diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
index 412386e2d..527cb7e63 100644
--- a/docs/source/_toctree.yml
+++ b/docs/source/_toctree.yml
@@ -73,6 +73,8 @@
 - sections:
   - local: sarm
     title: SARM
+  - local: topreward
+    title: TOPReward
   title: "Reward Models"
 - sections:
   - local: inference
diff --git a/docs/source/topreward.mdx b/docs/source/topreward.mdx
new file mode 100644
index 000000000..dc653f096
--- /dev/null
+++ b/docs/source/topreward.mdx
@@ -0,0 +1,191 @@
+# 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, and answers the question "given this video, how strongly does the VLM agree that the instruction is satisfied?".
+
+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 prompt assembly + label-masking logic.
+
+## 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` and `qwen-vl-utils`. 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 that stores the task string                   |
+| `reward_model.max_frames` | `16`                        | Cap on frames per sample (compute_reward only; predict_curves bypasses) |
+| `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.
+- `predict_curves(batch, num_prefixes=None)`: per-frame progress curve in `[0, 1]` (min-max normalised log-probs over prefix lengths). `num_prefixes=None` is fully dense; `num_prefixes=15` matches the upstream sparse-dense default with linear interpolation between anchors.
+
+## 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)
+```
+
+There is no `from_pretrained` weight download for TOPReward itself — the VLM is fetched from the Hub on construction.
+
+### Score a clip + task
+
+```python
+import numpy as np
+from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX
+
+# frames: np.ndarray, shape (T, H, W, C), dtype uint8
+# task: str
+batch = {
+    f"{TOPREWARD_FEATURE_PREFIX}frames": [frames],
+    f"{TOPREWARD_FEATURE_PREFIX}task": [task],
+}
+reward = reward_model.compute_reward(batch)  # tensor of shape (1,)
+```
+
+For a dense per-frame curve over the same clip:
+
+```python
+out = reward_model.predict_curves(batch, num_prefixes=15)
+progress = out["progress"][0].numpy()  # shape (T,), values in [0, 1]
+```
+
+### 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 writes normalised frames + task strings under the `observation.topreward.*` namespace; the model reads them in `compute_reward`.
+
+### Offline dataset labeling
+
+Mirror the SARM / Robometer RA-BC flow — write a `topreward_progress.parquet` once, then reuse it for training (RA-BC) and visualisation (overlay videos):
+
+```bash
+# Fully dense per-frame labeling
+uv run python -m lerobot.rewards.topreward.compute_rabc_weights \
+    --dataset-repo-id lerobot/libero_10_image \
+    --device cuda
+
+# 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-prefixes 15 \
+    --device cuda
+```
+
+Then render the SARM-style 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
+```
+
+## Publishing a named TOPReward configuration
+
+Because TOPReward stores no weights of its own, "publishing a TOPReward model" amounts to writing the LeRobot `config.json` (≈ 1 KB) that pins the VLM id, prompt and reduction:
+
+```python
+from lerobot.rewards.topreward import TOPRewardConfig, TOPRewardModel
+
+cfg = TOPRewardConfig(
+    vlm_name="Qwen/Qwen3-VL-8B-Instruct",
+    reduction="mean",
+    fps=2.0,
+)
+TOPRewardModel(cfg).save_pretrained("./topreward-qwen3vl-8b")
+# Push the directory to the Hub via `huggingface-cli` or `HfApi.upload_folder`.
+```
+
+Reloading restores the same configuration (no weight download for TOPReward itself; the VLM is re-fetched via `vlm_name`):
+
+```python
+reloaded = TOPRewardModel.from_pretrained("./topreward-qwen3vl-8b")
+```
+
+## 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}
+}
+```
diff --git a/pyproject.toml b/pyproject.toml
index ca6248c95..4c62d965c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -209,6 +209,7 @@ 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]"]
+topreward = ["lerobot[transformers-dep]", "lerobot[qwen-vl-utils-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]"]
diff --git a/src/lerobot/rewards/__init__.py b/src/lerobot/rewards/__init__.py
index 203fe2ee1..ae23424e3 100644
--- a/src/lerobot/rewards/__init__.py
+++ b/src/lerobot/rewards/__init__.py
@@ -21,11 +21,13 @@ from .factory import (
 )
 from .pretrained import PreTrainedRewardModel as PreTrainedRewardModel
 from .sarm.configuration_sarm import SARMConfig as SARMConfig
+from .topreward.configuration_topreward import TOPRewardConfig as TOPRewardConfig
 
 __all__ = [
     # Configuration classes
     "RewardClassifierConfig",
     "SARMConfig",
+    "TOPRewardConfig",
     # Base class
     "PreTrainedRewardModel",
     # Factory functions
diff --git a/src/lerobot/rewards/factory.py b/src/lerobot/rewards/factory.py
index c173f44a5..d500cc593 100644
--- a/src/lerobot/rewards/factory.py
+++ b/src/lerobot/rewards/factory.py
@@ -26,6 +26,7 @@ from lerobot.processor import PolicyAction, PolicyProcessorPipeline
 from .classifier.configuration_classifier import RewardClassifierConfig
 from .pretrained import PreTrainedRewardModel
 from .sarm.configuration_sarm import SARMConfig
+from .topreward.configuration_topreward import TOPRewardConfig
 
 
 def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
@@ -37,7 +38,7 @@ def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
 
     Args:
         name: The name of the reward model. Supported names are "reward_classifier",
-              "sarm".
+              "sarm", "topreward".
 
     Returns:
         The reward model class corresponding to the given name.
@@ -53,6 +54,10 @@ def get_reward_model_class(name: str) -> type[PreTrainedRewardModel]:
         from lerobot.rewards.sarm.modeling_sarm import SARMRewardModel
 
         return SARMRewardModel
+    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 +74,7 @@ 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", "topreward".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -82,6 +87,8 @@ def make_reward_model_config(reward_type: str, **kwargs) -> RewardModelConfig:
         return RewardClassifierConfig(**kwargs)
     elif reward_type == "sarm":
         return SARMConfig(**kwargs)
+    elif reward_type == "topreward":
+        return TOPRewardConfig(**kwargs)
     else:
         try:
             config_cls = RewardModelConfig.get_choice_class(reward_type)
@@ -162,6 +169,14 @@ def make_reward_pre_post_processors(
             dataset_meta=kwargs.get("dataset_meta"),
         )
 
+    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:
             processors = _make_processors_from_reward_model_config(
diff --git a/src/lerobot/rewards/topreward/__init__.py b/src/lerobot/rewards/topreward/__init__.py
new file mode 100644
index 000000000..9b03ca866
--- /dev/null
+++ b/src/lerobot/rewards/topreward/__init__.py
@@ -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"]
diff --git a/src/lerobot/rewards/topreward/compute_rabc_weights.py b/src/lerobot/rewards/topreward/compute_rabc_weights.py
new file mode 100644
index 000000000..1c67caa21
--- /dev/null
+++ b/src/lerobot/rewards/topreward/compute_rabc_weights.py
@@ -0,0 +1,395 @@
+#!/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.
+
+This mirrors :mod:`lerobot.rewards.sarm.compute_rabc_weights` (and the
+ROBOMETER equivalent): it walks every episode in a dataset, runs the
+TOPReward zero-shot reward model, and writes a parquet file with one row
+per frame. The output uses the same schema SARM produces so existing
+consumers — :class:`lerobot.rewards.sarm.rabc.RABCWeights` (which reads
+``progress_sparse``) and the SARM-style overlay script in
+``examples/dataset/create_progress_videos.py`` — work without modification.
+
+TOPReward is zero-shot: there is no fine-tuned checkpoint to load. The
+``--reward-model-path`` argument is therefore optional and only used when
+you want to load a TOPReward LeRobot config (e.g. one published on the Hub
+that pins ``vlm_name`` and prompt knobs). Otherwise the default
+:class:`TOPRewardConfig` is used, which points at
+``Qwen/Qwen3-VL-8B-Instruct`` — the VLM is re-downloaded from the HF Hub
+on every run unless cached.
+
+Parquet schema:
+    +--------------------+---------+----------------------------------------+
+    | column             | dtype   | meaning                                |
+    +====================+=========+========================================+
+    | ``index``          | int64   | global frame index                     |
+    | ``episode_index``  | int64   | episode id                             |
+    | ``frame_index``    | int64   | local within-episode index             |
+    | ``progress_sparse``| float32 | per-frame TOPReward progress in [0, 1] |
+    |                    |         | (RA-BC + overlay read this column)     |
+    +--------------------+---------+----------------------------------------+
+
+Usage:
+    # Full computation (one VLM forward per frame, slowest but most accurate)
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image
+
+    # Sparse-dense mode: 15 anchor prefixes per episode, interpolated to
+    # per-frame resolution. Matches upstream TOPReward ``num_samples=15``.
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image \\
+        --num-prefixes 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
+
+The output is written to the dataset's local cache directory as
+``topreward_progress.parquet`` (or to ``--output-path`` if provided).
+"""
+
+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 TOPREWARD_FEATURE_PREFIX
+
+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 _frames_to_uint8_hwc(video: torch.Tensor) -> np.ndarray:
+    """Convert a ``(T, C, H, W)`` or ``(T, H, W, C)`` tensor to ``(T, H, W, C) uint8``.
+
+    Inlined here (rather than reusing the processor) so the labeling script
+    can side-step the ``max_frames`` tail-crop and feed full trajectories
+    to :meth:`TOPRewardModel.predict_curves`.
+    """
+    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 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_prefixes: int | None = None,
+    fps: float | None = None,
+    reduction: str | None = None,
+    use_video_description: bool = False,
+) -> Path:
+    """Run TOPReward over a dataset and write per-frame progress.
+
+    Args:
+        dataset_repo_id: Hugging Face dataset repo id or local path.
+        reward_model_path: Optional TOPReward LeRobot config repo / dir to
+            load (a tiny ``config.json``). When ``None`` (default), a
+            fresh :class:`TOPRewardConfig` is constructed from the CLI
+            overrides.
+        vlm_name: Override the VLM backbone (HF Hub id).
+        output_path: Where to write the parquet. Defaults to
+            ``<dataset_root>/topreward_progress.parquet``.
+        device: Device for the VLM.
+        num_prefixes: Number of evenly-spaced anchor prefixes per episode.
+            ``None`` (default) = fully dense (one VLM forward per frame).
+            Set to ``15`` to match upstream TOPReward ``num_samples=15``.
+        fps: Override the config's ``fps``.
+        reduction: Override the config's ``reduction`` (``"mean"`` / ``"sum"``).
+        use_video_description: Override the config's ``use_video_description``.
+
+    Returns:
+        Path to the written parquet file.
+    """
+    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
+        # Apply CLI overrides on top of the loaded config.
+        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}")
+            # vlm_name affects the loaded weights; reload from scratch.
+            config.vlm_name = vlm_name
+            config.device = device
+            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
+        if reduction is not None:
+            config_kwargs["reduction"] = reduction
+        if use_video_description:
+            config_kwargs["use_video_description"] = True
+        config = TOPRewardConfig(**config_kwargs)
+        logging.info(f"Constructing TOPReward with VLM: {config.vlm_name}")
+        model = TOPRewardModel(config)
+
+    model.to(device).eval()
+
+    image_key = config.image_key
+    frames_key = f"{TOPREWARD_FEATURE_PREFIX}frames"
+    task_batch_key = f"{TOPREWARD_FEATURE_PREFIX}task"
+
+    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")
+
+    all_index: list[int] = []
+    all_episode: list[int] = []
+    all_frame: list[int] = []
+    all_progress: list[float] = []
+
+    for episode_idx in tqdm(range(dataset.num_episodes), 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")
+
+        # Read the whole episode into one (N, C, H, W) tensor and convert
+        # to (N, H, W, C) uint8 — same format ``TOPREWARD_FEATURE_PREFIX.frames``
+        # expects. We deliberately bypass the encoder step here so its
+        # ``max_frames`` tail-crop doesn't clip the prefix sweep.
+        ep_video = torch.stack([dataset[ep_start + i][image_key] for i in range(num_frames)])
+        ep_frames_uint8 = _frames_to_uint8_hwc(ep_video)
+
+        batch = {frames_key: [ep_frames_uint8], task_batch_key: [task]}
+        out = model.predict_curves(batch, num_prefixes=num_prefixes)
+        per_frame = out["progress"][0, :num_frames].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(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),
+            # Same column name SARM uses so RABCWeights + the overlay
+            # script read TOPReward's output without per-model branching.
+            "progress_sparse": np.asarray(all_progress, dtype=np.float32),
+        }
+    )
+
+    # Persist provenance metadata: the LeRobot path (if any) and the VLM id.
+    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:
+    # Full RA-BC computation with the default Qwen3-VL-8B-Instruct backbone
+    python -m lerobot.rewards.topreward.compute_rabc_weights \\
+        --dataset-repo-id lerobot/libero_10_image
+
+    # 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-prefixes 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 (repo id or local dir). "
+        "Falls back to a fresh TOPRewardConfig if unset.",
+    )
+    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. Defaults to <dataset_root>/topreward_progress.parquet.",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+        help="Device to use (default: cuda).",
+    )
+    parser.add_argument(
+        "--num-prefixes",
+        type=int,
+        default=None,
+        help="Evenly-spaced anchor prefixes per episode. None = fully dense "
+        "(one VLM forward per frame). 15 matches upstream TOPReward.",
+    )
+    parser.add_argument(
+        "--fps",
+        type=float,
+        default=None,
+        help="Override TOPRewardConfig.fps (frames per second for the Qwen video processor).",
+    )
+    parser.add_argument(
+        "--reduction",
+        type=str,
+        default=None,
+        choices=["mean", "sum"],
+        help="Override TOPRewardConfig.reduction.",
+    )
+    parser.add_argument(
+        "--use-video-description",
+        action="store_true",
+        help="Generate an instruction-agnostic video description and prepend "
+        "it as context before scoring (doubles VLM calls per prefix).",
+    )
+    parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Upload the progress file 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_prefixes=args.num_prefixes,
+        fps=args.fps,
+        reduction=args.reduction,
+        use_video_description=args.use_video_description,
+    )
+
+    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()
diff --git a/src/lerobot/rewards/topreward/configuration_topreward.py b/src/lerobot/rewards/topreward/configuration_topreward.py
new file mode 100644
index 000000000..bb4d1cf35
--- /dev/null
+++ b/src/lerobot/rewards/topreward/configuration_topreward.py
@@ -0,0 +1,157 @@
+# 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
+# computes the log-likelihood of the suffix ``f"{instruction} ... True"``
+# given the video, then reduces those token log-probs to a scalar reward.
+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``).
+        use_video_description: If ``True``, make an extra VLM call to
+            produce an instruction-agnostic video description and prepend
+            it as additional context. Doubles inference cost but avoids
+            circular grounding when the instruction names objects shown
+            in frames.
+        reduction: Reduction over per-token log-probs of the suffix
+            tokens (``"mean"`` or ``"sum"``).
+        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
+    use_video_description: bool = False
+
+    reduction: str = "mean"
+    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.reduction not in {"mean", "sum"}:
+            raise ValueError(f"reduction must be 'mean' or 'sum', got {self.reduction!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.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}")
diff --git a/src/lerobot/rewards/topreward/modeling_topreward.py b/src/lerobot/rewards/topreward/modeling_topreward.py
new file mode 100644
index 000000000..c4e707426
--- /dev/null
+++ b/src/lerobot/rewards/topreward/modeling_topreward.py
@@ -0,0 +1,563 @@
+# 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. Build a chat-style prompt:
+   ``[video(frames, fps), text=prompt_prefix, text="{instruction} ... True"]``
+2. Forward the full token sequence through the VLM.
+3. Mask all but the final token with ``-100`` (``prompt_length = input_len - 1``,
+   mirrored from upstream). After the standard causal-LM next-token shift, this
+   isolates the single position where the model predicts the literal ``"True"``
+   that ends the prompt — the binary "is the instruction true given the video?"
+   answer.
+4. Read that token's log-probability from the logits and reduce it (mean or sum
+   — equivalent for a single token, kept for API parity with upstream) into a
+   scalar reward.
+
+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, cast
+
+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 PIL import Image
+from torch import Tensor
+
+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
+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 AutoProcessor, Qwen3VLForConditionalGeneration
+else:
+    AutoProcessor = None  # type: ignore[assignment]
+    Qwen3VLForConditionalGeneration = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T", bound="TOPRewardModel")
+
+_TRUE_ANSWER = "True"
+
+
+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}")
+
+
+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 minmax_normalize_rewards(rewards: list[float] | np.ndarray) -> np.ndarray:
+    """Min-max normalise raw log-prob rewards into ``[0, 1]``.
+
+    Matches upstream ``QwenClient.normalize_rewards(rewards, method="minmax")``:
+    a single-element input maps to ``[1.0]`` (no information to scale), and a
+    flat input (``max == min``) maps to all-ones.
+    """
+    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)
+
+
+class TOPRewardModel(PreTrainedRewardModel):
+    """TOPReward zero-shot reward model."""
+
+    name = "topreward"
+    config_class = TOPRewardConfig
+
+    def __init__(self, config: TOPRewardConfig) -> None:
+        require_package("transformers", extra="topreward")
+        require_package("qwen-vl-utils", extra="topreward", import_name="qwen_vl_utils")
+        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
+
+        # TOPReward is zero-shot: load the VLM as-is from the Hub. No
+        # weights of our own, no embedding resize, no head wiring.
+        self.model = Qwen3VLForConditionalGeneration.from_pretrained(config.vlm_name, **model_kwargs)
+        self.processor = AutoProcessor.from_pretrained(config.vlm_name, trust_remote_code=True)
+
+    def compute_reward(self, batch: dict[str, Any]) -> Tensor:
+        """Return one log-prob reward per sample in the batch.
+
+        Expects a batch produced by :class:`TOPRewardEncoderProcessorStep`:
+        ``observation[f"{TOPREWARD_FEATURE_PREFIX}frames"]`` is a list of
+        ``(T, H, W, C) uint8`` numpy arrays (one per sample) and
+        ``observation[f"{TOPREWARD_FEATURE_PREFIX}task"]`` is a list of
+        task strings of the same length.
+        """
+        frames_per_sample, tasks = self._unpack_batch(batch)
+        rewards = [
+            self._compute_log_prob_reward(frames, task)
+            for frames, task in zip(frames_per_sample, tasks, strict=True)
+        ]
+        out = torch.as_tensor(rewards, dtype=torch.float32)
+        if np.isfinite(self.config.success_threshold):
+            out = (out > self.config.success_threshold).float()
+        return out.to(self.config.device or "cpu")
+
+    @torch.no_grad()
+    def predict_curves(
+        self,
+        batch: dict[str, Any],
+        *,
+        num_prefixes: int | None = None,
+    ) -> dict[str, Tensor]:
+        """Per-sample dense progress curves over prefixes ``[0, t]``.
+
+        Mirrors upstream ``compute_instruction_rewards_for_prefixes``: for
+        each sample we run one VLM forward per prefix length and read the
+        log-prob reward at that prefix. Raw log-probs are then min-max
+        normalised per-trajectory to ``[0, 1]``. Because trajectories
+        within a batch can have different lengths, the returned
+        ``progress`` tensor is right-padded with ``NaN`` to the longest
+        trajectory in the batch.
+
+        Args:
+            batch: Same input as :meth:`compute_reward`.
+            num_prefixes: How many evenly-spaced prefix lengths to score
+                per trajectory. ``None`` (default) uses every prefix
+                length ``[1, N]`` → fully dense, ``N`` VLM forwards per
+                trajectory. Pass a smaller integer (e.g. ``15``, the
+                upstream default) for sparse-dense scoring with linear
+                interpolation between anchors.
+
+        Returns:
+            Dict with one float32 CPU tensor:
+
+            - ``progress``: ``(B, T_max)`` — per-frame progress in
+              ``[0, 1]`` (min-max normalised log-prob curve), padded with
+              ``NaN``.
+        """
+        if num_prefixes is not None and num_prefixes < 1:
+            raise ValueError(f"num_prefixes must be >= 1 or None, got {num_prefixes}")
+
+        frames_per_sample, tasks = self._unpack_batch(batch)
+        curves: list[np.ndarray] = []
+        max_len = 0
+        for frames, task in zip(frames_per_sample, tasks, strict=True):
+            num_frames = int(frames.shape[0])
+            if num_frames == 0:
+                curves.append(np.zeros(0, dtype=np.float32))
+                continue
+
+            if num_prefixes is None or num_prefixes >= num_frames:
+                anchor_lengths = np.arange(1, num_frames + 1, dtype=np.int64)
+            else:
+                # Match upstream: linspace from 1 to N, dedupe (rounding
+                # collisions for short trajectories), sort ascending.
+                anchor_lengths = np.unique(np.linspace(1, num_frames, num_prefixes).round().astype(np.int64))
+
+            raw_rewards = [self._compute_log_prob_reward(frames[:length], task) for length in anchor_lengths]
+            normalized_at_anchors = minmax_normalize_rewards(raw_rewards)
+
+            # Linear interpolation back to per-frame resolution when
+            # `num_prefixes < num_frames`.
+            if anchor_lengths.shape[0] == num_frames:
+                per_frame = normalized_at_anchors
+            else:
+                per_frame = np.interp(
+                    np.arange(1, num_frames + 1, dtype=np.float64),
+                    anchor_lengths.astype(np.float64),
+                    normalized_at_anchors.astype(np.float64),
+                ).astype(np.float32)
+
+            curves.append(per_frame)
+            max_len = max(max_len, num_frames)
+
+        padded = np.full((len(curves), max_len), np.nan, dtype=np.float32)
+        for i, curve in enumerate(curves):
+            padded[i, : curve.shape[0]] = curve
+        return {"progress": torch.from_numpy(padded)}
+
+    # ------------------------------------------------------------------
+    # Save / load — VLM weights are not stored in our checkpoint
+    # ------------------------------------------------------------------
+
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Save ``config.json`` only.
+
+        TOPReward has no fine-tuned weights of its own — the VLM is
+        identified by :attr:`TOPRewardConfig.vlm_name` and lives on the
+        Hugging Face Hub under that id. Writing the VLM into a
+        ``model.safetensors`` here would just duplicate ~16 GB of Qwen
+        weights under our org for no benefit.
+        """
+        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,  # accepted for API parity; unused
+        **kwargs: Any,
+    ) -> T:
+        """Load a TOPReward configuration and instantiate the wrapped VLM.
+
+        Two modes:
+
+        - Local directory containing ``config.json``: read the config and
+          rebuild the model. The VLM is re-fetched from the Hub via
+          :attr:`TOPRewardConfig.vlm_name`.
+        - HF Hub repo id: download just ``config.json``, same as above.
+        """
+        del strict  # TOPReward has no weights of its own to (strictly) load.
+        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):
+            # Validate that the remote repo at least contains a TOPReward config.json
+            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.
+
+        Skips the safetensors upload — the wrapped VLM is identified by
+        ``vlm_name`` and we never modify it.
+        """
+        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}")
+
+    def _unpack_batch(self, batch: dict[str, Any]) -> tuple[list[np.ndarray], list[str]]:
+        frames_key = f"{TOPREWARD_FEATURE_PREFIX}frames"
+        task_key = f"{TOPREWARD_FEATURE_PREFIX}task"
+        if frames_key not in batch or task_key not in batch:
+            raise KeyError(
+                "TOPReward batch missing pre-encoded inputs (expected "
+                f"`{frames_key}` and `{task_key}`). Make sure the "
+                "TOPRewardEncoderProcessorStep ran before `compute_reward`."
+            )
+        frames_per_sample = list(batch[frames_key])
+        tasks = list(batch[task_key])
+        if len(frames_per_sample) != len(tasks):
+            raise ValueError(
+                f"frames batch size ({len(frames_per_sample)}) does not match task batch size ({len(tasks)})"
+            )
+        return frames_per_sample, tasks
+
+    @torch.no_grad()
+    def _compute_log_prob_reward(self, frames: np.ndarray, instruction: str) -> float:
+        """Compute the log-likelihood of the final answer token given the prompt.
+
+        Port of ``QwenClient.compute_instruction_reward`` (the upstream
+        TOPReward implementation), stripped of the
+        :class:`InstructionRewardResult` metadata wrapper we don't need.
+        Returns ``log P(final_token | video + prompt + instruction)`` — by
+        default the final token is the literal ``"True"`` that closes the
+        suffix template, which is the binary "is the instruction satisfied"
+        signal the paper describes.
+        """
+        device = next(self.model.parameters()).device
+        pil_frames = _frames_to_pil(frames)
+
+        if self.config.use_video_description:
+            description = self._generate_object_state_reasoning(pil_frames)
+            prompt_text = (
+                f"{description} Therefore given the above description and the "
+                "video, the video shows a robot manipulation trajectory that "
+                "**completes** the following instruction: "
+            )
+        else:
+            prompt_text = self.config.prompt_prefix
+
+        eos_token = self.processor.tokenizer.eos_token
+        instruction_suffix = self.config.prompt_suffix_template.format(instruction=instruction)
+
+        # Two prompt assembly modes match the upstream:
+        #
+        # - ``add_chat_template=True``: wrap the FULL prompt (including
+        #   instruction) with the chat template, then append the literal
+        #   ``"True"`` token outside the template.
+        # - ``add_chat_template=False``: apply the chat template to the
+        #   video+prefix only (no generation prompt), strip the trailing
+        #   EOS, then concatenate the literal instruction suffix.
+        if self.config.add_chat_template:
+            # Suffix excluding the trailing "True" — we want "True" to be
+            # the scored token, not part of the template's user turn.
+            suffix_for_template = instruction_suffix.removesuffix(_TRUE_ANSWER).rstrip()
+            templated_messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "video", "video": pil_frames, "fps": self.config.fps},
+                        {"type": "text", "text": f"{prompt_text}{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}"
+            image_inputs, video_inputs = self._process_vision_info(templated_messages)
+        else:
+            user_messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "video", "video": pil_frames, "fps": self.config.fps},
+                        {"type": "text", "text": prompt_text},
+                    ],
+                }
+            ]
+            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}"
+            image_inputs, video_inputs = self._process_vision_info(user_messages)
+
+        inputs = self.processor(
+            text=[full_text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(device)
+
+        input_len = int(inputs["input_ids"].shape[-1])
+        if input_len > self.config.max_input_length:
+            raise ValueError(
+                f"TOPReward input length {input_len} exceeds max_input_length "
+                f"{self.config.max_input_length}; lower `max_frames` or raise `max_input_length`."
+            )
+
+        labels = inputs["input_ids"].clone()
+        # Mask everything except the very last token. ``prompt_length = input_len - 1``
+        # mirrors upstream ``QwenClient.compute_instruction_reward``; after the
+        # causal-LM next-token shift below this isolates exactly one position —
+        # the prediction of the literal ``"True"`` that closes ``prompt_suffix_template``.
+        # The resulting reward is therefore ``log P("True" | video + prompt + instruction)``.
+        prompt_length = input_len - 1
+        labels[:, :prompt_length] = -100
+        if "attention_mask" in inputs:
+            labels = labels.masked_fill(inputs["attention_mask"] == 0, -100)
+
+        self.model.eval()
+        outputs = self.model(**inputs, labels=labels)
+
+        logits = outputs.logits[:, :-1, :]
+        target_labels = labels[:, 1:]
+        log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
+        mask = target_labels != -100
+        safe_targets = target_labels.masked_fill(~mask, 0)
+        token_log_probs = log_probs.gather(-1, safe_targets.unsqueeze(-1)).squeeze(-1)
+        masked_log_probs = token_log_probs[mask]
+        if masked_log_probs.numel() == 0:
+            raise RuntimeError(
+                "TOPReward could not isolate any suffix tokens to score. Check that "
+                "`prompt_suffix_template` produces at least one tokenised character."
+            )
+
+        # ``mean`` vs ``sum`` are equivalent for a single scored token but the
+        # knob is kept for API parity with upstream (and for forward-compat with
+        # any future variant that scores more than the final answer token).
+        if self.config.reduction == "sum":
+            reward = masked_log_probs.sum().item()
+        else:  # mean
+            reward = masked_log_probs.mean().item()
+        return float(reward)
+
+    @torch.no_grad()
+    def _generate_object_state_reasoning(self, pil_frames: list[Image.Image]) -> str:
+        """Instruction-agnostic trajectory description (upstream
+        ``QwenClient.generate_object_state_reasoning``). Used when
+        :attr:`TOPRewardConfig.use_video_description` is ``True``.
+        """
+        device = next(self.model.parameters()).device
+        user_messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": pil_frames, "fps": self.config.fps},
+                    {
+                        "type": "text",
+                        "text": "Describe the robot manipulation trajectory in this video:",
+                    },
+                ],
+            }
+        ]
+        prompt_chat = self.processor.apply_chat_template(
+            user_messages, tokenize=False, add_generation_prompt=True
+        )
+        image_inputs, video_inputs = self._process_vision_info(user_messages)
+        inputs = self.processor(
+            text=[prompt_chat],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt",
+        ).to(device)
+
+        self.model.eval()
+        output_ids = self.model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=False,
+        )
+        response = self.processor.batch_decode(
+            output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+        prompt_decoded = self.processor.batch_decode(
+            inputs["input_ids"], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+        if response.startswith(prompt_decoded):
+            return response[len(prompt_decoded) :].strip()
+        return response.strip()
+
+    @staticmethod
+    def _process_vision_info(messages: list[dict[str, Any]]) -> tuple[Any, Any]:
+        """Thin wrapper around ``qwen_vl_utils.process_vision_info``.
+
+        Kept as a method so tests can monkey-patch it without depending on
+        the import-time presence of ``qwen_vl_utils``.
+        """
+        from qwen_vl_utils import process_vision_info
+
+        return cast(tuple[Any, Any], process_vision_info(messages))
diff --git a/src/lerobot/rewards/topreward/processor_topreward.py b/src/lerobot/rewards/topreward/processor_topreward.py
new file mode 100644
index 000000000..5c0b77dbf
--- /dev/null
+++ b/src/lerobot/rewards/topreward/processor_topreward.py
@@ -0,0 +1,200 @@
+# 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
+from typing import Any
+
+import numpy as np
+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 TOPRewardConfig
+from lerobot.types import EnvTransition, TransitionKey
+from lerobot.utils.constants import (
+    OBS_IMAGES,
+    OBS_PREFIX,
+    POLICY_POSTPROCESSOR_DEFAULT_NAME,
+    POLICY_PREPROCESSOR_DEFAULT_NAME,
+)
+
+# Namespace for TOPReward's pre-encoded observation tensors written by the
+# processor and consumed by the model. Keys: ``frames`` (one ``(T,H,W,C)``
+# uint8 numpy array per sample) and ``task`` (one string per sample).
+TOPREWARD_FEATURE_PREFIX = f"{OBS_PREFIX}topreward."
+
+
+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.
+
+    Mirrors the Robometer helper: accepts ``(T, C, H, W)`` or ``(T, H, W, C)``
+    layouts, rescales floats in ``[0, 1]`` to ``[0, 255]``, clips values
+    outside the uint8 range and tail-crops to ``max_frames``.
+    """
+    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("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):
+    """Normalise raw frames + task into TOPReward-namespaced observation entries.
+
+    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}frames"]``: list of
+      ``(T, H, W, C) uint8`` numpy arrays, one per sample.
+    - ``observation[f"{TOPREWARD_FEATURE_PREFIX}task"]``: list of strings,
+      one per sample.
+
+    The actual chat-template / tokenisation happens model-side because
+    TOPReward's reward extraction needs the tokenizer to know the
+    prompt/suffix split (label masking on suffix tokens only).
+    """
+
+    image_key: str = OBS_IMAGES + ".top"
+    task_key: str = "task"
+    default_task: str | None = None
+    max_frames: int | None = 16
+
+    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("TOPRewardEncoderProcessorStep requires an observation dict")
+
+        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]
+        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 TOPReward 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,
+        )
+
+        frames_per_sample = [
+            _video_to_numpy(tensor[i], max_frames=self.max_frames) for i in range(batch_size)
+        ]
+
+        new_observation = dict(observation)
+        new_observation[f"{TOPREWARD_FEATURE_PREFIX}frames"] = frames_per_sample
+        new_observation[f"{TOPREWARD_FEATURE_PREFIX}task"] = list(tasks)
+
+        new_transition = transition.copy()
+        new_transition[TransitionKey.OBSERVATION] = new_observation
+        return new_transition
+
+    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 {
+            "image_key": self.image_key,
+            "task_key": self.task_key,
+            "default_task": self.default_task,
+            "max_frames": self.max_frames,
+        }
+
+
+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 normalises frames + task for the TOPReward model.
+
+    The preprocessor adds a batch dimension if needed, runs TOPReward's
+    encoder, and moves any tensor entries to the configured device. The
+    postprocessor is the identity since TOPReward outputs a single reward
+    tensor.
+    """
+    del dataset_stats  # TOPReward's VLM handles its own normalisation.
+
+    preprocessor = PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+        steps=[
+            AddBatchDimensionProcessorStep(),
+            TOPRewardEncoderProcessorStep(
+                image_key=config.image_key,
+                task_key=config.task_key,
+                default_task=config.default_task,
+                max_frames=config.max_frames,
+            ),
+            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
diff --git a/src/lerobot/templates/lerobot_rewardmodel_modelcard_template.md b/src/lerobot/templates/lerobot_rewardmodel_modelcard_template.md
index 933bf7586..11df95de5 100644
--- a/src/lerobot/templates/lerobot_rewardmodel_modelcard_template.md
+++ b/src/lerobot/templates/lerobot_rewardmodel_modelcard_template.md
@@ -13,6 +13,8 @@
 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 == "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 %}
diff --git a/tests/rewards/test_modeling_topreward.py b/tests/rewards/test_modeling_topreward.py
new file mode 100644
index 000000000..eec44ce1f
--- /dev/null
+++ b/tests/rewards/test_modeling_topreward.py
@@ -0,0 +1,421 @@
+# 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.
+
+"""Tests for the TOPReward reward model."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.configs.rewards import RewardModelConfig
+from lerobot.rewards.factory import get_reward_model_class, make_reward_model_config
+from lerobot.rewards.topreward import TOPRewardConfig
+from lerobot.rewards.topreward.modeling_topreward import minmax_normalize_rewards
+from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX
+from tests.utils import skip_if_package_missing
+
+
+class _FakeTokenizer:
+    """Minimal tokenizer surface used by ``TOPRewardModel._compute_log_prob_reward``."""
+
+    eos_token = "<|endoftext|>"
+
+
+class _FakeProcessor:
+    """Stand-in for the Qwen ``AutoProcessor`` returned by ``from_pretrained``."""
+
+    def __init__(self) -> None:
+        self.tokenizer = _FakeTokenizer()
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):  # noqa: ARG003
+        return cls()
+
+
+class _FakeQwenModel(torch.nn.Module):
+    """Stand-in for ``Qwen3VLForConditionalGeneration``.
+
+    Provides the minimum surface ``TOPRewardModel`` touches at construction
+    time (a ``parameters()`` iterator for device inference). Actual
+    ``_compute_log_prob_reward`` calls are bypassed by monkey-patching the
+    method directly in the tests, so we never invoke ``self.model(...)``.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._param = torch.nn.Parameter(torch.zeros(1))
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):  # noqa: ARG003
+        return cls()
+
+
+def _patch_build(monkeypatch) -> None:
+    """Stub out HF AutoX so TOPReward construction is cheap and offline."""
+    from lerobot.rewards.topreward import modeling_topreward
+
+    monkeypatch.setattr(modeling_topreward, "Qwen3VLForConditionalGeneration", _FakeQwenModel)
+    monkeypatch.setattr(modeling_topreward, "AutoProcessor", _FakeProcessor)
+
+
+def _make_batch(frames: list[np.ndarray], tasks: list[str]) -> dict[str, list]:
+    """Build a ``compute_reward``-ready batch using TOPReward's namespaced keys."""
+    return {
+        f"{TOPREWARD_FEATURE_PREFIX}frames": frames,
+        f"{TOPREWARD_FEATURE_PREFIX}task": tasks,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Registry + factory
+# ---------------------------------------------------------------------------
+
+
+def test_topreward_config_registered():
+    assert "topreward" in RewardModelConfig.get_known_choices()
+    assert RewardModelConfig.get_choice_class("topreward") is TOPRewardConfig
+    assert isinstance(make_reward_model_config("topreward", device="cpu"), TOPRewardConfig)
+
+
+def test_topreward_factory_returns_in_tree_class():
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    assert get_reward_model_class("topreward") is TOPRewardModel
+
+
+# ---------------------------------------------------------------------------
+# Config validation
+# ---------------------------------------------------------------------------
+
+
+def test_topreward_config_rejects_bad_reduction():
+    with pytest.raises(ValueError, match="reduction must be"):
+        TOPRewardConfig(device="cpu", reduction="median")
+
+
+def test_topreward_config_rejects_zero_max_frames():
+    with pytest.raises(ValueError, match="max_frames must be >= 1"):
+        TOPRewardConfig(device="cpu", max_frames=0)
+
+
+def test_topreward_config_rejects_non_positive_fps():
+    with pytest.raises(ValueError, match="fps must be > 0"):
+        TOPRewardConfig(device="cpu", fps=0.0)
+
+
+def test_topreward_config_rejects_suffix_without_instruction_placeholder():
+    with pytest.raises(ValueError, match=r"\{instruction\}"):
+        TOPRewardConfig(device="cpu", prompt_suffix_template="no placeholder here")
+
+
+# ---------------------------------------------------------------------------
+# minmax_normalize_rewards — pure math helper
+# ---------------------------------------------------------------------------
+
+
+def test_minmax_normalize_rewards_maps_min_and_max_to_zero_and_one():
+    values = minmax_normalize_rewards([-3.0, -1.0, 0.0, -2.0])
+    assert values.shape == (4,)
+    assert values[0] == pytest.approx(0.0)
+    assert values[2] == pytest.approx(1.0)
+    # Monotonicity preserved within the input range.
+    assert values[3] == pytest.approx(1.0 / 3.0, abs=1e-6)
+
+
+def test_minmax_normalize_rewards_handles_singleton_and_flat_inputs():
+    # Single element -> mapped to 1.0 (no information to scale).
+    assert minmax_normalize_rewards([42.0]).tolist() == [1.0]
+    # All-equal values -> all ones (avoid divide-by-zero).
+    assert minmax_normalize_rewards([0.5, 0.5, 0.5]).tolist() == [1.0, 1.0, 1.0]
+
+
+def test_minmax_normalize_rewards_empty_input_returns_empty_array():
+    out = minmax_normalize_rewards([])
+    assert out.shape == (0,)
+
+
+# ---------------------------------------------------------------------------
+# compute_reward
+# ---------------------------------------------------------------------------
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_compute_reward_returns_one_scalar_per_sample(monkeypatch):
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+
+    captured = []
+
+    def fake_log_prob(self, frames, instruction):  # noqa: ARG002
+        captured.append((frames.shape, instruction))
+        return -1.5
+
+    monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
+
+    frames_a = np.zeros((4, 8, 8, 3), dtype=np.uint8)
+    frames_b = np.zeros((6, 8, 8, 3), dtype=np.uint8)
+    batch = _make_batch([frames_a, frames_b], ["pick the cube", "open the drawer"])
+
+    rewards = model.compute_reward(batch)
+
+    assert rewards.shape == (2,)
+    assert rewards.dtype == torch.float32
+    assert torch.allclose(rewards, torch.tensor([-1.5, -1.5]))
+    # `_compute_log_prob_reward` was called once per sample with the right tasks.
+    assert [task for _, task in captured] == ["pick the cube", "open the drawer"]
+    assert [shape[0] for shape, _ in captured] == [4, 6]
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_compute_reward_applies_success_threshold(monkeypatch):
+    """When ``success_threshold`` is finite, the model returns binary success
+    instead of the raw log-prob — useful as a drop-in success detector."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu", success_threshold=-2.0)
+    model = TOPRewardModel(cfg)
+
+    rewards_in = iter([-1.5, -3.0])  # first above threshold, second below
+    monkeypatch.setattr(
+        TOPRewardModel,
+        "_compute_log_prob_reward",
+        lambda _self, _frames, _instr: next(rewards_in),
+    )
+
+    frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
+    rewards = model.compute_reward(_make_batch(frames, ["task", "task"]))
+
+    assert torch.equal(rewards, torch.tensor([1.0, 0.0]))
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_compute_reward_errors_when_inputs_missing(monkeypatch):
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+
+    with pytest.raises(KeyError, match=r"observation\.topreward\."):
+        model.compute_reward({})
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_compute_reward_errors_when_batch_sizes_mismatch(monkeypatch):
+    """frames and task lists must have matching lengths — a stale processor
+    that produces only one task for a multi-sample batch should surface as
+    an explicit error, not a silent zip truncation."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+    monkeypatch.setattr(
+        TOPRewardModel,
+        "_compute_log_prob_reward",
+        lambda _self, _frames, _instr: 0.0,
+    )
+
+    frames = [np.zeros((2, 8, 8, 3), dtype=np.uint8), np.zeros((2, 8, 8, 3), dtype=np.uint8)]
+    with pytest.raises(ValueError, match="task batch size"):
+        model.compute_reward(_make_batch(frames, ["only one task"]))
+
+
+# ---------------------------------------------------------------------------
+# predict_curves
+# ---------------------------------------------------------------------------
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_predict_curves_runs_one_forward_per_prefix(monkeypatch):
+    """``predict_curves`` must call the VLM once per prefix length per
+    trajectory and write min-max-normalised values back into the curve."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+
+    # Simulate a strictly increasing log-prob curve as the prefix grows.
+    call_log: list[int] = []
+
+    def fake_log_prob(self, frames, instruction):  # noqa: ARG002
+        call_log.append(int(frames.shape[0]))
+        return float(frames.shape[0])  # log-prob = prefix length
+
+    monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
+
+    frames = np.zeros((5, 8, 8, 3), dtype=np.uint8)
+    batch = _make_batch([frames], ["lift the cup"])
+    out = model.predict_curves(batch)
+
+    # One forward per prefix length, in order.
+    assert call_log == [1, 2, 3, 4, 5]
+    # (B, T_max) shape, padded with NaN beyond each trajectory's length.
+    assert out["progress"].shape == (1, 5)
+    # Strictly increasing raw rewards -> min-max-normalised to [0, 1] linearly.
+    expected = torch.tensor([[0.0, 0.25, 0.5, 0.75, 1.0]])
+    assert torch.allclose(out["progress"], expected, atol=1e-6)
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_predict_curves_sparse_dense_interpolates_to_full_resolution(monkeypatch):
+    """With ``num_prefixes < N`` the model should score only the requested
+    number of anchor prefixes and linearly interpolate between them — the
+    upstream sparse-dense pattern (``num_samples=15``)."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+
+    call_log: list[int] = []
+
+    def fake_log_prob(self, frames, instruction):  # noqa: ARG002
+        call_log.append(int(frames.shape[0]))
+        return float(frames.shape[0])
+
+    monkeypatch.setattr(TOPRewardModel, "_compute_log_prob_reward", fake_log_prob)
+
+    frames = np.zeros((9, 8, 8, 3), dtype=np.uint8)
+    out = model.predict_curves(_make_batch([frames], ["lift the cup"]), num_prefixes=3)
+
+    # 3 anchors at linspace(1, 9, 3) -> [1, 5, 9] -> 3 VLM forwards instead of 9.
+    assert call_log == [1, 5, 9]
+    # Returned curve is full resolution (9 frames) and monotone in [0, 1].
+    assert out["progress"].shape == (1, 9)
+    curve = out["progress"][0].numpy()
+    assert curve[0] == pytest.approx(0.0)
+    assert curve[-1] == pytest.approx(1.0)
+    assert np.all(np.diff(curve) >= 0)
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_predict_curves_rejects_invalid_num_prefixes(monkeypatch):
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    model = TOPRewardModel(TOPRewardConfig(device="cpu"))
+    batch = _make_batch([np.zeros((3, 8, 8, 3), dtype=np.uint8)], ["task"])
+    with pytest.raises(ValueError, match="num_prefixes must be"):
+        model.predict_curves(batch, num_prefixes=0)
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_predict_curves_right_pads_with_nan_for_variable_lengths(monkeypatch):
+    """Trajectories of different lengths in the same batch are right-padded
+    with ``NaN`` so the output is a regular ``(B, T_max)`` tensor."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+    monkeypatch.setattr(
+        TOPRewardModel,
+        "_compute_log_prob_reward",
+        lambda _self, frames, _instr: float(frames.shape[0]),
+    )
+
+    frames_short = np.zeros((2, 8, 8, 3), dtype=np.uint8)
+    frames_long = np.zeros((4, 8, 8, 3), dtype=np.uint8)
+    out = model.predict_curves(_make_batch([frames_short, frames_long], ["a", "b"]))
+
+    assert out["progress"].shape == (2, 4)
+    # Trailing entries for the shorter trajectory are NaN.
+    assert torch.isnan(out["progress"][0, 2:]).all()
+    # The longer trajectory has no NaNs.
+    assert not torch.isnan(out["progress"][1]).any()
+
+
+# ---------------------------------------------------------------------------
+# Save / load — config-only checkpoint
+# ---------------------------------------------------------------------------
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_save_pretrained_writes_only_config_json(monkeypatch, tmp_path):
+    """A TOPReward "checkpoint" is just ``config.json``. Writing
+    ``model.safetensors`` would only duplicate ~16 GB of Qwen weights for
+    no benefit, so :meth:`_save_pretrained` must skip it entirely.
+    """
+    from huggingface_hub.constants import CONFIG_NAME, SAFETENSORS_SINGLE_FILE
+
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(
+        device="cpu",
+        vlm_name="Qwen/Qwen3-VL-8B-Instruct",
+        reduction="sum",
+        fps=4.0,
+        image_key="observation.images.front",
+    )
+    model = TOPRewardModel(cfg)
+    model.save_pretrained(str(tmp_path))
+
+    assert (tmp_path / CONFIG_NAME).exists()
+    # Zero-shot model: no safetensors written by `_save_pretrained`.
+    assert not (tmp_path / SAFETENSORS_SINGLE_FILE).exists()
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_from_pretrained_local_dir_roundtrips_config(monkeypatch, tmp_path):
+    """Save a TOPRewardConfig locally and reload it — user knobs must survive."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(
+        device="cpu",
+        vlm_name="Qwen/Qwen3-VL-8B-Instruct",
+        reduction="sum",
+        fps=4.0,
+        image_key="observation.images.front",
+        use_video_description=True,
+        add_chat_template=True,
+        success_threshold=-1.5,
+    )
+    TOPRewardModel(cfg).save_pretrained(str(tmp_path))
+
+    reloaded = TOPRewardModel.from_pretrained(str(tmp_path))
+
+    assert isinstance(reloaded.config, TOPRewardConfig)
+    assert reloaded.config.vlm_name == "Qwen/Qwen3-VL-8B-Instruct"
+    assert reloaded.config.reduction == "sum"
+    assert reloaded.config.fps == 4.0
+    assert reloaded.config.image_key == "observation.images.front"
+    assert reloaded.config.use_video_description is True
+    assert reloaded.config.add_chat_template is True
+    assert reloaded.config.success_threshold == -1.5
+
+
+@skip_if_package_missing("transformers")
+def test_topreward_is_not_trainable(monkeypatch):
+    """The whole point of TOPReward is that it is zero-shot.
+    ``is_trainable`` must therefore be ``False`` and ``forward(...)`` must
+    raise the base-class ``NotImplementedError``."""
+    from lerobot.rewards.topreward.modeling_topreward import TOPRewardModel
+
+    _patch_build(monkeypatch)
+    cfg = TOPRewardConfig(device="cpu")
+    model = TOPRewardModel(cfg)
+
+    assert model.is_trainable is False
+    with pytest.raises(NotImplementedError, match="not trainable"):
+        model.forward({"x": torch.zeros(1)})
diff --git a/tests/rewards/test_topreward_processor.py b/tests/rewards/test_topreward_processor.py
new file mode 100644
index 000000000..6b610aedc
--- /dev/null
+++ b/tests/rewards/test_topreward_processor.py
@@ -0,0 +1,253 @@
+# 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.
+
+"""Tests for TOPReward's pre-processing helpers and encoder step."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.rewards.topreward.processor_topreward import (
+    TOPREWARD_FEATURE_PREFIX,
+    TOPRewardEncoderProcessorStep,
+    _expand_tasks,
+    _video_to_numpy,
+)
+from lerobot.types import TransitionKey
+
+# ---------------------------------------------------------------------------
+# _video_to_numpy — pure (T, C, H, W) -> (T, H, W, C) uint8 conversion
+# ---------------------------------------------------------------------------
+
+
+def test_video_to_numpy_chw_float_is_converted_to_thwc_uint8():
+    video = torch.rand(4, 3, 8, 8)  # (T, C, H, W) floats in [0, 1]
+    array = _video_to_numpy(video, max_frames=None)
+
+    assert array.shape == (4, 8, 8, 3)
+    assert array.dtype == np.uint8
+    assert array.min() >= 0 and array.max() <= 255
+
+
+def test_video_to_numpy_already_thwc_uint8_passes_through():
+    video = torch.randint(0, 256, (3, 8, 8, 3), dtype=torch.uint8)
+    array = _video_to_numpy(video, max_frames=None)
+
+    assert array.shape == (3, 8, 8, 3)
+    assert array.dtype == np.uint8
+
+
+def test_video_to_numpy_max_frames_tail_crops_recent_frames():
+    """``max_frames`` should keep the **last** K frames (most recent)."""
+    video = torch.zeros(10, 3, 4, 4)
+    for t in range(10):
+        video[t] = t / 9.0
+
+    array = _video_to_numpy(video, max_frames=3)
+
+    assert array.shape == (3, 4, 4, 3)
+    assert int(array[0, 0, 0, 0]) == int(round(7 / 9 * 255))
+    assert int(array[-1, 0, 0, 0]) == 255
+
+
+def test_video_to_numpy_rejects_3d_input():
+    with pytest.raises(ValueError, match="Expected channel dim"):
+        _video_to_numpy(torch.zeros(4, 8, 8), max_frames=None)
+
+
+def test_video_to_numpy_floats_above_one_pass_through_without_rescaling():
+    """If ``array.max() > 1`` the helper assumes the tensor is already in the
+    uint8 range; values pass through unchanged (but are still clipped to 255)."""
+    video = torch.full((1, 3, 2, 2), 5.0)
+    array = _video_to_numpy(video, max_frames=None)
+
+    assert array.shape == (1, 2, 2, 3)
+    assert int(array.max()) == 5
+
+
+def test_video_to_numpy_clips_very_large_floats_to_uint8_max():
+    video = torch.full((1, 3, 2, 2), 300.0)
+    array = _video_to_numpy(video, max_frames=None)
+
+    assert int(array.max()) == 255
+
+
+# ---------------------------------------------------------------------------
+# _expand_tasks — string / list / tuple broadcasting to batch size
+# ---------------------------------------------------------------------------
+
+
+def test_expand_tasks_string_is_broadcast_to_batch_size():
+    assert _expand_tasks("pick up", batch_size=3, default=None) == ["pick up", "pick up", "pick up"]
+
+
+def test_expand_tasks_list_of_matching_size_passes_through():
+    assert _expand_tasks(["a", "b", "c"], batch_size=3, default=None) == ["a", "b", "c"]
+
+
+def test_expand_tasks_tuple_is_normalised_to_list():
+    assert _expand_tasks(("a", "b"), batch_size=2, default=None) == ["a", "b"]
+
+
+def test_expand_tasks_single_element_list_is_broadcast():
+    assert _expand_tasks(["only one"], batch_size=3, default=None) == ["only one"] * 3
+
+
+def test_expand_tasks_size_mismatch_raises():
+    with pytest.raises(ValueError, match="Expected 3 tasks"):
+        _expand_tasks(["a", "b"], batch_size=3, default=None)
+
+
+def test_expand_tasks_missing_uses_default():
+    assert _expand_tasks(None, batch_size=2, default="fallback") == ["fallback", "fallback"]
+
+
+def test_expand_tasks_missing_without_default_raises():
+    with pytest.raises(KeyError, match="task description"):
+        _expand_tasks(None, batch_size=1, default=None)
+
+
+def test_expand_tasks_wrong_type_raises():
+    with pytest.raises(TypeError, match="must be a string or list"):
+        _expand_tasks(42, batch_size=1, default=None)
+
+
+# ---------------------------------------------------------------------------
+# Encoder step — input/output shapes + dataclass surface
+# ---------------------------------------------------------------------------
+
+
+def _make_transition(observation: dict, complementary: dict | None = None) -> dict:
+    """Build a tiny ``EnvTransition`` dict for the encoder step."""
+    transition: dict = {TransitionKey.OBSERVATION: observation}
+    if complementary is not None:
+        transition[TransitionKey.COMPLEMENTARY_DATA] = complementary
+    return transition
+
+
+def test_encoder_step_writes_namespaced_frames_and_task():
+    """The encoder step's output is the contract the model reads from. It
+    must populate exactly two namespaced keys: ``frames`` and ``task``."""
+    step = TOPRewardEncoderProcessorStep(
+        image_key="observation.images.top",
+        task_key="task",
+        max_frames=None,
+    )
+
+    frames_batch = torch.zeros(2, 4, 3, 8, 8)  # (B=2, T=4, C, H, W)
+    out = step(
+        _make_transition(
+            observation={"observation.images.top": frames_batch},
+            complementary={"task": ["pick", "place"]},
+        )
+    )
+
+    obs_out = out[TransitionKey.OBSERVATION]
+    frames_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}frames"]
+    tasks_out = obs_out[f"{TOPREWARD_FEATURE_PREFIX}task"]
+
+    assert len(frames_out) == 2
+    assert all(arr.shape == (4, 8, 8, 3) and arr.dtype == np.uint8 for arr in frames_out)
+    assert tasks_out == ["pick", "place"]
+
+
+def test_encoder_step_adds_singleton_time_dim_for_4d_input():
+    """A ``(B, C, H, W)`` observation is the single-frame case; the encoder
+    must unsqueeze the time dim so the model still sees a video."""
+    step = TOPRewardEncoderProcessorStep(image_key="observation.images.top", max_frames=None)
+
+    frames_batch = torch.zeros(1, 3, 8, 8)  # (B=1, C, H, W) — no time dim
+    out = step(
+        _make_transition(
+            observation={"observation.images.top": frames_batch},
+            complementary={"task": "pick"},
+        )
+    )
+
+    frames_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}frames"]
+    assert len(frames_out) == 1
+    assert frames_out[0].shape == (1, 8, 8, 3)  # (T=1, H, W, C)
+
+
+def test_encoder_step_uses_default_task_when_complementary_is_missing():
+    step = TOPRewardEncoderProcessorStep(
+        image_key="observation.images.top",
+        default_task="perform the task",
+    )
+
+    frames_batch = torch.zeros(1, 2, 3, 4, 4)
+    out = step(_make_transition(observation={"observation.images.top": frames_batch}))
+
+    tasks_out = out[TransitionKey.OBSERVATION][f"{TOPREWARD_FEATURE_PREFIX}task"]
+    assert tasks_out == ["perform the task"]
+
+
+def test_encoder_step_rejects_missing_image_key():
+    step = TOPRewardEncoderProcessorStep(image_key="observation.images.top")
+    with pytest.raises(KeyError, match="image key"):
+        step(_make_transition(observation={}, complementary={"task": "pick"}))
+
+
+def test_encoder_step_rejects_non_dict_observation():
+    step = TOPRewardEncoderProcessorStep()
+    with pytest.raises(ValueError, match="observation dict"):
+        step({TransitionKey.OBSERVATION: torch.zeros(1, 3, 8, 8)})
+
+
+def test_encoder_step_rejects_3d_or_6d_input():
+    """The encoder accepts ``(B,C,H,W)`` or ``(B,T,C,H,W)`` only."""
+    step = TOPRewardEncoderProcessorStep(image_key="observation.images.top")
+    with pytest.raises(ValueError, match=r"\(B,C,H,W\)"):
+        step(
+            _make_transition(
+                observation={"observation.images.top": torch.zeros(8, 8, 3)},
+                complementary={"task": "pick"},
+            )
+        )
+
+
+def test_encoder_step_get_config_roundtrips_user_fields():
+    """``get_config`` must serialise every user-tunable field — these are
+    what the processor pipeline saves under ``preprocessor_config.json``."""
+    step = TOPRewardEncoderProcessorStep(
+        image_key="observation.images.cam_top",
+        task_key="task",
+        default_task="do the thing",
+        max_frames=8,
+    )
+
+    assert step.get_config() == {
+        "image_key": "observation.images.cam_top",
+        "task_key": "task",
+        "default_task": "do the thing",
+        "max_frames": 8,
+    }
+
+
+def test_encoder_step_transform_features_is_identity():
+    """The encoder writes plain Python objects (numpy arrays / strings)
+    into ``observation`` at call time but does NOT advertise new typed
+    features at pipeline-build time — the model reads them via the
+    ``TOPREWARD_FEATURE_PREFIX`` namespace, not via the typed feature map.
+    """
+    step = TOPRewardEncoderProcessorStep()
+    features = {
+        PipelineFeatureType.OBSERVATION: {
+            "observation.images.top": PolicyFeature(shape=(3, 224, 224), type=FeatureType.VISUAL),
+        }
+    }
+    assert step.transform_features(features) == features
diff --git a/uv.lock b/uv.lock
index 7092f780a..3da9e9e43 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3009,6 +3009,10 @@ test = [
     { name = "pytest-cov" },
     { name = "pytest-timeout" },
 ]
+topreward = [
+    { name = "qwen-vl-utils" },
+    { name = "transformers" },
+]
 training = [
     { name = "accelerate" },
     { name = "av" },
@@ -3154,6 +3158,7 @@ requires-dist = [
     { name = "lerobot", extras = ["pyzmq-dep"], marker = "extra == 'unitree-g1'" },
     { name = "lerobot", extras = ["qwen-vl-utils-dep"], marker = "extra == 'eo1'" },
     { name = "lerobot", extras = ["qwen-vl-utils-dep"], marker = "extra == 'sarm'" },
+    { name = "lerobot", extras = ["qwen-vl-utils-dep"], marker = "extra == 'topreward'" },
     { name = "lerobot", extras = ["qwen-vl-utils-dep"], marker = "extra == 'wallx'" },
     { name = "lerobot", extras = ["reachy2"], marker = "extra == 'all'" },
     { name = "lerobot", extras = ["rebot"], marker = "extra == 'all'" },
@@ -3177,6 +3182,7 @@ requires-dist = [
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'pi'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'sarm'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'smolvla'" },
+    { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'topreward'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'wallx'" },
     { name = "lerobot", extras = ["transformers-dep"], marker = "extra == 'xvla'" },
     { name = "lerobot", extras = ["video-benchmark"], marker = "extra == 'all'" },
@@ -3244,7 +3250,7 @@ requires-dist = [
     { name = "transformers", marker = "extra == 'transformers-dep'", specifier = ">=5.4.0,<5.6.0" },
     { name = "wandb", marker = "extra == 'training'", specifier = ">=0.24.0,<0.25.0" },
 ]
-provides-extras = ["dataset", "training", "hardware", "viz", "core-scripts", "evaluation", "dataset-viz", "av-dep", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "diffusers-dep", "qwen-vl-utils-dep", "matplotlib-dep", "pyserial-dep", "deepdiff-dep", "pynput-dep", "pyzmq-dep", "motorbridge-dep", "motorbridge-smart-servo-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "rebot", "kinematics", "intelrealsense", "phone", "diffusion", "wallx", "pi", "smolvla", "multi-task-dit", "groot", "sarm", "xvla", "eo1", "hilserl", "async", "peft", "dev", "notebook", "test", "video-benchmark", "aloha", "pusht", "libero", "metaworld", "all"]
+provides-extras = ["dataset", "training", "hardware", "viz", "core-scripts", "evaluation", "dataset-viz", "av-dep", "pygame-dep", "placo-dep", "transformers-dep", "grpcio-dep", "can-dep", "peft-dep", "scipy-dep", "diffusers-dep", "qwen-vl-utils-dep", "matplotlib-dep", "pyserial-dep", "deepdiff-dep", "pynput-dep", "pyzmq-dep", "motorbridge-dep", "motorbridge-smart-servo-dep", "feetech", "dynamixel", "damiao", "robstride", "openarms", "gamepad", "hopejr", "lekiwi", "unitree-g1", "reachy2", "rebot", "kinematics", "intelrealsense", "phone", "diffusion", "wallx", "pi", "smolvla", "multi-task-dit", "groot", "sarm", "topreward", "xvla", "eo1", "hilserl", "async", "peft", "dev", "notebook", "test", "video-benchmark", "aloha", "pusht", "libero", "metaworld", "all"]
 
 [[package]]
 name = "librt"