+    ):
+        if use_adarms is None:
+            use_adarms = [False, False]
+        super().__init__()
+
+        vlm_config_hf = CONFIG_MAPPING["paligemma"]()
+        vlm_config_hf._vocab_size = 257152  # noqa: SLF001
+        vlm_config_hf.image_token_index = 257152
+        vlm_config_hf.text_config.hidden_size = vlm_config.width
+        vlm_config_hf.text_config.intermediate_size = vlm_config.mlp_dim
+        vlm_config_hf.text_config.num_attention_heads = vlm_config.num_heads
+        vlm_config_hf.text_config.head_dim = vlm_config.head_dim
+        vlm_config_hf.text_config.num_hidden_layers = vlm_config.depth
+        vlm_config_hf.text_config.num_key_value_heads = vlm_config.num_kv_heads
+        vlm_config_hf.text_config.hidden_activation = "gelu_pytorch_tanh"
+        vlm_config_hf.text_config.dtype = "float32"
+        vlm_config_hf.text_config.vocab_size = 257152
+        vlm_config_hf.text_config.use_adarms = use_adarms[0]
+        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
+        vlm_config_hf.vision_config.intermediate_size = 4304
+        vlm_config_hf.vision_config.projection_dim = 2048
+        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
+        vlm_config_hf.vision_config.dtype = "float32"
+
+        action_expert_config_hf = CONFIG_MAPPING["gemma"](
+            head_dim=action_expert_config.head_dim,
+            hidden_size=action_expert_config.width,
+            intermediate_size=action_expert_config.mlp_dim,
+            num_attention_heads=action_expert_config.num_heads,
+            num_hidden_layers=action_expert_config.depth,
+            num_key_value_heads=action_expert_config.num_kv_heads,
+            vocab_size=257152,
+            hidden_activation="gelu_pytorch_tanh",
+            dtype="float32",
+            use_adarms=use_adarms[1],
+            adarms_cond_dim=action_expert_config.width if use_adarms[1] else None,
+        )
+
+        self.paligemma = PaliGemmaForConditionalGenerationWithPiGemma(config=vlm_config_hf)
+        self.gemma_expert = PiGemmaForCausalLM(config=action_expert_config_hf)
+        self.gemma_expert.model.embed_tokens = None
+
+        self.to_bfloat16_for_selected_params(precision)
+
+    def to_bfloat16_for_selected_params(self, precision: Literal["bfloat16", "float32"] = "bfloat16"):
+        if precision == "bfloat16":
+            self.to(dtype=torch.bfloat16)
+        elif precision == "float32":
+            self.to(dtype=torch.float32)
+            return
+        else:
+            raise ValueError(f"Invalid precision: {precision}")
+
+        params_to_keep_float32 = [
+            "vision_tower",
+            "multi_modal_projector",
+            "input_layernorm",
+            "post_attention_layernorm",
+            "model.norm",
+        ]
+
+        for name, param in self.named_parameters():
+            if any(selector in name for selector in params_to_keep_float32):
+                param.data = param.data.to(dtype=torch.float32)
+
+    def embed_image(self, image: torch.Tensor):
+        # Transformers 5.4 no longer divides PaliGemma image features by sqrt(hidden_size),
+        # so the upstream helper now matches OpenPI's patched PaliGemma image-scale semantics.
+        # See https://github.com/huggingface/transformers/pull/44432/changes#diff-c916907e7e52ac85ee1a1527560eae4656cd6c76141ceb1fe3da61bd5f697d2a
+        out_dtype = image.dtype
+        if image.dtype != torch.float32:
+            image = image.to(torch.float32)
+        image_outputs = self.paligemma.model.get_image_features(image)
+        features = image_outputs.pooler_output
+        if features.dtype != out_dtype:
+            features = features.to(out_dtype)
+        return features
+
+    def embed_language_tokens(self, tokens: torch.Tensor):
+        return self.paligemma.model.language_model.get_input_embeddings()(tokens)
+
+    def forward(
+        self,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: list[torch.FloatTensor] | None = None,
+        use_cache: bool | None = None,
+        adarms_cond: list[torch.Tensor] | None = None,
+    ):
+        if adarms_cond is None:
+            adarms_cond = [None, None]
+        if inputs_embeds[1] is None:
+            prefix_output = self.paligemma.model.language_model.forward(
+                inputs_embeds=inputs_embeds[0],
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                adarms_cond=adarms_cond[0] if adarms_cond is not None else None,
+            )
+            prefix_past_key_values = prefix_output.past_key_values
+            prefix_output = prefix_output.last_hidden_state
+            suffix_output = None
+        elif inputs_embeds[0] is None:
+            suffix_output = self.gemma_expert.model.forward(
+                inputs_embeds=inputs_embeds[1],
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                adarms_cond=adarms_cond[1] if adarms_cond is not None else None,
+            )
+            suffix_output = suffix_output.last_hidden_state
+            prefix_output = None
+            prefix_past_key_values = None
+        else:
+            models = [self.paligemma.model.language_model, self.gemma_expert.model]
+            num_layers = self.paligemma.config.text_config.num_hidden_layers
+
+            # Check if gradient checkpointing is enabled for any of the models
+            use_gradient_checkpointing = (
+                hasattr(self.gemma_expert.model, "gradient_checkpointing")
+                and self.gemma_expert.model.gradient_checkpointing
+                and self.training
+            ) or (hasattr(self, "gradient_checkpointing") and self.gradient_checkpointing and self.training)
+
+            # Force enable gradient checkpointing if we're in training mode and the model supports it
+            if self.training and hasattr(self.gemma_expert.model, "gradient_checkpointing"):
+                if not self.gemma_expert.model.gradient_checkpointing:
+                    print("Forcing gradient checkpointing to be enabled for Gemma expert model")
+                    self.gemma_expert.model.gradient_checkpointing = True
+                use_gradient_checkpointing = True
+
+            # Debug gradient checkpointing status
+            if hasattr(self, "_debug_gc_printed") and not self._debug_gc_printed:
+                print(f"Gemma expert model gradient checkpointing: {use_gradient_checkpointing}")
+                print(f"Model training mode: {self.training}")
+                print(
+                    f"Gemma expert model has gradient_checkpointing attr: {hasattr(self.gemma_expert.model, 'gradient_checkpointing')}"
+                )
+                if hasattr(self.gemma_expert.model, "gradient_checkpointing"):
+                    print(
+                        f"Gemma expert model gradient_checkpointing value: {self.gemma_expert.model.gradient_checkpointing}"
+                    )
+                self._debug_gc_printed = True
+
+            # Define the complete layer computation function for gradient checkpointing
+            def compute_layer_complete(layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond):
+                models = [self.paligemma.model.language_model, self.gemma_expert.model]
+
+                query_states = []
+                key_states = []
+                value_states = []
+                gates = []
+                for i, hidden_states in enumerate(inputs_embeds):
+                    layer = models[i].layers[layer_idx]
+                    hidden_states, gate = layernorm_forward(
+                        layer.input_layernorm, hidden_states, adarms_cond[i]
+                    )
+                    gates.append(gate)
+
+                    input_shape = hidden_states.shape[:-1]
+                    hidden_shape = (*input_shape, -1, layer.self_attn.head_dim)
+                    query_state = layer.self_attn.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+                    key_state = layer.self_attn.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+                    value_state = layer.self_attn.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+                    query_states.append(query_state)
+                    key_states.append(key_state)
+                    value_states.append(value_state)
+
+                # Concatenate and process attention
+                query_states = torch.cat(query_states, dim=2)
+                key_states = torch.cat(key_states, dim=2)
+                value_states = torch.cat(value_states, dim=2)
+
+                dummy_tensor = torch.zeros(
+                    query_states.shape[0],
+                    query_states.shape[2],
+                    query_states.shape[-1],
+                    device=query_states.device,
+                    dtype=query_states.dtype,
+                )
+                cos, sin = self.paligemma.model.language_model.rotary_emb(dummy_tensor, position_ids)
+                query_states, key_states = modeling_gemma.apply_rotary_pos_emb(
+                    query_states, key_states, cos, sin, unsqueeze_dim=1
+                )
+
+                batch_size = query_states.shape[0]
+                scaling = self.paligemma.model.language_model.layers[layer_idx].self_attn.scaling
+
+                # Attention computation
+                att_output, _ = modeling_gemma.eager_attention_forward(
+                    self.paligemma.model.language_model.layers[layer_idx].self_attn,
+                    query_states,
+                    key_states,
+                    value_states,
+                    attention_mask,
+                    scaling,
+                )
+                # Get head_dim from the current layer, not from the model
+                head_dim = self.paligemma.model.language_model.layers[layer_idx].self_attn.head_dim
+                att_output = att_output.reshape(batch_size, -1, 1 * 8 * head_dim)
+
+                # Process layer outputs
+                outputs_embeds = []
+                start_pos = 0
+                for i, hidden_states in enumerate(inputs_embeds):
+                    layer = models[i].layers[layer_idx]
+                    end_pos = start_pos + hidden_states.shape[1]
+
+                    if att_output.dtype != layer.self_attn.o_proj.weight.dtype:
+                        att_output = att_output.to(layer.self_attn.o_proj.weight.dtype)
+                    out_emb = layer.self_attn.o_proj(att_output[:, start_pos:end_pos])
+
+                    # first residual
+                    out_emb = _gated_residual(hidden_states, out_emb, gates[i])
+                    after_first_residual = out_emb.clone()
+                    out_emb, gate = layernorm_forward(layer.post_attention_layernorm, out_emb, adarms_cond[i])
+                    # Convert to bfloat16 if the next layer (mlp) uses bfloat16
+                    if layer.mlp.up_proj.weight.dtype == torch.bfloat16:
+                        out_emb = out_emb.to(dtype=torch.bfloat16)
+
+                    out_emb = layer.mlp(out_emb)
+                    # second residual
+                    out_emb = _gated_residual(after_first_residual, out_emb, gate)
+                    outputs_embeds.append(out_emb)
+                    start_pos = end_pos
+
+                return outputs_embeds
+
+            # Process all layers with gradient checkpointing if enabled
+            for layer_idx in range(num_layers):
+                if use_gradient_checkpointing:
+                    inputs_embeds = torch.utils.checkpoint.checkpoint(
+                        compute_layer_complete,
+                        layer_idx,
+                        inputs_embeds,
+                        attention_mask,
+                        position_ids,
+                        adarms_cond,
+                        use_reentrant=False,
+                        preserve_rng_state=False,
+                    )
+                else:
+                    inputs_embeds = compute_layer_complete(
+                        layer_idx, inputs_embeds, attention_mask, position_ids, adarms_cond
+                    )
+
+                # Old code removed - now using compute_layer_complete function above
+
+            # final norm
+            # Define final norm computation function for gradient checkpointing
+            def compute_final_norms(inputs_embeds, adarms_cond):
+                outputs_embeds = []
+                for i, hidden_states in enumerate(inputs_embeds):
+                    out_emb, _ = layernorm_forward(models[i].norm, hidden_states, adarms_cond[i])
+                    outputs_embeds.append(out_emb)
+                return outputs_embeds
+
+            # Apply gradient checkpointing to final norm if enabled
+            if use_gradient_checkpointing:
+                outputs_embeds = torch.utils.checkpoint.checkpoint(
+                    compute_final_norms,
+                    inputs_embeds,
+                    adarms_cond,
+                    use_reentrant=False,
+                    preserve_rng_state=False,
+                )
+            else:
+                outputs_embeds = compute_final_norms(inputs_embeds, adarms_cond)
+
+            prefix_output = outputs_embeds[0]
+            suffix_output = outputs_embeds[1]
+            prefix_past_key_values = None
+
+        return [prefix_output, suffix_output], prefix_past_key_values
@@ -0,0 +1,79 @@
+import torch
+import torch.nn.functional as F  # noqa: N812
+
+
+def resize_with_pad_torch(
+    images: torch.Tensor,
+    height: int,
+    width: int,
+    mode: str = "bilinear",
+) -> torch.Tensor:
+    """PyTorch version of resize_with_pad. Resizes an image to a target height and width without distortion
+    by padding with black. If the image is float32, it must be in the range [-1, 1].
+
+    Args:
+        images: Tensor of shape [*b, h, w, c] or [*b, c, h, w]
+        height: Target height
+        width: Target width
+        mode: Interpolation mode ('bilinear', 'nearest', etc.)
+
+    Returns:
+        Resized and padded tensor with same shape format as input
+    """
+    # Check if input is in channels-last format [*b, h, w, c] or channels-first [*b, c, h, w]
+    if images.shape[-1] <= 4:  # Assume channels-last format
+        channels_last = True
+        # Convert to channels-first for torch operations
+        if images.dim() == 3:
+            images = images.unsqueeze(0)  # Add batch dimension
+        images = images.permute(0, 3, 1, 2)  # [b, h, w, c] -> [b, c, h, w]
+    else:
+        channels_last = False
+        if images.dim() == 3:
+            images = images.unsqueeze(0)  # Add batch dimension
+
+    batch_size, channels, cur_height, cur_width = images.shape
+
+    # Calculate resize ratio
+    ratio = max(cur_width / width, cur_height / height)
+    resized_height = int(cur_height / ratio)
+    resized_width = int(cur_width / ratio)
+
+    # Resize
+    resized_images = F.interpolate(
+        images,
+        size=(resized_height, resized_width),
+        mode=mode,
+        align_corners=False if mode == "bilinear" else None,
+    )
+
+    # Handle dtype-specific clipping
+    if images.dtype == torch.uint8:
+        resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)
+    elif images.dtype == torch.float32:
+        resized_images = resized_images.clamp(-1.0, 1.0)
+    else:
+        raise ValueError(f"Unsupported image dtype: {images.dtype}")
+
+    # Calculate padding
+    pad_h0, remainder_h = divmod(height - resized_height, 2)
+    pad_h1 = pad_h0 + remainder_h
+    pad_w0, remainder_w = divmod(width - resized_width, 2)
+    pad_w1 = pad_w0 + remainder_w
+
+    # Pad
+    constant_value = 0 if images.dtype == torch.uint8 else -1.0
+    padded_images = F.pad(
+        resized_images,
+        (pad_w0, pad_w1, pad_h0, pad_h1),  # left, right, top, bottom
+        mode="constant",
+        value=constant_value,
+    )
+
+    # Convert back to original format if needed
+    if channels_last:
+        padded_images = padded_images.permute(0, 2, 3, 1)  # [b, c, h, w] -> [b, h, w, c]
+        if batch_size == 1 and images.shape[0] == 1:
+            padded_images = padded_images.squeeze(0)  # Remove batch dimension if it was added
+
+    return padded_images
@@ -0,0 +1,471 @@
+import copy
+import logging
+import math
+
+import torch
+import torch.nn.functional as F  # noqa: N812
+from torch import Tensor, nn
+
+import tests.policies.pi0_pi05.openpi_pytorch.gemma as _gemma
+from tests.policies.pi0_pi05.openpi_pytorch import preprocessing_pytorch as _preprocessing
+from tests.policies.pi0_pi05.openpi_pytorch.gemma_pytorch import PaliGemmaWithExpertModel
+
+
+def get_safe_dtype(target_dtype, device_type):
+    """Get a safe dtype for the given device type."""
+    if device_type == "cpu":
+        # CPU doesn't support bfloat16, use float32 instead
+        if target_dtype == torch.bfloat16:
+            return torch.float32
+        if target_dtype == torch.float64:
+            return torch.float64
+    return target_dtype
+
+
+def create_sinusoidal_pos_embedding(
+    time: torch.tensor, dimension: int, min_period: float, max_period: float, device="cpu"
+) -> Tensor:
+    """Computes sine-cosine positional embedding vectors for scalar positions."""
+    if dimension % 2 != 0:
+        raise ValueError(f"dimension ({dimension}) must be divisible by 2")
+
+    if time.ndim != 1:
+        raise ValueError("The time tensor is expected to be of shape `(batch_size, )`.")
+
+    dtype = get_safe_dtype(torch.float64, device.type)
+    fraction = torch.linspace(0.0, 1.0, dimension // 2, dtype=dtype, device=device)
+    period = min_period * (max_period / min_period) ** fraction
+
+    # Compute the outer product
+    scaling_factor = 1.0 / period * 2 * math.pi
+    sin_input = scaling_factor[None, :] * time[:, None]
+    return torch.cat([torch.sin(sin_input), torch.cos(sin_input)], dim=1)
+
+
+def sample_beta(alpha, beta, bsize, device):
+    alpha_t = torch.as_tensor(alpha, dtype=torch.float32, device=device)
+    beta_t = torch.as_tensor(beta, dtype=torch.float32, device=device)
+    dist = torch.distributions.Beta(alpha_t, beta_t)
+    return dist.sample((bsize,))
+
+
+def make_att_2d_masks(pad_masks, att_masks):
+    """Copied from big_vision.
+
+    Tokens can attend to valid inputs tokens which have a cumulative mask_ar
+    smaller or equal to theirs. This way `mask_ar` int[B, N] can be used to
+    setup several types of attention, for example:
+
+      [[1 1 1 1 1 1]]: pure causal attention.
+
+      [[0 0 0 1 1 1]]: prefix-lm attention. The first 3 tokens can attend between
+          themselves and the last 3 tokens have a causal attention. The first
+          entry could also be a 1 without changing behaviour.
+
+      [[1 0 1 0 1 0 0 1 0 0]]: causal attention between 4 blocks. Tokens of a
+          block can attend all previous blocks and all tokens on the same block.
+
+    Args:
+      input_mask: bool[B, N] true if its part of the input, false if padding.
+      mask_ar: int32[B, N] mask that's 1 where previous tokens cannot depend on
+        it and 0 where it shares the same attention mask as the previous token.
+    """
+    if att_masks.ndim != 2:
+        raise ValueError(att_masks.ndim)
+    if pad_masks.ndim != 2:
+        raise ValueError(pad_masks.ndim)
+
+    cumsum = torch.cumsum(att_masks, dim=1)
+    att_2d_masks = cumsum[:, None, :] <= cumsum[:, :, None]
+    pad_2d_masks = pad_masks[:, None, :] * pad_masks[:, :, None]
+    return att_2d_masks & pad_2d_masks
+
+
+class PI0Pytorch(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.pi05 = config.pi05
+
+        paligemma_config = _gemma.get_config(config.paligemma_variant)
+        action_expert_config = _gemma.get_config(config.action_expert_variant)
+
+        self.paligemma_with_expert = PaliGemmaWithExpertModel(
+            paligemma_config,
+            action_expert_config,
+            use_adarms=[False, True] if self.pi05 else [False, False],
+            precision=config.dtype,
+        )
+
+        self.action_in_proj = nn.Linear(config.action_dim, action_expert_config.width)
+        self.action_out_proj = nn.Linear(action_expert_config.width, config.action_dim)
+
+        if self.pi05:
+            self.time_mlp_in = nn.Linear(action_expert_config.width, action_expert_config.width)
+            self.time_mlp_out = nn.Linear(action_expert_config.width, action_expert_config.width)
+        else:
+            self.state_proj = nn.Linear(config.action_dim, action_expert_config.width)
+            self.action_time_mlp_in = nn.Linear(2 * action_expert_config.width, action_expert_config.width)
+            self.action_time_mlp_out = nn.Linear(action_expert_config.width, action_expert_config.width)
+
+        torch.set_float32_matmul_precision("high")
+        if config.pytorch_compile_mode is not None:
+            self.sample_actions = torch.compile(self.sample_actions, mode=config.pytorch_compile_mode)
+
+        # Initialize gradient checkpointing flag
+        self.gradient_checkpointing_enabled = False
+
+        # The upstream OpenPI module verifies a site-package Transformers patch here.
+        # This vendored test copy instead routes through LeRobot's local PiGemma compatibility layer.
+
+    def gradient_checkpointing_enable(self):
+        """Enable gradient checkpointing for memory optimization."""
+        self.gradient_checkpointing_enabled = True
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = True
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = True
+        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = True
+
+        logging.info("Enabled gradient checkpointing for PI0Pytorch model")
+
+    def gradient_checkpointing_disable(self):
+        """Disable gradient checkpointing."""
+        self.gradient_checkpointing_enabled = False
+        self.paligemma_with_expert.paligemma.model.language_model.gradient_checkpointing = False
+        self.paligemma_with_expert.paligemma.model.vision_tower.gradient_checkpointing = False
+        self.paligemma_with_expert.gemma_expert.model.gradient_checkpointing = False
+
+        logging.info("Disabled gradient checkpointing for PI0Pytorch model")
+
+    def is_gradient_checkpointing_enabled(self):
+        """Check if gradient checkpointing is enabled."""
+        return self.gradient_checkpointing_enabled
+
+    def _apply_checkpoint(self, func, *args, **kwargs):
+        """Helper method to apply gradient checkpointing if enabled."""
+        if self.gradient_checkpointing_enabled and self.training:
+            return torch.utils.checkpoint.checkpoint(
+                func, *args, use_reentrant=False, preserve_rng_state=False, **kwargs
+            )
+        return func(*args, **kwargs)
+
+    def _prepare_attention_masks_4d(self, att_2d_masks):
+        """Helper method to prepare 4D attention masks for transformer."""
+        att_2d_masks_4d = att_2d_masks[:, None, :, :]
+        return torch.where(att_2d_masks_4d, 0.0, -2.3819763e38)
+
+    def _preprocess_observation(self, observation, *, train=True):
+        """Helper method to preprocess observation."""
+        observation = _preprocessing.preprocess_observation_pytorch(observation, train=train)
+        return (
+            list(observation.images.values()),
+            list(observation.image_masks.values()),
+            observation.tokenized_prompt,
+            observation.tokenized_prompt_mask,
+            observation.state,
+        )
+
+    def sample_noise(self, shape, device):
+        return torch.normal(
+            mean=0.0,
+            std=1.0,
+            size=shape,
+            dtype=torch.float32,
+            device=device,
+        )
+
+    def sample_time(self, bsize, device):
+        time_beta = sample_beta(1.5, 1.0, bsize, device)
+        time = time_beta * 0.999 + 0.001
+        return time.to(dtype=torch.float32, device=device)
+
+    def embed_prefix(
+        self, images, img_masks, lang_tokens, lang_masks
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Embed images with SigLIP and language tokens with embedding layer to prepare
+        for PaliGemma transformer processing.
+        """
+        embs = []
+        pad_masks = []
+        att_masks = []
+
+        # Process images
+        for img, img_mask in zip(images, img_masks, strict=True):
+
+            def image_embed_func(img):
+                return self.paligemma_with_expert.embed_image(img)
+
+            img_emb = self._apply_checkpoint(image_embed_func, img)
+
+            bsize, num_img_embs = img_emb.shape[:2]
+
+            embs.append(img_emb)
+            pad_masks.append(img_mask[:, None].expand(bsize, num_img_embs))
+
+            # Create attention masks so that image tokens attend to each other
+            att_masks += [0] * num_img_embs
+
+        # Process language tokens
+        def lang_embed_func(lang_tokens):
+            lang_emb = self.paligemma_with_expert.embed_language_tokens(lang_tokens)
+            # Transformers > 5.4 scales Gemma token embeddings inside embed_tokens, matching
+            # OpenPI's former explicit sqrt(hidden_size) multiply without applying it twice.
+            # See https://github.com/huggingface/transformers/pull/44432/changes#diff-5f76eac6f18f4b491521314c318a9692318feb4d19228e9576cce7bde4240834
+            return lang_emb
+
+        lang_emb = self._apply_checkpoint(lang_embed_func, lang_tokens)
+
+        embs.append(lang_emb)
+        pad_masks.append(lang_masks)
+
+        # full attention between image and language inputs
+        num_lang_embs = lang_emb.shape[1]
+        att_masks += [0] * num_lang_embs
+
+        embs = torch.cat(embs, dim=1)
+        pad_masks = torch.cat(pad_masks, dim=1)
+        att_masks = torch.tensor(att_masks, dtype=torch.bool, device=pad_masks.device)
+
+        # Get batch size from the first dimension of the concatenated tensors
+        bsize = pad_masks.shape[0]
+        att_masks = att_masks[None, :].expand(bsize, len(att_masks))
+
+        return embs, pad_masks, att_masks
+
+    def embed_suffix(self, state, noisy_actions, timestep):
+        """Embed state, noisy_actions, timestep to prepare for Expert Gemma processing."""
+        embs = []
+        pad_masks = []
+        att_masks = []
+
+        if not self.pi05:
+            if self.state_proj.weight.dtype == torch.float32:
+                state = state.to(torch.float32)
+
+            # Embed state
+            def state_proj_func(state):
+                return self.state_proj(state)
+
+            state_emb = self._apply_checkpoint(state_proj_func, state)
+
+            embs.append(state_emb[:, None, :])
+            bsize = state_emb.shape[0]
+            device = state_emb.device
+
+            state_mask = torch.ones(bsize, 1, dtype=torch.bool, device=device)
+            pad_masks.append(state_mask)
+
+            # Set attention masks so that image and language inputs do not attend to state or actions
+            att_masks += [1]
+
+        # Embed timestep using sine-cosine positional encoding with sensitivity in the range [0, 1]
+        time_emb = create_sinusoidal_pos_embedding(
+            timestep,
+            self.action_in_proj.out_features,
+            min_period=4e-3,
+            max_period=4.0,
+            device=timestep.device,
+        )
+        time_emb = time_emb.type(dtype=timestep.dtype)
+
+        # Fuse timestep + action information using an MLP
+        def action_proj_func(noisy_actions):
+            return self.action_in_proj(noisy_actions)
+
+        action_emb = self._apply_checkpoint(action_proj_func, noisy_actions)
+
+        if not self.pi05:
+            time_emb = time_emb[:, None, :].expand_as(action_emb)
+            action_time_emb = torch.cat([action_emb, time_emb], dim=2)
+
+            # Apply MLP layers
+            def mlp_func(action_time_emb):
+                x = self.action_time_mlp_in(action_time_emb)
+                x = F.silu(x)  # swish == silu
+                return self.action_time_mlp_out(x)
+
+            action_time_emb = self._apply_checkpoint(mlp_func, action_time_emb)
+            adarms_cond = None
+        else:
+            # time MLP (for adaRMS)
+            def time_mlp_func(time_emb):
+                x = self.time_mlp_in(time_emb)
+                x = F.silu(x)  # swish == silu
+                x = self.time_mlp_out(x)
+                return F.silu(x)
+
+            time_emb = self._apply_checkpoint(time_mlp_func, time_emb)
+            action_time_emb = action_emb
+            adarms_cond = time_emb
+
+        # Add to input tokens
+        embs.append(action_time_emb)
+
+        bsize, action_time_dim = action_time_emb.shape[:2]
+        action_time_mask = torch.ones(bsize, action_time_dim, dtype=torch.bool, device=timestep.device)
+        pad_masks.append(action_time_mask)
+
+        # Set attention masks so that image, language and state inputs do not attend to action tokens
+        att_masks += [1] + ([0] * (self.config.action_horizon - 1))
+
+        embs = torch.cat(embs, dim=1)
+        pad_masks = torch.cat(pad_masks, dim=1)
+        att_masks = torch.tensor(att_masks, dtype=embs.dtype, device=embs.device)
+        att_masks = att_masks[None, :].expand(bsize, len(att_masks))
+
+        return embs, pad_masks, att_masks, adarms_cond
+
+    def forward(self, observation, actions, noise=None, time=None) -> Tensor:
+        """Do a full training forward pass and compute the loss (batch_size x num_steps x num_motors)"""
+        images, img_masks, lang_tokens, lang_masks, state = self._preprocess_observation(
+            observation, train=True
+        )
+
+        if noise is None:
+            noise = self.sample_noise(actions.shape, actions.device)
+
+        if time is None:
+            time = self.sample_time(actions.shape[0], actions.device)
+
+        time_expanded = time[:, None, None]
+        x_t = time_expanded * noise + (1 - time_expanded) * actions
+        u_t = noise - actions
+
+        prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
+            images, img_masks, lang_tokens, lang_masks
+        )
+        suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, time)
+        if (
+            self.paligemma_with_expert.paligemma.model.language_model.layers[0].self_attn.q_proj.weight.dtype
+            == torch.bfloat16
+        ):
+            suffix_embs = suffix_embs.to(dtype=torch.bfloat16)
+            prefix_embs = prefix_embs.to(dtype=torch.bfloat16)
+
+        pad_masks = torch.cat([prefix_pad_masks, suffix_pad_masks], dim=1)
+        att_masks = torch.cat([prefix_att_masks, suffix_att_masks], dim=1)
+
+        att_2d_masks = make_att_2d_masks(pad_masks, att_masks)
+        position_ids = torch.cumsum(pad_masks, dim=1) - 1
+
+        # Prepare attention masks
+        att_2d_masks_4d = self._prepare_attention_masks_4d(att_2d_masks)
+
+        # Apply gradient checkpointing if enabled
+        def forward_func(prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond):
+            (_, suffix_out), _ = self.paligemma_with_expert.forward(
+                attention_mask=att_2d_masks_4d,
+                position_ids=position_ids,
+                past_key_values=None,
+                inputs_embeds=[prefix_embs, suffix_embs],
+                use_cache=False,
+                adarms_cond=[None, adarms_cond],
+            )
+            return suffix_out
+
+        suffix_out = self._apply_checkpoint(
+            forward_func, prefix_embs, suffix_embs, att_2d_masks_4d, position_ids, adarms_cond
+        )
+
+        suffix_out = suffix_out[:, -self.config.action_horizon :]
+        suffix_out = suffix_out.to(dtype=torch.float32)
+
+        # Apply gradient checkpointing to final action projection if enabled
+        def action_out_proj_func(suffix_out):
+            return self.action_out_proj(suffix_out)
+
+        v_t = self._apply_checkpoint(action_out_proj_func, suffix_out)
+
+        return F.mse_loss(u_t, v_t, reduction="none")
+
+    @torch.no_grad()
+    def sample_actions(self, device, observation, noise=None, num_steps=10) -> Tensor:
+        """Do a full inference forward and compute the action (batch_size x num_steps x num_motors)"""
+        bsize = observation.state.shape[0]
+        if noise is None:
+            actions_shape = (bsize, self.config.action_horizon, self.config.action_dim)
+            noise = self.sample_noise(actions_shape, device)
+
+        images, img_masks, lang_tokens, lang_masks, state = self._preprocess_observation(
+            observation, train=False
+        )
+
+        prefix_embs, prefix_pad_masks, prefix_att_masks = self.embed_prefix(
+            images, img_masks, lang_tokens, lang_masks
+        )
+        prefix_att_2d_masks = make_att_2d_masks(prefix_pad_masks, prefix_att_masks)
+        prefix_position_ids = torch.cumsum(prefix_pad_masks, dim=1) - 1
+
+        # Compute image and language key value cache
+        prefix_att_2d_masks_4d = self._prepare_attention_masks_4d(prefix_att_2d_masks)
+        self.paligemma_with_expert.paligemma.model.language_model.config._attn_implementation = "eager"  # noqa: SLF001
+
+        _, past_key_values = self.paligemma_with_expert.forward(
+            attention_mask=prefix_att_2d_masks_4d,
+            position_ids=prefix_position_ids,
+            past_key_values=None,
+            inputs_embeds=[prefix_embs, None],
+            use_cache=True,
+        )
+
+        dt = -1.0 / num_steps
+        dt = torch.tensor(dt, dtype=torch.float32, device=device)
+
+        x_t = noise
+        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        while time >= -dt / 2:
+            expanded_time = time.expand(bsize)
+            v_t = self.denoise_step(
+                state,
+                prefix_pad_masks,
+                past_key_values,
+                x_t,
+                expanded_time,
+            )
+
+            # Euler step - use new tensor assignment instead of in-place operation
+            x_t = x_t + dt * v_t
+            time += dt
+        return x_t
+
+    def denoise_step(
+        self,
+        state,
+        prefix_pad_masks,
+        past_key_values,
+        x_t,
+        timestep,
+    ):
+        """Apply one denoising step of the noise `x_t` at a given timestep."""
+        suffix_embs, suffix_pad_masks, suffix_att_masks, adarms_cond = self.embed_suffix(state, x_t, timestep)
+
+        suffix_len = suffix_pad_masks.shape[1]
+        batch_size = prefix_pad_masks.shape[0]
+        prefix_len = prefix_pad_masks.shape[1]
+
+        prefix_pad_2d_masks = prefix_pad_masks[:, None, :].expand(batch_size, suffix_len, prefix_len)
+
+        suffix_att_2d_masks = make_att_2d_masks(suffix_pad_masks, suffix_att_masks)
+
+        full_att_2d_masks = torch.cat([prefix_pad_2d_masks, suffix_att_2d_masks], dim=2)
+
+        prefix_offsets = torch.sum(prefix_pad_masks, dim=-1)[:, None]
+        position_ids = prefix_offsets + torch.cumsum(suffix_pad_masks, dim=1) - 1
+
+        # Prepare attention masks
+        full_att_2d_masks_4d = self._prepare_attention_masks_4d(full_att_2d_masks)
+        self.paligemma_with_expert.gemma_expert.model.config._attn_implementation = "eager"  # noqa: SLF001
+
+        past_key_values = copy.deepcopy(past_key_values)
+        outputs_embeds, _ = self.paligemma_with_expert.forward(
+            attention_mask=full_att_2d_masks_4d,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=[None, suffix_embs],
+            use_cache=False,
+            adarms_cond=[None, adarms_cond],
+        )
+
+        suffix_out = outputs_embeds[1]
+        suffix_out = suffix_out[:, -self.config.action_horizon :]
+        suffix_out = suffix_out.to(dtype=torch.float32)
+        return self.action_out_proj(suffix_out)
@@ -0,0 +1,179 @@
+import logging
+from collections.abc import Sequence
+
+import torch
+
+from tests.policies.pi0_pi05.openpi_pytorch import image_tools
+
+logger = logging.getLogger("openpi")
+
+# Constants moved from model.py
+IMAGE_KEYS = (
+    "base_0_rgb",
+    "left_wrist_0_rgb",
+    "right_wrist_0_rgb",
+)
+
+IMAGE_RESOLUTION = (224, 224)
+
+
+def preprocess_observation_pytorch(
+    observation,
+    *,
+    train: bool = False,
+    image_keys: Sequence[str] = IMAGE_KEYS,
+    image_resolution: tuple[int, int] = IMAGE_RESOLUTION,
+):
+    """Torch.compile-compatible version of preprocess_observation_pytorch with simplified type annotations.
+
+    This function avoids complex type annotations that can cause torch.compile issues.
+    """
+    if not set(image_keys).issubset(observation.images):
+        raise ValueError(f"images dict missing keys: expected {image_keys}, got {list(observation.images)}")
+
+    batch_shape = observation.state.shape[:-1]
+
+    out_images = {}
+    for key in image_keys:
+        image = observation.images[key]
+
+        # TODO: This is a hack to handle both [B, C, H, W] and [B, H, W, C] formats
+        # Handle both [B, C, H, W] and [B, H, W, C] formats
+        is_channels_first = image.shape[1] == 3  # Check if channels are in dimension 1
+
+        if is_channels_first:
+            # Convert [B, C, H, W] to [B, H, W, C] for processing
+            image = image.permute(0, 2, 3, 1)
+
+        if image.shape[1:3] != image_resolution:
+            logger.info(f"Resizing image {key} from {image.shape[1:3]} to {image_resolution}")
+            image = image_tools.resize_with_pad_torch(image, *image_resolution)
+
+        if train:
+            # Convert from [-1, 1] to [0, 1] for PyTorch augmentations
+            image = image / 2.0 + 0.5
+
+            # Apply PyTorch-based augmentations
+            if "wrist" not in key:
+                # Geometric augmentations for non-wrist cameras
+                height, width = image.shape[1:3]
+
+                # Random crop and resize
+                crop_height = int(height * 0.95)
+                crop_width = int(width * 0.95)
+
+                # Random crop
+                max_h = height - crop_height
+                max_w = width - crop_width
+                if max_h > 0 and max_w > 0:
+                    # Use tensor operations instead of .item() for torch.compile compatibility
+                    start_h = torch.randint(0, max_h + 1, (1,), device=image.device)
+                    start_w = torch.randint(0, max_w + 1, (1,), device=image.device)
+                    image = image[:, start_h : start_h + crop_height, start_w : start_w + crop_width, :]
+
+                # Resize back to original size
+                image = torch.nn.functional.interpolate(
+                    image.permute(0, 3, 1, 2),  # [b, h, w, c] -> [b, c, h, w]
+                    size=(height, width),
+                    mode="bilinear",
+                    align_corners=False,
+                ).permute(0, 2, 3, 1)  # [b, c, h, w] -> [b, h, w, c]
+
+                # Random rotation (small angles)
+                # Use tensor operations instead of .item() for torch.compile compatibility
+                angle = torch.rand(1, device=image.device) * 10 - 5  # Random angle between -5 and 5 degrees
+                if torch.abs(angle) > 0.1:  # Only rotate if angle is significant
+                    # Convert to radians
+                    angle_rad = angle * torch.pi / 180.0
+
+                    # Create rotation matrix
+                    cos_a = torch.cos(angle_rad)
+                    sin_a = torch.sin(angle_rad)
+
+                    # Apply rotation using grid_sample
+                    grid_x = torch.linspace(-1, 1, width, device=image.device)
+                    grid_y = torch.linspace(-1, 1, height, device=image.device)
+
+                    # Create meshgrid
+                    grid_y, grid_x = torch.meshgrid(grid_y, grid_x, indexing="ij")
+
+                    # Expand to batch dimension
+                    grid_x = grid_x.unsqueeze(0).expand(image.shape[0], -1, -1)
+                    grid_y = grid_y.unsqueeze(0).expand(image.shape[0], -1, -1)
+
+                    # Apply rotation transformation
+                    grid_x_rot = grid_x * cos_a - grid_y * sin_a
+                    grid_y_rot = grid_x * sin_a + grid_y * cos_a
+
+                    # Stack and reshape for grid_sample
+                    grid = torch.stack([grid_x_rot, grid_y_rot], dim=-1)
+
+                    image = torch.nn.functional.grid_sample(
+                        image.permute(0, 3, 1, 2),  # [b, h, w, c] -> [b, c, h, w]
+                        grid,
+                        mode="bilinear",
+                        padding_mode="zeros",
+                        align_corners=False,
+                    ).permute(0, 2, 3, 1)  # [b, c, h, w] -> [b, h, w, c]
+
+            # Color augmentations for all cameras
+            # Random brightness
+            # Use tensor operations instead of .item() for torch.compile compatibility
+            brightness_factor = (
+                0.7 + torch.rand(1, device=image.device) * 0.6
+            )  # Random factor between 0.7 and 1.3
+            image = image * brightness_factor
+
+            # Random contrast
+            # Use tensor operations instead of .item() for torch.compile compatibility
+            contrast_factor = (
+                0.6 + torch.rand(1, device=image.device) * 0.8
+            )  # Random factor between 0.6 and 1.4
+            mean = image.mean(dim=[1, 2, 3], keepdim=True)
+            image = (image - mean) * contrast_factor + mean
+
+            # Random saturation (convert to HSV, modify S, convert back)
+            # For simplicity, we'll just apply a random scaling to the color channels
+            # Use tensor operations instead of .item() for torch.compile compatibility
+            saturation_factor = (
+                0.5 + torch.rand(1, device=image.device) * 1.0
+            )  # Random factor between 0.5 and 1.5
+            gray = image.mean(dim=-1, keepdim=True)
+            image = gray + (image - gray) * saturation_factor
+
+            # Clamp values to [0, 1]
+            image = torch.clamp(image, 0, 1)
+
+            # Back to [-1, 1]
+            image = image * 2.0 - 1.0
+
+        # Convert back to [B, C, H, W] format if it was originally channels-first
+        if is_channels_first:
+            image = image.permute(0, 3, 1, 2)  # [B, H, W, C] -> [B, C, H, W]
+
+        out_images[key] = image
+
+    # obtain mask
+    out_masks = {}
+    for key in out_images:
+        if key not in observation.image_masks:
+            # do not mask by default
+            out_masks[key] = torch.ones(batch_shape, dtype=torch.bool, device=observation.state.device)
+        else:
+            out_masks[key] = observation.image_masks[key]
+
+    # Create a simple object with the required attributes instead of using the complex Observation class
+    class SimpleProcessedObservation:
+        def __init__(self, **kwargs):
+            for key, value in kwargs.items():
+                setattr(self, key, value)
+
+    return SimpleProcessedObservation(
+        images=out_images,
+        image_masks=out_masks,
+        state=observation.state,
+        tokenized_prompt=observation.tokenized_prompt,
+        tokenized_prompt_mask=observation.tokenized_prompt_mask,
+        token_ar_mask=observation.token_ar_mask,
+        token_loss_mask=observation.token_loss_mask,
+    )
@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import pytest
+import torch
+
+pytest.importorskip("transformers")
+
+from lerobot.policies.pi05 import PI05Config  # noqa: E402
+from lerobot.policies.pi05.modeling_pi05 import PI05Pytorch  # noqa: E402
+from tests.policies.pi0_pi05.utils.torch_compile import (  # noqa: E402
+    assert_cache_stability,
+    assert_compiled_output_matches_eager,
+    assert_explain_has_no_graph_breaks,
+    benchmark_runtime,
+    make_compile_config,
+    reset_compile_state,
+)
+from tests.utils import require_cuda  # noqa: E402
+
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="torch.compile benchmark is too slow for CI; run manually on GPU nodes",
+)
+
+
+def _make_model(*, compile_model):
+    return PI05Pytorch(make_compile_config(PI05Config, compile_model=compile_model)).cuda().eval()
+
+
+def _make_dummy_inputs(config):
+    device = torch.device("cuda")
+    common = {
+        "images": [torch.randn(1, 3, *config.image_resolution, device=device)],
+        "img_masks": [torch.ones(1, dtype=torch.bool, device=device)],
+        "tokens": torch.randint(0, 1024, (1, 5), dtype=torch.long, device=device),
+        "masks": torch.ones(1, 5, dtype=torch.bool, device=device),
+    }
+    forward_kwargs = {
+        **common,
+        "actions": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "noise": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "time": torch.rand(1, device=device),
+    }
+    sample_kwargs = {
+        **common,
+        "noise": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "num_steps": config.num_inference_steps,
+    }
+    return forward_kwargs, sample_kwargs
+
+
+@require_cuda
+def test_pi05_torch_compile_forward_and_sample_actions():
+    if not hasattr(torch, "compile"):
+        pytest.skip("torch.compile is not available")
+    if not torch._dynamo.is_dynamo_supported():
+        pytest.skip("torch._dynamo is not supported on this platform")
+
+    torch.manual_seed(0)
+    eager_model = _make_model(compile_model=False)
+    torch.manual_seed(0)
+    compiled_model = _make_model(compile_model=True)
+    forward_kwargs, sample_kwargs = _make_dummy_inputs(compiled_model.config)
+
+    try:
+        assert_compiled_output_matches_eager(eager_model, compiled_model, forward_kwargs, sample_kwargs)
+
+        assert_explain_has_no_graph_breaks(eager_model.forward, forward_kwargs, "pi05.forward")
+        assert_explain_has_no_graph_breaks(eager_model.sample_actions, sample_kwargs, "pi05.sample_actions")
+
+        assert_cache_stability(compiled_model.forward, forward_kwargs, "pi05.forward")
+        assert_cache_stability(compiled_model.sample_actions, sample_kwargs, "pi05.sample_actions")
+
+        benchmark_runtime(eager_model.forward, compiled_model.forward, forward_kwargs, "pi05.forward")
+        benchmark_runtime(
+            eager_model.sample_actions,
+            compiled_model.sample_actions,
+            sample_kwargs,
+            "pi05.sample_actions",
+        )
+    finally:
+        reset_compile_state()
+        del eager_model
+        del compiled_model
+        torch.cuda.empty_cache()
@@ -14,52 +14,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-"""Test script to verify PI0OpenPI policy integration with LeRobot vs the original implementation"""
+"""Compare LeRobot PI0.5 against the vendored OpenPI PyTorch reference."""

+import gc
 import os
-from copy import deepcopy
-from typing import Any

-import numpy as np
 import pytest
 import torch

-# Skip if openpi or transformers is not available
-pytest.importorskip("openpi")
 pytest.importorskip("transformers")

-# Skip this entire module in CI
-pytestmark = pytest.mark.skipif(
-    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="This test requires local OpenPI installation and is not meant for CI",
+from lerobot.configs import PreTrainedConfig  # noqa: E402
+from lerobot.policies.pi05 import PI05Policy  # noqa: E402
+from lerobot.policies.pi05.processor_pi05 import make_pi05_pre_post_processors  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_STATE  # noqa: E402
+from tests.policies.pi0_pi05.openpi_pytorch.pi0_pytorch import PI0Pytorch  # noqa: E402
+from tests.policies.pi0_pi05.utils.openpi_parity import (  # noqa: E402
+    assert_processor_inputs_match_lerobot,
+    clone_batch,
+    deterministic_openpi_forward_preprocess,
+    fix_reference_state_dict,
+    fixed_flow_sampling,
+    load_openpi_reference_state_dict,
+    make_openpi_observation_from_raw,
+    openpi_model_actions_from_raw,
 )

-from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing  # noqa: E402
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="OpenPI parity and torch.compile checks are too slow for CI; run manually on GPU nodes",
+)

-# NOTE: Assumes PYTHONPATH is set to include OpenPI src as per instructions.
-from openpi.models_pytorch.pi0_pytorch import PI0Pytorch  # noqa: E402
-from transformers import AutoTokenizer  # noqa: E402
-
-from lerobot.policies.pi05 import PI05Config, PI05Policy  # noqa: E402
-from lerobot.policies.pi05.processor_pi05 import make_pi05_pre_post_processors  # noqa: E402
-from lerobot.processor import PolicyProcessorPipeline  # noqa: E402
-from lerobot.types import PolicyAction  # noqa: E402
-
-# TODO: ADDING DEFAULT IMAGES_FEATURES TO CONFIG
 DUMMY_ACTION_DIM = 32
 DUMMY_STATE_DIM = 32
 DUMMY_ACTION_HORIZON = 50
 DUMMY_MAX_TOKEN_LEN = 200
-DEVICE = "cpu"  # Use CPU to avoid memory issues for testing
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+COMPILE_MODE = "default"
+FORWARD_RTOL = 1e-4
+FORWARD_ATOL = 1e-4
+SAMPLE_RTOL = 1e-2
+SAMPLE_ATOL = 5e-3

 DUMMY_DATASET_STATS = {
-    "observation.state": {
+    OBS_STATE: {
        "mean": torch.zeros(DUMMY_STATE_DIM),
        "std": torch.ones(DUMMY_STATE_DIM),
        "q01": torch.zeros(DUMMY_STATE_DIM),
        "q99": torch.ones(DUMMY_STATE_DIM),
    },
-    "action": {
+    ACTION: {
        "mean": torch.zeros(DUMMY_ACTION_DIM),
        "std": torch.ones(DUMMY_ACTION_DIM),
        "q01": torch.zeros(DUMMY_ACTION_DIM),
@@ -88,6 +92,15 @@ DUMMY_DATASET_STATS = {
 }


+@pytest.fixture(autouse=True)
+def cleanup_cuda_after_test():
+    yield
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
 class PI05BaseOriginalConfig:
    action_dim: int = DUMMY_ACTION_DIM
    action_horizon: int = DUMMY_ACTION_HORIZON
@@ -96,341 +109,163 @@ class PI05BaseOriginalConfig:
    precision: str = "float32"
    pi05: bool = True
    dtype: str = "float32"
+    pytorch_compile_mode: str | None = None


-def instantiate_lerobot_pi05(
-    from_pretrained: bool = False,
-) -> tuple[
-    PI05Policy,
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    if from_pretrained:
-        # Load the policy first
-        policy = PI05Policy.from_pretrained(pretrained_name_or_path="lerobot/pi05_base", strict=True)
-    else:
-        config = PI05Config(max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32")
-        policy = PI05Policy(config)
+def instantiate_lerobot_pi05(*, compile_model: bool = False, gradient_checkpointing: bool = False):
+    config = PreTrainedConfig.from_pretrained("lerobot/pi05_base")
+    config.device = str(DEVICE)
+    config.dtype = "float32"
+    config.compile_model = compile_model
+    config.compile_mode = COMPILE_MODE
+    config.gradient_checkpointing = gradient_checkpointing

+    policy = PI05Policy.from_pretrained("lerobot/pi05_base", config=config, strict=True)
    policy.to(DEVICE)
-    policy.config.device = DEVICE
-    preprocessor, postprocessor = make_pi05_pre_post_processors(
-        config=policy.config, dataset_stats=DUMMY_DATASET_STATS
-    )
-    return (policy, preprocessor, postprocessor)
+    policy.config.device = str(DEVICE)
+    preprocessor, _ = make_pi05_pre_post_processors(config=policy.config, dataset_stats=DUMMY_DATASET_STATS)
+    return policy, preprocessor


-def instantiate_original_pi05(from_pretrained: bool = False, model_path: str | None = None):
-    config = PI05BaseOriginalConfig()
-    policy = PI0Pytorch(config)
+def instantiate_original_pi05():
+    policy = PI0Pytorch(PI05BaseOriginalConfig()).to(DEVICE)

-    if from_pretrained:
-        try:
-            print("Loading converted PyTorch weights from HuggingFace Hub (lerobot/pi05_base)...")
-
-            # Download the model from HuggingFace Hub
-            import safetensors.torch
-            from huggingface_hub import snapshot_download
-
-            # Download the entire repository
-            if model_path and os.path.exists(model_path):
-                cache_dir = model_path
-                print(f"Using cached model from: {cache_dir}")
-            else:
-                cache_dir = snapshot_download(repo_id="lerobot/pi05_base", repo_type="model")
-                print(f"Downloaded model to: {cache_dir}")
-
-            # Try to load safetensors format first
-            model_file = os.path.join(cache_dir, "model.safetensors")
-            if os.path.exists(model_file):
-                state_dict = safetensors.torch.load_file(model_file)
-                print(f"Loaded {len(state_dict)} parameters from safetensors")
-            else:
-                raise FileNotFoundError(f"No safetensors file found in {cache_dir}")
-
-            # Load the state dict into the model
-            missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False)
-
-            if missing_keys:
-                print(f"Missing keys: {len(missing_keys)}")
-                if len(missing_keys) <= 5:
-                    for key in missing_keys:
-                        print(f"    - {key}")
-                else:
-                    for key in missing_keys[:5]:
-                        print(f"    - {key}")
-                    print(f"    ... and {len(missing_keys) - 5} more")
-
-            if unexpected_keys:
-                print(f"Unexpected keys: {len(unexpected_keys)}")
-                if len(unexpected_keys) <= 5:
-                    for key in unexpected_keys:
-                        print(f"    - {key}")
-                else:
-                    for key in unexpected_keys[:5]:
-                        print(f"    - {key}")
-                    print(f"    ... and {len(unexpected_keys) - 5} more")
-
-            if not missing_keys and not unexpected_keys:
-                print("All pretrained weights loaded successfully!")
-            else:
-                print("Pretrained weights loaded with some missing/unexpected keys (this may be normal)")
-
-        except Exception as e:
-            print(f"Failed to load pretrained weights: {e}")
-            print("   Using randomly initialized weights...")
-            import traceback
-
-            traceback.print_exc()
-
-    policy.to(DEVICE)
+    # NOTE: `lerobot/pi05_base` 的 LeRobot loader 和 PI0 一样会在 strict load 前做 key
+    # 兼容转换，因此预期没有 missing_keys 或 unexpected_keys。vendored reference 则是裸
+    # `nn.Module`，需要在测试侧补齐 checkpoint 与模块命名之间的最小差异。
+    # NOTE: `lm_head.weight` 是 PaliGemma tied embedding 的保存名；LeRobot 的
+    # from_pretrained 会把它映射到内部 `embed_tokens.weight`，而 reference 模型没有这层
+    # loader，所以这里手动复用同一份 tensor，避免把权重别名差异误判成模型差异。
+    state_dict = fix_reference_state_dict(load_openpi_reference_state_dict("lerobot/pi05_base"))
+    missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False)
+    assert missing_keys == []
+    assert unexpected_keys == []
    return policy


 def create_dummy_data():
-    batch_size = 2  # Reduce batch size for testing
-    device = DEVICE
-
-    # Use the exact same prompt for both implementations
+    batch_size = 2
    prompt = "Pick up the red block and place it in the bin"
-
-    batch = {
-        "observation.state": torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device),
-        "action": torch.randn(
-            batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=device
+    return {
+        OBS_STATE: torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=DEVICE),
+        ACTION: torch.randn(
+            batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=DEVICE
        ),
-        # Create images in [0, 1] range as expected by LeRobot (will be converted to [-1, 1] internally)
        "observation.images.base_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
        "observation.images.left_wrist_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
        "observation.images.right_wrist_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
-        # Add the task prompt for LeRobot - provide as list with single element to trigger expansion
        "task": [prompt for _ in range(batch_size)],
    }
-    return batch


-def extract_lerobot_processed_inputs(lerobot_pi0, batch):
-    """Extract the exact same processed inputs that LeRobot uses internally."""
-    # Get the tokenized language from LeRobot's internal method
-    lang_tokens, lang_masks = lerobot_pi0._tokenize_language(batch)
-
-    # Get the preprocessed images from LeRobot's internal method
-    images, img_masks = lerobot_pi0._preprocess_images(batch, train=False)
-
-    # Create dummy token_ar_mask and token_loss_mask for original implementation
-    token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
-    token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
-
-    return images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask
+def prepare_parity_inputs(lerobot_pi05, lerobot_preprocessor):
+    torch.manual_seed(0)
+    raw_batch = create_dummy_data()
+    lerobot_batch = lerobot_preprocessor(clone_batch(raw_batch))
+    openpi_observation = make_openpi_observation_from_raw(
+        raw_batch,
+        action_dim=DUMMY_ACTION_DIM,
+        max_token_len=DUMMY_MAX_TOKEN_LEN,
+        dataset_stats=DUMMY_DATASET_STATS,
+        pi05=True,
+    )
+    openpi_actions = openpi_model_actions_from_raw(
+        raw_batch,
+        action_dim=DUMMY_ACTION_DIM,
+        dataset_stats=DUMMY_DATASET_STATS,
+        pi05=True,
+    )
+    assert_processor_inputs_match_lerobot(
+        lerobot_pi05,
+        lerobot_batch,
+        openpi_observation,
+        compare_state=False,
+    )
+    batch_size = raw_batch[OBS_STATE].shape[0]
+    noise = torch.randn(
+        batch_size,
+        DUMMY_ACTION_HORIZON,
+        DUMMY_ACTION_DIM,
+        dtype=torch.float32,
+        device=DEVICE,
+    )
+    time = torch.linspace(0.2, 0.8, batch_size, dtype=torch.float32, device=DEVICE)
+    return lerobot_batch, openpi_observation, openpi_actions, noise, time


-class PI05Observation:
-    """Observation class that matches the original OpenPI format."""
-
-    def __init__(
-        self,
-        state,
-        images,
-        image_masks,
-        tokenized_prompt,
-        tokenized_prompt_mask,
-        token_ar_mask,
-        token_loss_mask,
-    ):
-        self.state = state
-        self.images = images
-        self.image_masks = image_masks
-        self.tokenized_prompt = tokenized_prompt
-        self.tokenized_prompt_mask = tokenized_prompt_mask
-        self.token_ar_mask = token_ar_mask
-        self.token_loss_mask = token_loss_mask
-
-
-def create_original_observation_with_openpi_preprocessing(batch):
-    """Create observation object for OpenPI using OpenPI's own preprocessing with pi05 state tokenizer."""
-    batch_size = batch["observation.state"].shape[0]
-    device = batch["observation.state"].device
-
-    # Create tokenizer for OpenPI (same as LeRobot uses)
-    tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224")
-
-    # Get task description (pi05 processor handles all text formatting)
-    tasks = batch.get("task", ["Pick up the object"] * batch_size)
-    if isinstance(tasks, str):
-        tasks = [tasks] * batch_size
-    elif len(tasks) == 1:
-        tasks = tasks * batch_size
-
-    # Use pi05 state and input tokenizer logic (same as Pi05PrepareStateTokenizerProcessorStep)
-    state = batch["observation.state"]
-    state = deepcopy(state)
-
-    # Prepare state (pad to max_state_dim)
-    from lerobot.policies.pi05.modeling_pi05 import pad_vector
-
-    state = pad_vector(state, DUMMY_STATE_DIM)
-
-    # Normalize state to [-1, 1] range if needed (assuming it's already normalized from normalize_inputs)
-    # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
-    state_np = state.cpu().numpy()
-    discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1
-
-    # Create pi05-formatted prompts that include state information
-    full_prompts = []
-    for i, task in enumerate(tasks):
-        cleaned_text = task.strip().replace("_", " ").replace("\n", " ")
-        state_str = " ".join(map(str, discretized_states[i]))
-        full_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: "
-        full_prompts.append(full_prompt)
-
-    # Tokenize with max_length padding to match OpenPI's expected format
-    tokenized = tokenizer(
-        full_prompts,
-        padding="max_length",
-        padding_side="right",
-        truncation=True,
-        max_length=DUMMY_MAX_TOKEN_LEN,
-        return_tensors="pt",
+def assert_forward_matches(*, compile_model: bool = False, gradient_checkpointing: bool = False):
+    lerobot_pi05, lerobot_preprocessor = instantiate_lerobot_pi05(
+        compile_model=compile_model,
+        gradient_checkpointing=gradient_checkpointing,
+    )
+    original_pi05 = instantiate_original_pi05()
+    lerobot_batch, openpi_observation, openpi_actions, noise, time = prepare_parity_inputs(
+        lerobot_pi05,
+        lerobot_preprocessor,
    )

-    lang_tokens = tokenized["input_ids"].to(device)
-    lang_masks = tokenized["attention_mask"].to(device, dtype=torch.bool)
+    if gradient_checkpointing:
+        lerobot_pi05.train()
+    else:
+        lerobot_pi05.eval()
+    original_pi05.eval()

-    # Create dummy token_ar_mask and token_loss_mask for OpenPI
-    token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
-    token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
+    with fixed_flow_sampling(lerobot_pi05.model, noise=noise, time=time):
+        lerobot_loss, _ = lerobot_pi05(lerobot_batch, reduction="none")
+    with deterministic_openpi_forward_preprocess(original_pi05):
+        openpi_losses = original_pi05(openpi_observation, openpi_actions, noise=noise, time=time)
+    openpi_loss = openpi_losses.mean(dim=(1, 2))

-    # Convert LeRobot images format to OpenPI format (convert [0,1] to [-1,1] range)
-    image_dict = {
-        "base_0_rgb": batch["observation.images.base_0_rgb"] * 2.0 - 1.0,
-        "left_wrist_0_rgb": batch["observation.images.left_wrist_0_rgb"] * 2.0 - 1.0,
-        "right_wrist_0_rgb": batch["observation.images.right_wrist_0_rgb"] * 2.0 - 1.0,
-    }
+    torch.testing.assert_close(lerobot_loss, openpi_loss, rtol=FORWARD_RTOL, atol=FORWARD_ATOL)

-    # Create image masks (all ones for real images)
-    image_masks_dict = {}
-    for key in image_dict:
-        image_masks_dict[key] = torch.ones(batch_size, dtype=torch.bool, device=device)

-    # Create raw observation object (before preprocessing)
-    raw_observation = PI05Observation(
-        state=batch["observation.state"],
-        images=image_dict,
-        image_masks=image_masks_dict,
-        tokenized_prompt=lang_tokens,
-        tokenized_prompt_mask=lang_masks,
-        token_ar_mask=token_ar_mask,
-        token_loss_mask=token_loss_mask,
+def assert_sample_actions_match_openpi(*, compile_model: bool = False):
+    lerobot_pi05, lerobot_preprocessor = instantiate_lerobot_pi05(compile_model=compile_model)
+    original_pi05 = instantiate_original_pi05()
+    lerobot_batch, openpi_observation, _openpi_actions, noise, _time = prepare_parity_inputs(
+        lerobot_pi05,
+        lerobot_preprocessor,
    )

-    # Now use OpenPI's preprocessing
-    processed_obs = openpi_preprocessing.preprocess_observation_pytorch(raw_observation, train=False)
-
-    return processed_obs
-
-
-def create_original_observation_from_lerobot(lerobot_pi0, batch):
-    """Create observation object compatible with original OpenPI using the exact same inputs as LeRobot."""
-    _batch_size = batch["observation.state"].shape[0]
-    _device = batch["observation.state"].device
-
-    # Extract the exact same processed inputs that LeRobot uses
-    images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask = (
-        extract_lerobot_processed_inputs(lerobot_pi0, batch)
-    )
-
-    # Convert images list to dict with original OpenPI keys
-    image_dict = {
-        "base_0_rgb": images[0],
-        "left_wrist_0_rgb": images[1],
-        "right_wrist_0_rgb": images[2],
-    }
-
-    # Convert image masks list to dict with original OpenPI keys
-    image_masks_dict = {
-        "base_0_rgb": img_masks[0],
-        "left_wrist_0_rgb": img_masks[1],
-        "right_wrist_0_rgb": img_masks[2],
-    }
-
-    return PI05Observation(
-        state=batch["observation.state"],
-        images=image_dict,
-        image_masks=image_masks_dict,
-        tokenized_prompt=lang_tokens,
-        tokenized_prompt_mask=lang_masks,
-        token_ar_mask=token_ar_mask,
-        token_loss_mask=token_loss_mask,
-    )
-
-
-def test_pi05_original_vs_lerobot():
-    """Test PI05 original implementation vs LeRobot implementation."""
-    print("Initializing models...")
-    lerobot_pi05, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_pi05(
-        from_pretrained=True
-    )  # Load pretrained LeRobot model
-    original_pi0 = instantiate_original_pi05(
-        from_pretrained=True
-    )  # Load pretrained OpenPI model from HuggingFace Hub
-
-    print("Creating dummy data...")
-    batch = create_dummy_data()
-    batch_lerobot = deepcopy(batch)
-
-    # Test each model with its own preprocessing (more realistic end-to-end test)
-    print("\nTest each model with its own preprocessing")
-    print("Creating observation for OpenPI using OpenPI's own preprocessing...")
-    pi0_obs_openpi = create_original_observation_with_openpi_preprocessing(batch)
-
-    print(f"Task prompt: '{batch['task'][0]}'")
-    print(f"OpenPI tokenized prompt shape: {pi0_obs_openpi.tokenized_prompt.shape}")
-    print(f"OpenPI image shapes: {[img.shape for img in pi0_obs_openpi.images.values()]}")
-    print(f"OpenPI state shape: {pi0_obs_openpi.state.shape}")
-
-    print("Testing OpenPI with own preprocessing...")
-    original_pi0.eval()
-    torch.manual_seed(42)  # Set seed for reproducibility
-    batch_size = batch["observation.state"].shape[0]
-    noise_shape = (batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM)
-    fixed_noise = torch.randn(noise_shape, dtype=torch.float32, device=DEVICE)
-
-    with torch.no_grad():
-        openpi_actions = original_pi0.sample_actions(
-            device=DEVICE, observation=pi0_obs_openpi, noise=fixed_noise, num_steps=10
-        )
-        openpi_actions_unit = openpi_actions[:, 0, :]
-    print(f"OpenPI (own preprocessing) Actions shape: {openpi_actions.shape}")
-    print(f"OpenPI (own preprocessing) Actions unit shape: {openpi_actions_unit.shape}")
-    print(f"OpenPI (own preprocessing) Actions mean: {openpi_actions.mean().item():.6f}")
-    print(f"OpenPI (own preprocessing) Actions std: {openpi_actions.std().item():.6f}")
-
-    print("Testing LeRobot with own preprocessing...")
    lerobot_pi05.eval()
-    torch.manual_seed(42)  # Set the same seed
-
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
+    original_pi05.eval()
    with torch.no_grad():
-        lerobot_actions_own = lerobot_pi05.predict_action_chunk(
-            batch_lerobot_processed
-        )  # batch_size, n_action_steps, action_dim
-        lerobot_actions_unit = lerobot_actions_own[:, 0, :]
-    print(f"LeRobot (own preprocessing) Actions shape: {lerobot_actions_own.shape}")
-    print(f"LeRobot (own preprocessing) Actions unit shape: {lerobot_actions_unit.shape}")
-    print(f"LeRobot (own preprocessing) Actions mean: {lerobot_actions_own.mean().item():.6f}")
-    print(f"LeRobot (own preprocessing) Actions std: {lerobot_actions_own.std().item():.6f}")
+        lerobot_actions = lerobot_pi05.predict_action_chunk(lerobot_batch, noise=noise, num_steps=10)
+        openpi_actions = original_pi05.sample_actions(
+            device=DEVICE,
+            observation=openpi_observation,
+            noise=noise,
+            num_steps=10,
+        )

-    print("\nComparing end-to-end implementations:")
-    print(f"Actions close (atol=1e-4): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)}")
-    print(f"Actions close (atol=1e-2): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)}")
-    print(f"Max absolute difference: {torch.abs(lerobot_actions_own - openpi_actions).max().item():.6f}")
+    torch.testing.assert_close(lerobot_actions, openpi_actions, rtol=SAMPLE_RTOL, atol=SAMPLE_ATOL)

-    assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)
-    assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)
-    assert torch.abs(lerobot_actions_own - openpi_actions).max().item() < 1e-4
+
+def test_pi05_forward_matches_openpi():
+    assert_forward_matches()
+
+
+def test_pi05_sample_actions_match_openpi():
+    assert_sample_actions_match_openpi()
+
+
+def test_pi05_gradient_checkpointing_forward_matches_openpi():
+    assert_forward_matches(gradient_checkpointing=True)
+
+
+def test_pi05_compile_forward_matches_openpi():
+    assert_forward_matches(compile_model=True)
+
+
+def test_pi05_compile_sample_actions_match_openpi():
+    assert_sample_actions_match_openpi(compile_model=True)
+
+
+def test_pi05_compile_gradient_checkpointing_forward_matches_openpi():
+    assert_forward_matches(compile_model=True, gradient_checkpointing=True)
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+
+import pytest
+import torch
+
+pytest.importorskip("transformers")
+
+from lerobot.policies.pi0 import PI0Config  # noqa: E402
+from lerobot.policies.pi0.modeling_pi0 import PI0Pytorch  # noqa: E402
+from tests.policies.pi0_pi05.utils.torch_compile import (  # noqa: E402
+    assert_cache_stability,
+    assert_compiled_output_matches_eager,
+    assert_explain_has_no_graph_breaks,
+    benchmark_runtime,
+    make_compile_config,
+    reset_compile_state,
+)
+from tests.utils import require_cuda  # noqa: E402
+
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="torch.compile benchmark is too slow for CI; run manually on GPU nodes",
+)
+
+
+def _make_model(*, compile_model):
+    return PI0Pytorch(make_compile_config(PI0Config, compile_model=compile_model)).cuda().eval()
+
+
+def _make_dummy_inputs(config):
+    device = torch.device("cuda")
+    common = {
+        "images": [torch.randn(1, 3, *config.image_resolution, device=device)],
+        "img_masks": [torch.ones(1, dtype=torch.bool, device=device)],
+        "lang_tokens": torch.randint(0, 1024, (1, 5), dtype=torch.long, device=device),
+        "lang_masks": torch.ones(1, 5, dtype=torch.bool, device=device),
+        "state": torch.randn(1, config.max_state_dim, device=device),
+    }
+    forward_kwargs = {
+        **common,
+        "actions": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "noise": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "time": torch.rand(1, device=device),
+    }
+    sample_kwargs = {
+        **common,
+        "noise": torch.randn(1, config.chunk_size, config.max_action_dim, device=device),
+        "num_steps": config.num_inference_steps,
+    }
+    return forward_kwargs, sample_kwargs
+
+
+@require_cuda
+def test_pi0_torch_compile_forward_and_sample_actions():
+    if not hasattr(torch, "compile"):
+        pytest.skip("torch.compile is not available")
+    if not torch._dynamo.is_dynamo_supported():
+        pytest.skip("torch._dynamo is not supported on this platform")
+
+    torch.manual_seed(0)
+    eager_model = _make_model(compile_model=False)
+    torch.manual_seed(0)
+    compiled_model = _make_model(compile_model=True)
+    forward_kwargs, sample_kwargs = _make_dummy_inputs(compiled_model.config)
+
+    try:
+        assert_compiled_output_matches_eager(eager_model, compiled_model, forward_kwargs, sample_kwargs)
+
+        assert_explain_has_no_graph_breaks(eager_model.forward, forward_kwargs, "pi0.forward")
+        assert_explain_has_no_graph_breaks(eager_model.sample_actions, sample_kwargs, "pi0.sample_actions")
+
+        assert_cache_stability(compiled_model.forward, forward_kwargs, "pi0.forward")
+        assert_cache_stability(compiled_model.sample_actions, sample_kwargs, "pi0.sample_actions")
+
+        benchmark_runtime(eager_model.forward, compiled_model.forward, forward_kwargs, "pi0.forward")
+        benchmark_runtime(
+            eager_model.sample_actions, compiled_model.sample_actions, sample_kwargs, "pi0.sample_actions"
+        )
+    finally:
+        reset_compile_state()
+        del eager_model
+        del compiled_model
+        torch.cuda.empty_cache()
@@ -14,51 +14,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-"""Test script to verify PI0 policy integration with LeRobot vs the original implementation"""
+"""Compare LeRobot PI0 against the vendored OpenPI PyTorch reference."""

+import gc
 import os
-from copy import deepcopy
-from typing import Any

 import pytest
 import torch

-# Skip if openpi or transformers is not available
-pytest.importorskip("openpi")
 pytest.importorskip("transformers")

-# Skip this entire module in CI
-pytestmark = pytest.mark.skipif(
-    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
-    reason="This test requires local OpenPI installation and is not meant for CI",
+from lerobot.configs import PreTrainedConfig  # noqa: E402
+from lerobot.policies.pi0 import PI0Policy  # noqa: E402
+from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_STATE  # noqa: E402
+from tests.policies.pi0_pi05.openpi_pytorch.pi0_pytorch import PI0Pytorch  # noqa: E402
+from tests.policies.pi0_pi05.utils.openpi_parity import (  # noqa: E402
+    assert_processor_inputs_match_lerobot,
+    clone_batch,
+    deterministic_openpi_forward_preprocess,
+    fix_reference_state_dict,
+    fixed_flow_sampling,
+    load_openpi_reference_state_dict,
+    make_openpi_observation_from_raw,
+    openpi_model_actions_from_raw,
 )

-from openpi.models_pytorch import preprocessing_pytorch as openpi_preprocessing  # noqa: E402
+pytestmark = pytest.mark.skipif(
+    os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
+    reason="OpenPI parity and torch.compile checks are too slow for CI; run manually on GPU nodes",
+)

-# NOTE: Assumes PYTHONPATH is set to include OpenPI src as per instructions.
-from openpi.models_pytorch.pi0_pytorch import PI0Pytorch  # noqa: E402
-from transformers import AutoTokenizer  # noqa: E402
-
-from lerobot.policies.pi0 import PI0Config, PI0Policy  # noqa: E402
-from lerobot.policies.pi0.processor_pi0 import make_pi0_pre_post_processors  # noqa: E402
-from lerobot.processor import PolicyProcessorPipeline  # noqa: E402
-from lerobot.types import PolicyAction  # noqa: E402
-
-# TODO: ADDING DEFAULT IMAGES_FEATURES TO CONFIG
 DUMMY_ACTION_DIM = 32
 DUMMY_STATE_DIM = 32
 DUMMY_ACTION_HORIZON = 50
-DUMMY_MAX_TOKEN_LEN = 48  # Default for PI0 (non-pi05)
-DEVICE = "cpu"  # Use CPU to avoid memory issues for testing
+DUMMY_MAX_TOKEN_LEN = 48
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+COMPILE_MODE = "default"
+FORWARD_RTOL = 1e-4
+FORWARD_ATOL = 1e-4
+SAMPLE_RTOL = 1e-2
+SAMPLE_ATOL = 5e-3

 DUMMY_DATASET_STATS = {
-    "observation.state": {
+    OBS_STATE: {
        "mean": torch.zeros(DUMMY_STATE_DIM),
        "std": torch.ones(DUMMY_STATE_DIM),
        "q01": torch.zeros(DUMMY_STATE_DIM),
        "q99": torch.ones(DUMMY_STATE_DIM),
    },
-    "action": {
+    ACTION: {
        "mean": torch.zeros(DUMMY_ACTION_DIM),
        "std": torch.ones(DUMMY_ACTION_DIM),
        "q01": torch.zeros(DUMMY_ACTION_DIM),
@@ -87,6 +92,15 @@ DUMMY_DATASET_STATS = {
 }


+@pytest.fixture(autouse=True)
+def cleanup_cuda_after_test():
+    yield
+    gc.collect()
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
 class PI0BaseOriginalConfig:
    action_dim: int = DUMMY_ACTION_DIM
    action_horizon: int = DUMMY_ACTION_HORIZON
@@ -95,333 +109,156 @@ class PI0BaseOriginalConfig:
    precision: str = "float32"
    pi05: bool = False
    dtype: str = "float32"
+    pytorch_compile_mode: str | None = None


-def instantiate_lerobot_pi0(
-    from_pretrained: bool = False,
-) -> tuple[
-    PI0Policy,
-    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    if from_pretrained:
-        # Load the policy first
-        policy = PI0Policy.from_pretrained(pretrained_name_or_path="lerobot/pi0_base", strict=True)
-    else:
-        config = PI0Config(max_action_dim=DUMMY_ACTION_DIM, max_state_dim=DUMMY_STATE_DIM, dtype="float32")
-        policy = PI0Policy(config)
+def instantiate_lerobot_pi0(*, compile_model: bool = False, gradient_checkpointing: bool = False):
+    config = PreTrainedConfig.from_pretrained("lerobot/pi0_base")
+    config.device = str(DEVICE)
+    config.dtype = "float32"
+    config.compile_model = compile_model
+    config.compile_mode = COMPILE_MODE
+    config.gradient_checkpointing = gradient_checkpointing

+    policy = PI0Policy.from_pretrained("lerobot/pi0_base", config=config, strict=True)
    policy.to(DEVICE)
-    policy.config.device = DEVICE
-    preprocessor, postprocessor = make_pi0_pre_post_processors(
-        config=policy.config, dataset_stats=DUMMY_DATASET_STATS
-    )
-    return (policy, preprocessor, postprocessor)
+    policy.config.device = str(DEVICE)
+    preprocessor, _ = make_pi0_pre_post_processors(config=policy.config, dataset_stats=DUMMY_DATASET_STATS)
+    return policy, preprocessor


-def instantiate_original_pi0(from_pretrained: bool = False, model_path: str = None):
-    config = PI0BaseOriginalConfig()
-    policy = PI0Pytorch(config)
-
-    if from_pretrained:
-        try:
-            print("Loading converted PyTorch weights from HuggingFace Hub (lerobot/pi0_base)...")
-
-            # Download the model from HuggingFace Hub
-            import safetensors.torch
-            from huggingface_hub import snapshot_download
-
-            # Download the entire repository
-            if model_path and os.path.exists(model_path):
-                cache_dir = model_path
-                print(f"Using cached model from: {cache_dir}")
-            else:
-                cache_dir = snapshot_download(repo_id="lerobot/pi0_base", repo_type="model")
-                print(f"Downloaded model to: {cache_dir}")
-
-            # Try to load safetensors format first
-            model_file = os.path.join(cache_dir, "model.safetensors")
-            if os.path.exists(model_file):
-                state_dict = safetensors.torch.load_file(model_file)
-                print(f"Loaded {len(state_dict)} parameters from safetensors")
-            else:
-                raise FileNotFoundError(f"No safetensors file found in {cache_dir}")
-
-            # Load the state dict into the model
-            missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False)
-
-            if missing_keys:
-                print(f"Missing keys: {len(missing_keys)}")
-                if len(missing_keys) <= 5:
-                    for key in missing_keys:
-                        print(f"    - {key}")
-                else:
-                    for key in missing_keys[:5]:
-                        print(f"    - {key}")
-                    print(f"    ... and {len(missing_keys) - 5} more")
-
-            if unexpected_keys:
-                print(f"Unexpected keys: {len(unexpected_keys)}")
-                if len(unexpected_keys) <= 5:
-                    for key in unexpected_keys:
-                        print(f"    - {key}")
-                else:
-                    for key in unexpected_keys[:5]:
-                        print(f"    - {key}")
-                    print(f"    ... and {len(unexpected_keys) - 5} more")
-
-            if not missing_keys and not unexpected_keys:
-                print("All pretrained weights loaded successfully!")
-            else:
-                print("Pretrained weights loaded with some missing/unexpected keys (this may be normal)")
-
-        except Exception as e:
-            print(f"Failed to load pretrained weights: {e}")
-            print("   Using randomly initialized weights...")
-            import traceback
-
-            traceback.print_exc()
-
-    policy.to(DEVICE)
+def instantiate_original_pi0():
+    policy = PI0Pytorch(PI0BaseOriginalConfig()).to(DEVICE)
+    state_dict = fix_reference_state_dict(load_openpi_reference_state_dict("lerobot/pi0_base"))
+    missing_keys, unexpected_keys = policy.load_state_dict(state_dict, strict=False)
+    assert missing_keys == []
+    assert unexpected_keys == []
    return policy


 def create_dummy_data():
-    batch_size = 2  # Reduce batch size for testing
-    device = DEVICE
-
-    # Use the exact same prompt for both implementations
+    batch_size = 2
    prompt = "Pick up the red block and place it in the bin"
-
-    batch = {
-        "observation.state": torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device),
-        "action": torch.randn(
-            batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=device
+    return {
+        OBS_STATE: torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=DEVICE),
+        ACTION: torch.randn(
+            batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM, dtype=torch.float32, device=DEVICE
        ),
-        # Create images in [0, 1] range as expected by LeRobot (will be converted to [-1, 1] internally)
        "observation.images.base_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
        "observation.images.left_wrist_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
        "observation.images.right_wrist_0_rgb": torch.rand(
-            batch_size, 3, 224, 224, dtype=torch.float32, device=device
+            batch_size, 3, 224, 224, dtype=torch.float32, device=DEVICE
        ),
-        # Add the task prompt for LeRobot - provide as list with single element to trigger expansion
        "task": [prompt for _ in range(batch_size)],
    }
-    return batch


-def extract_lerobot_processed_inputs(lerobot_pi0, batch):
-    """Extract the exact same processed inputs that LeRobot uses internally."""
-    # Get the tokenized language from LeRobot's internal method
-    lang_tokens, lang_masks = lerobot_pi0._tokenize_language(batch)
-
-    # Get the preprocessed images from LeRobot's internal method
-    images, img_masks = lerobot_pi0._preprocess_images(batch, train=False)
-
-    # Create dummy token_ar_mask and token_loss_mask for original implementation
-    token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
-    token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
-
-    return images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask
+def prepare_parity_inputs(lerobot_pi0, lerobot_preprocessor):
+    torch.manual_seed(0)
+    raw_batch = create_dummy_data()
+    lerobot_batch = lerobot_preprocessor(clone_batch(raw_batch))
+    openpi_observation = make_openpi_observation_from_raw(
+        raw_batch,
+        action_dim=DUMMY_ACTION_DIM,
+        max_token_len=DUMMY_MAX_TOKEN_LEN,
+        dataset_stats=DUMMY_DATASET_STATS,
+        pi05=False,
+    )
+    openpi_actions = openpi_model_actions_from_raw(
+        raw_batch,
+        action_dim=DUMMY_ACTION_DIM,
+        dataset_stats=DUMMY_DATASET_STATS,
+        pi05=False,
+    )
+    assert_processor_inputs_match_lerobot(
+        lerobot_pi0,
+        lerobot_batch,
+        openpi_observation,
+        compare_state=True,
+    )
+    batch_size = raw_batch[OBS_STATE].shape[0]
+    noise = torch.randn(
+        batch_size,
+        DUMMY_ACTION_HORIZON,
+        DUMMY_ACTION_DIM,
+        dtype=torch.float32,
+        device=DEVICE,
+    )
+    time = torch.linspace(0.2, 0.8, batch_size, dtype=torch.float32, device=DEVICE)
+    return lerobot_batch, openpi_observation, openpi_actions, noise, time


-class PI0Observation:
-    """Observation class that matches the original OpenPI format."""
+def assert_forward_matches(*, compile_model: bool = False, gradient_checkpointing: bool = False):
+    lerobot_pi0, lerobot_preprocessor = instantiate_lerobot_pi0(
+        compile_model=compile_model,
+        gradient_checkpointing=gradient_checkpointing,
+    )
+    original_pi0 = instantiate_original_pi0()
+    lerobot_batch, openpi_observation, openpi_actions, noise, time = prepare_parity_inputs(
+        lerobot_pi0,
+        lerobot_preprocessor,
+    )

-    def __init__(
-        self,
-        state,
-        images,
-        image_masks,
-        tokenized_prompt,
-        tokenized_prompt_mask,
-        token_ar_mask,
-        token_loss_mask,
-    ):
-        self.state = state
-        self.images = images
-        self.image_masks = image_masks
-        self.tokenized_prompt = tokenized_prompt
-        self.tokenized_prompt_mask = tokenized_prompt_mask
-        self.token_ar_mask = token_ar_mask
-        self.token_loss_mask = token_loss_mask
-
-
-def create_original_observation_with_openpi_preprocessing(batch):
-    """Create observation object for OpenPI using OpenPI's own preprocessing."""
-    batch_size = batch["observation.state"].shape[0]
-    device = batch["observation.state"].device
-
-    # Create tokenizer for OpenPI (same as LeRobot uses)
-    tokenizer = AutoTokenizer.from_pretrained("google/paligemma-3b-pt-224")
-
-    # Get task description
-    if "task" in batch:
-        tasks = batch["task"]
-        if isinstance(tasks, str):
-            # Single string: add newline if not present, then convert to list
-            if not tasks.endswith("\n"):
-                tasks = f"{tasks}\n"
-            tasks = [tasks]
-        elif isinstance(tasks, list) and all(isinstance(t, str) for t in tasks):
-            # List of strings: add newline to each if not present
-            tasks = [t if t.endswith("\n") else f"{t}\n" for t in tasks]
-            if len(tasks) == 1:
-                # Expand to batch size
-                tasks = tasks * batch_size
-                if len(tasks) != batch_size:
-                    raise ValueError(f"Expected batch size {batch_size}, got {len(tasks)}")
-        # If task is neither string nor list of strings, leave unchanged
+    if gradient_checkpointing:
+        lerobot_pi0.train()
    else:
-        # Default task if not provided
-        tasks = ["Pick up the object\n"] * batch_size
-
-    # Tokenize with max_length padding to match OpenPI's expected format
-    tokenized = tokenizer(
-        tasks,
-        padding="max_length",
-        padding_side="right",
-        truncation=True,
-        max_length=DUMMY_MAX_TOKEN_LEN,
-        return_tensors="pt",
-    )
-
-    lang_tokens = tokenized["input_ids"].to(device)
-    lang_masks = tokenized["attention_mask"].to(device, dtype=torch.bool)
-
-    # Create dummy token_ar_mask and token_loss_mask for OpenPI
-    token_ar_mask = torch.zeros_like(lang_tokens, dtype=torch.int32)
-    token_loss_mask = torch.ones_like(lang_masks, dtype=torch.bool)
-
-    # Convert LeRobot images format to OpenPI format (convert [0,1] to [-1,1] range)
-    image_dict = {
-        "base_0_rgb": batch["observation.images.base_0_rgb"] * 2.0 - 1.0,
-        "left_wrist_0_rgb": batch["observation.images.left_wrist_0_rgb"] * 2.0 - 1.0,
-        "right_wrist_0_rgb": batch["observation.images.right_wrist_0_rgb"] * 2.0 - 1.0,
-    }
-
-    # Create image masks (all ones for real images)
-    image_masks_dict = {}
-    for key in image_dict:
-        image_masks_dict[key] = torch.ones(batch_size, dtype=torch.bool, device=device)
-
-    # Create raw observation object (before preprocessing)
-    raw_observation = PI0Observation(
-        state=batch["observation.state"],
-        images=image_dict,
-        image_masks=image_masks_dict,
-        tokenized_prompt=lang_tokens,
-        tokenized_prompt_mask=lang_masks,
-        token_ar_mask=token_ar_mask,
-        token_loss_mask=token_loss_mask,
-    )
-
-    # Now use OpenPI's preprocessing
-    processed_obs = openpi_preprocessing.preprocess_observation_pytorch(raw_observation, train=False)
-
-    return processed_obs
-
-
-def create_original_observation_from_lerobot(lerobot_pi0, batch):
-    """Create observation object compatible with original OpenPI using the exact same inputs as LeRobot."""
-    _batch_size = batch["observation.state"].shape[0]
-    _device = batch["observation.state"].device
-
-    # Extract the exact same processed inputs that LeRobot uses
-    images, img_masks, lang_tokens, lang_masks, token_ar_mask, token_loss_mask = (
-        extract_lerobot_processed_inputs(lerobot_pi0, batch)
-    )
-
-    # Convert images list to dict with original OpenPI keys
-    image_dict = {
-        "base_0_rgb": images[0],
-        "left_wrist_0_rgb": images[1],
-        "right_wrist_0_rgb": images[2],
-    }
-
-    # Convert image masks list to dict with original OpenPI keys
-    image_masks_dict = {
-        "base_0_rgb": img_masks[0],
-        "left_wrist_0_rgb": img_masks[1],
-        "right_wrist_0_rgb": img_masks[2],
-    }
-
-    return PI0Observation(
-        state=batch["observation.state"],
-        images=image_dict,
-        image_masks=image_masks_dict,
-        tokenized_prompt=lang_tokens,
-        tokenized_prompt_mask=lang_masks,
-        token_ar_mask=token_ar_mask,
-        token_loss_mask=token_loss_mask,
-    )
-
-
-def test_pi0_original_vs_lerobot():
-    """Test PI0 original implementation vs LeRobot implementation."""
-    print("Initializing models...")
-    lerobot_pi0, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_pi0(
-        from_pretrained=True
-    )  # Load pretrained LeRobot model
-    original_pi0 = instantiate_original_pi0(
-        from_pretrained=True
-    )  # Load pretrained OpenPI model from HuggingFace Hub
-
-    print("Creating dummy data...")
-    batch = create_dummy_data()
-    batch_lerobot = deepcopy(batch)
-
-    # Test each model with its own preprocessing (more realistic end-to-end test)
-    print("\nTest each model with its own preprocessing")
-    print("Creating observation for OpenPI using OpenPI's own preprocessing...")
-    pi0_obs_openpi = create_original_observation_with_openpi_preprocessing(batch)
-
-    print(f"Task prompt: '{batch['task'][0]}'")
-    print(f"OpenPI tokenized prompt shape: {pi0_obs_openpi.tokenized_prompt.shape}")
-    print(f"OpenPI image shapes: {[img.shape for img in pi0_obs_openpi.images.values()]}")
-    print(f"OpenPI state shape: {pi0_obs_openpi.state.shape}")
-
-    print("Testing OpenPI with own preprocessing...")
+        lerobot_pi0.eval()
    original_pi0.eval()
-    torch.manual_seed(42)  # Set seed for reproducibility
-    batch_size = batch["observation.state"].shape[0]
-    noise_shape = (batch_size, DUMMY_ACTION_HORIZON, DUMMY_ACTION_DIM)
-    fixed_noise = torch.randn(noise_shape, dtype=torch.float32, device=DEVICE)

-    with torch.no_grad():
-        openpi_actions = original_pi0.sample_actions(
-            device=DEVICE, observation=pi0_obs_openpi, noise=fixed_noise, num_steps=10
-        )
-        openpi_actions_unit = openpi_actions[:, 0, :]
-    print(f"OpenPI (own preprocessing) Actions shape: {openpi_actions.shape}")
-    print(f"OpenPI (own preprocessing) Actions unit shape: {openpi_actions_unit.shape}")
-    print(f"OpenPI (own preprocessing) Actions mean: {openpi_actions.mean().item():.6f}")
-    print(f"OpenPI (own preprocessing) Actions std: {openpi_actions.std().item():.6f}")
+    with fixed_flow_sampling(lerobot_pi0.model, noise=noise, time=time):
+        lerobot_loss, _ = lerobot_pi0(lerobot_batch, reduction="none")
+    with deterministic_openpi_forward_preprocess(original_pi0):
+        openpi_losses = original_pi0(openpi_observation, openpi_actions, noise=noise, time=time)
+    openpi_loss = openpi_losses.mean(dim=(1, 2))
+
+    torch.testing.assert_close(lerobot_loss, openpi_loss, rtol=FORWARD_RTOL, atol=FORWARD_ATOL)
+
+
+def assert_sample_actions_match_openpi(*, compile_model: bool = False):
+    lerobot_pi0, lerobot_preprocessor = instantiate_lerobot_pi0(compile_model=compile_model)
+    original_pi0 = instantiate_original_pi0()
+    lerobot_batch, openpi_observation, _openpi_actions, noise, _time = prepare_parity_inputs(
+        lerobot_pi0,
+        lerobot_preprocessor,
+    )

-    print("Testing LeRobot with own preprocessing...")
    lerobot_pi0.eval()
-    torch.manual_seed(42)  # Set the same seed
-
-    batch_lerobot_processed = lerobot_preprocessor(batch_lerobot)
+    original_pi0.eval()
    with torch.no_grad():
-        lerobot_actions_own = lerobot_pi0.predict_action_chunk(
-            batch_lerobot_processed
-        )  # batch_size, n_action_steps, action_dim
-        lerobot_actions_unit = lerobot_actions_own[:, 0, :]
-    print(f"LeRobot (own preprocessing) Actions shape: {lerobot_actions_own.shape}")
-    print(f"LeRobot (own preprocessing) Actions unit shape: {lerobot_actions_unit.shape}")
-    print(f"LeRobot (own preprocessing) Actions mean: {lerobot_actions_own.mean().item():.6f}")
-    print(f"LeRobot (own preprocessing) Actions std: {lerobot_actions_own.std().item():.6f}")
+        lerobot_actions = lerobot_pi0.predict_action_chunk(lerobot_batch, noise=noise, num_steps=10)
+        openpi_actions = original_pi0.sample_actions(
+            device=DEVICE,
+            observation=openpi_observation,
+            noise=noise,
+            num_steps=10,
+        )

-    print("\nComparing end-to-end implementations:")
-    print(f"Actions close (atol=1e-4): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)}")
-    print(f"Actions close (atol=1e-2): {torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)}")
-    print(f"Max absolute difference: {torch.abs(lerobot_actions_own - openpi_actions).max().item():.6f}")
+    torch.testing.assert_close(lerobot_actions, openpi_actions, rtol=SAMPLE_RTOL, atol=SAMPLE_ATOL)

-    assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-4)
-    assert torch.allclose(lerobot_actions_own, openpi_actions, atol=1e-2)
-    assert torch.abs(lerobot_actions_own - openpi_actions).max().item() < 1e-4
+
+def test_pi0_forward_matches_openpi():
+    assert_forward_matches()
+
+
+def test_pi0_sample_actions_match_openpi():
+    assert_sample_actions_match_openpi()
+
+
+def test_pi0_gradient_checkpointing_forward_matches_openpi():
+    assert_forward_matches(gradient_checkpointing=True)
+
+
+def test_pi0_compile_forward_matches_openpi():
+    assert_forward_matches(compile_model=True)
+
+
+def test_pi0_compile_sample_actions_match_openpi():
+    assert_sample_actions_match_openpi(compile_model=True)
+
+
+def test_pi0_compile_gradient_checkpointing_forward_matches_openpi():
+    assert_forward_matches(compile_model=True, gradient_checkpointing=True)
@@ -0,0 +1 @@
+"""Utilities shared by PI0/PI05 policy tests."""
@@ -0,0 +1,291 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+from dataclasses import dataclass
+from functools import lru_cache
+from pathlib import Path
+
+import numpy as np
+import safetensors.torch
+import torch
+import torch.nn.functional as F  # noqa: N812
+from huggingface_hub import snapshot_download
+from transformers import AutoTokenizer
+
+from lerobot.utils.constants import (
+    ACTION,
+    OBS_LANGUAGE_ATTENTION_MASK,
+    OBS_LANGUAGE_TOKENS,
+    OBS_STATE,
+)
+from tests.policies.pi0_pi05.openpi_pytorch import preprocessing_pytorch as openpi_preprocessing
+
+IMAGE_KEYS = ("base_0_rgb", "left_wrist_0_rgb", "right_wrist_0_rgb")
+TOKENIZER_NAME = "google/paligemma-3b-pt-224"
+
+
+@dataclass
+class OpenPIObservation:
+    state: torch.Tensor
+    images: dict[str, torch.Tensor]
+    image_masks: dict[str, torch.Tensor]
+    tokenized_prompt: torch.Tensor
+    tokenized_prompt_mask: torch.Tensor
+    token_ar_mask: torch.Tensor
+    token_loss_mask: torch.Tensor
+
+
+@lru_cache(maxsize=1)
+def paligemma_tokenizer():
+    return AutoTokenizer.from_pretrained(TOKENIZER_NAME)
+
+
+def clone_batch(batch: dict) -> dict:
+    return {
+        key: value.clone() if isinstance(value, torch.Tensor) else list(value) for key, value in batch.items()
+    }
+
+
+def pad_last_dim(tensor: torch.Tensor, target_dim: int) -> torch.Tensor:
+    if tensor.shape[-1] > target_dim:
+        raise ValueError(f"Cannot pad last dimension {tensor.shape[-1]} down to {target_dim}")
+    return F.pad(tensor, (0, target_dim - tensor.shape[-1]))
+
+
+def mean_std_normalize(tensor: torch.Tensor, stats: dict[str, torch.Tensor]) -> torch.Tensor:
+    mean = stats["mean"].to(device=tensor.device, dtype=tensor.dtype)
+    std = stats["std"].to(device=tensor.device, dtype=tensor.dtype)
+    return (tensor - mean) / (std + 1e-8)
+
+
+def quantile_normalize(tensor: torch.Tensor, stats: dict[str, torch.Tensor]) -> torch.Tensor:
+    q01 = stats["q01"].to(device=tensor.device, dtype=tensor.dtype)
+    q99 = stats["q99"].to(device=tensor.device, dtype=tensor.dtype)
+    denom = torch.where(q99 == q01, torch.full_like(q99, 1e-8), q99 - q01)
+    return 2.0 * (tensor - q01) / denom - 1.0
+
+
+def openpi_model_state_from_raw(
+    batch: dict[str, torch.Tensor],
+    *,
+    action_dim: int,
+    dataset_stats: dict[str, dict[str, torch.Tensor]],
+    pi05: bool,
+) -> torch.Tensor:
+    state = batch[OBS_STATE].to(dtype=torch.float32)
+    if pi05:
+        state = quantile_normalize(state, dataset_stats[OBS_STATE])
+    else:
+        state = mean_std_normalize(state, dataset_stats[OBS_STATE])
+    return pad_last_dim(state, action_dim)
+
+
+def openpi_model_actions_from_raw(
+    batch: dict[str, torch.Tensor],
+    *,
+    action_dim: int,
+    dataset_stats: dict[str, dict[str, torch.Tensor]],
+    pi05: bool,
+) -> torch.Tensor:
+    actions = batch[ACTION].to(dtype=torch.float32)
+    if pi05:
+        actions = quantile_normalize(actions, dataset_stats[ACTION])
+    else:
+        actions = mean_std_normalize(actions, dataset_stats[ACTION])
+    return pad_last_dim(actions, action_dim)
+
+
+def _tasks_from_raw(batch: dict, batch_size: int) -> list[str]:
+    tasks = batch.get("task")
+    if tasks is None:
+        raise ValueError("The parity batch must include a task prompt.")
+    if isinstance(tasks, str):
+        return [tasks] * batch_size
+    if len(tasks) == 1:
+        return [tasks[0]] * batch_size
+    if len(tasks) != batch_size:
+        raise ValueError(f"Expected {batch_size} task prompts, got {len(tasks)}")
+    return list(tasks)
+
+
+def _format_pi0_prompts(tasks: list[str]) -> list[str]:
+    return [f"{task.strip().replace('_', ' ').replace(chr(10), ' ')}\n" for task in tasks]
+
+
+def _format_pi05_prompts(tasks: list[str], normalized_state: torch.Tensor) -> list[str]:
+    state_np = normalized_state.detach().cpu().numpy()
+    discretized_states = np.digitize(state_np, bins=np.linspace(-1, 1, 256 + 1)[:-1]) - 1
+    prompts = []
+    for task, state in zip(tasks, discretized_states, strict=True):
+        cleaned_text = task.strip().replace("_", " ").replace("\n", " ")
+        state_str = " ".join(map(str, state))
+        prompts.append(f"Task: {cleaned_text}, State: {state_str};\nAction: ")
+    return prompts
+
+
+def _tokenize_prompts(prompts: list[str], *, max_token_len: int, device: torch.device | str):
+    tokenized = paligemma_tokenizer()(
+        prompts,
+        padding="max_length",
+        padding_side="right",
+        truncation=True,
+        max_length=max_token_len,
+        return_tensors="pt",
+    )
+    tokens = tokenized["input_ids"].to(device)
+    masks = tokenized["attention_mask"].to(device=device, dtype=torch.bool)
+    return tokens, masks
+
+
+def make_openpi_observation_from_raw(
+    batch: dict[str, torch.Tensor],
+    *,
+    action_dim: int,
+    max_token_len: int,
+    dataset_stats: dict[str, dict[str, torch.Tensor]],
+    pi05: bool,
+) -> OpenPIObservation:
+    batch_size = batch[OBS_STATE].shape[0]
+    device = batch[OBS_STATE].device
+    state = openpi_model_state_from_raw(
+        batch,
+        action_dim=action_dim,
+        dataset_stats=dataset_stats,
+        pi05=pi05,
+    )
+
+    tasks = _tasks_from_raw(batch, batch_size)
+    prompts = _format_pi05_prompts(tasks, state) if pi05 else _format_pi0_prompts(tasks)
+    tokens, masks = _tokenize_prompts(prompts, max_token_len=max_token_len, device=device)
+
+    images = {
+        key: batch[f"observation.images.{key}"].to(device=device, dtype=torch.float32) * 2.0 - 1.0
+        for key in IMAGE_KEYS
+    }
+    image_masks = {key: torch.ones(batch_size, dtype=torch.bool, device=device) for key in IMAGE_KEYS}
+
+    return OpenPIObservation(
+        state=state,
+        images=images,
+        image_masks=image_masks,
+        tokenized_prompt=tokens,
+        tokenized_prompt_mask=masks,
+        token_ar_mask=torch.zeros_like(tokens, dtype=torch.int32),
+        token_loss_mask=torch.ones_like(masks, dtype=torch.bool),
+    )
+
+
+def assert_processor_inputs_match_lerobot(
+    lerobot_policy,
+    lerobot_batch: dict[str, torch.Tensor],
+    openpi_observation: OpenPIObservation,
+    *,
+    compare_state: bool,
+):
+    openpi_processed = openpi_preprocessing.preprocess_observation_pytorch(openpi_observation, train=False)
+    lerobot_images, lerobot_image_masks = lerobot_policy._preprocess_images(lerobot_batch)
+
+    # Token IDs, token masks, images, image masks, and PI0 state are intentionally built from the same
+    # raw batch through independent LeRobot/OpenPI-style processor logic. They must be bitwise equal.
+    torch.testing.assert_close(
+        openpi_observation.tokenized_prompt, lerobot_batch[OBS_LANGUAGE_TOKENS], rtol=0, atol=0
+    )
+    torch.testing.assert_close(
+        openpi_observation.tokenized_prompt_mask,
+        lerobot_batch[OBS_LANGUAGE_ATTENTION_MASK],
+        rtol=0,
+        atol=0,
+    )
+
+    for openpi_image, lerobot_image in zip(openpi_processed.images.values(), lerobot_images, strict=True):
+        torch.testing.assert_close(openpi_image, lerobot_image, rtol=0, atol=0)
+
+    for openpi_mask, lerobot_mask in zip(
+        openpi_processed.image_masks.values(), lerobot_image_masks, strict=True
+    ):
+        torch.testing.assert_close(openpi_mask, lerobot_mask, rtol=0, atol=0)
+
+    if compare_state:
+        torch.testing.assert_close(
+            openpi_processed.state, lerobot_policy.prepare_state(lerobot_batch), rtol=0, atol=0
+        )
+
+
+def load_openpi_reference_state_dict(repo_id: str) -> dict[str, torch.Tensor]:
+    cache_dir = Path(snapshot_download(repo_id=repo_id, repo_type="model"))
+    return safetensors.torch.load_file(cache_dir / "model.safetensors")
+
+
+def fix_reference_state_dict(state_dict: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
+    fixed_state_dict = dict(state_dict)
+    lm_head_key = "paligemma_with_expert.paligemma.lm_head.weight"
+    embed_tokens_key = "paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight"
+    if lm_head_key in fixed_state_dict and embed_tokens_key not in fixed_state_dict:
+        fixed_state_dict[embed_tokens_key] = fixed_state_dict[lm_head_key].clone()
+    return fixed_state_dict
+
+
+@contextmanager
+def fixed_flow_sampling(model, *, noise: torch.Tensor, time: torch.Tensor) -> Iterator[None]:
+    original_sample_noise = model.sample_noise
+    original_sample_time = model.sample_time
+
+    def sample_noise(shape, device):
+        if tuple(shape) != tuple(noise.shape):
+            raise ValueError(f"Expected noise shape {tuple(noise.shape)}, got {tuple(shape)}")
+        return noise.to(device=device)
+
+    def sample_time(batch_size, device):
+        if batch_size != time.shape[0]:
+            raise ValueError(f"Expected time batch size {time.shape[0]}, got {batch_size}")
+        return time.to(device=device)
+
+    model.sample_noise = sample_noise
+    model.sample_time = sample_time
+    try:
+        yield
+    finally:
+        model.sample_noise = original_sample_noise
+        model.sample_time = original_sample_time
+
+
+@contextmanager
+def deterministic_openpi_forward_preprocess(openpi_policy) -> Iterator[None]:
+    """Disable OpenPI's training-time image augmentation only inside a parity forward block.
+
+    OpenPI's `forward()` calls `_preprocess_observation(..., train=True)`, which can apply stochastic
+    image augmentation. LeRobot's policy forward path does not apply that augmentation, so parity would
+    otherwise compare two different image tensors rather than two model implementations. The context manager
+    keeps the public `openpi_policy.forward(observation, ...)` call while making preprocessing deterministic.
+
+    `yield` marks the body of the caller's `with` block. The `try/finally` restores the original method even
+    if the assertion inside the block fails, so the temporary monkeypatch cannot leak into later tests.
+    """
+
+    original_preprocess_observation = openpi_policy._preprocess_observation
+
+    def preprocess_observation(observation, *, train=True):
+        return original_preprocess_observation(observation, train=False)
+
+    openpi_policy._preprocess_observation = preprocess_observation
+    try:
+        yield
+    finally:
+        openpi_policy._preprocess_observation = original_preprocess_observation
--- a/Show More
+++ b/Show More
				`@@ -0,0 +1 @@`
				`../../../../docs/source/policy_molmoact2_README.md`
				`@@ -0,0 +1 @@`
				`../../../../docs/source/policy_vla_jepa_README.md`
				`@@ -0,0 +1 @@`
				`"""Utilities shared by GR00T policy tests."""`
				`@@ -0,0 +1 @@`
				`"""Lightweight vendored OpenPI PyTorch modules for PI0/PI05 parity tests."""`
				`@@ -0,0 +1 @@`
				`"""Utilities shared by PI0/PI05 policy tests."""`