refactor(processors): use specialized processors where possible

.github/workflows/documentation.yml

@@ -24,24 +24,19 @@ on:
         required: false
         type: string
 
-  # Triggers on pushes to main that touch the docs or the sources the API reference is generated from.
-  # `src/**` is included because the API reference is built from docstrings via `[[autodoc]]`: without it,
-  # published API pages would go stale as soon as a docstring changed.
+  # Triggers the workflow on push events to main for the docs folder
   push:
     branches:
       - main
     paths:
       - "docs/**"
-      - "src/**"
 
-  # Same for pull requests, so a docstring change gets a preview build and a broken `[[autodoc]]` path
-  # fails the PR rather than main.
+  # Triggers the workflow on pull request events targeting main for the docs folder
   pull_request:
     branches:
       - main
     paths:
       - "docs/**"
-      - "src/**"
 
   release:
     types: [published]
@@ -64,21 +59,12 @@ jobs:
     with:
       commit_sha: ${{ github.sha }}
       package: lerobot
-      # doc-builder ships a mock-deps registry entry for lerobot, so the reusable workflow takes its
-      # "light install" path: `pip install ./lerobot --no-deps` plus a handful of real dependencies.
-      # That is not enough to import lerobot — draccus runs `register_subclass` at import time and
-      # `processor/converters.py` calls `functools.singledispatch.register(torch.Tensor)`, neither of
-      # which works against a mock. Install the package for real before the build.
-      pre_command: uv pip install "./lerobot[dataset]"
-      # `--version main` is load-bearing: without `--not_python_module`, doc-builder falls back to
-      # `lerobot.__version__` and only maps that to the default branch when it contains "dev". Our main
-      # branch carries a release version (0.6.2), so omitting this would publish the main docs to
-      # /lerobot/v0.6.2/ instead of /lerobot/main/ and disable notebook building.
       additional_args: >-
+        --not_python_module
         ${{
           (github.event_name == 'release' && format('--version {0}', github.event.release.tag_name)) ||
           (inputs.version != '' && format('--version {0}', inputs.version)) ||
-          '--version main'
+          ''
         }}
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
@@ -97,6 +83,4 @@ jobs:
       commit_sha: ${{ github.event.pull_request.head.sha }}
       pr_number: ${{ github.event.number }}
       package: lerobot
-      # See the comment on build_main_docs. The PR workflow passes its own `--version pr_<n>`, so no
-      # additional_args are needed here.
-      pre_command: uv pip install "./lerobot[dataset]"
+      additional_args: --not_python_module

.github/workflows/quality.yml

@@ -56,41 +56,3 @@ jobs:
         uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd  # v3.0.1
         with:
           extra_args: --all-files --show-diff-on-failure --color=always
-
-  # This job runs the examples in our docstrings and validates the doctest allowlist.
-  # See docs/source/writing_docstrings.mdx for the standard these enforce.
-  doc-checks:
-    name: Run Documentation Checks (Doctests)
-    runs-on: ubuntu-latest
-    env:
-      # Examples that need a physical robot, a serial port or a Hub download are skipped by content.
-      # Everything else has to actually run. See src/lerobot/utils/doctest_utils.py.
-      SKIP_HARDWARE_DOCTEST: "1"
-      SKIP_CUDA_DOCTEST: "1"
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1  # v7.0.1
-        with:
-          persist-credentials: false
-
-      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
-        with:
-          enable-cache: true
-          version: "0.11.30"
-          python-version: "3.12"
-
-      - name: Install dependencies
-        run: uv sync --locked --extra test --extra dataset
-
-      - name: Check the doctest list is sorted and its paths exist
-        run: make check-doctest-list
-
-      - name: Check documented arguments match their signatures
-        run: make check-docstrings
-
-      - name: Check docstring coverage has not regressed
-        run: uv run --with interrogate interrogate --config=pyproject.toml
-
-      - name: Run doctests
-        run: make doctest

.pre-commit-config.yaml

@@ -67,11 +67,7 @@ repos:
         args: [--prose-wrap=preserve]
         # Jinja2 model-card templates use a .md extension but contain {% ... %} /
         # {{ ... }} tags that prettier's Markdown formatter mangles (e.g. table loops).
-        #
-        # docs/source/api/ holds the generated API reference. Its `[[autodoc]]` blocks restrict output
-        # to an indented `- member` list, which prettier reads as a lazy paragraph continuation and
-        # joins onto one line — silently turning a member list into part of the directive.
-        exclude: ^(src/lerobot/templates/.*\.md|docs/source/api/.*\.mdx)$
+        exclude: ^src/lerobot/templates/.*\.md$
 
   ##### Security #####
   - repo: https://github.com/gitleaks/gitleaks
@@ -108,13 +104,8 @@ repos:
   #       args: ["--docstring-style", "google", "-v", "2"]
   #       exclude: ^tests/.*$
 
-  # interrogate runs in CI (quality.yml, doc-checks job) rather than here. Its 1.7.0 release still imports
-  # the deprecated `py` package, which resolves against whatever `py` happens to be importable in
-  # pre-commit's isolated env — on a machine with miniconda on the path that is a stray `py.py` and the
-  # hook dies before it reads any config. The gate is the same either way; the CI step is just reliable.
   # - repo: https://github.com/econchick/interrogate
   #   rev: 1.7.0
   #   hooks:
   #     - id: interrogate
-  #       args: ["--config=pyproject.toml"]
-  #       pass_filenames: false
+  #       args: ["-vv", "--config=pyproject.toml"]

CONTRIBUTING.md

@@ -50,10 +50,6 @@ To run checks manually on all files:
 pre-commit run --all-files
 ```
 
-### Docstrings
-
-The API reference is generated from the docstrings in `src/lerobot/`. If you add or change anything public, follow the [docstring standard](https://huggingface.co/docs/lerobot/writing_docstrings) — the format is parsed by the renderer and checked in CI.
-
 ### Running Tests
 
 We use `pytest`. First, ensure you have test artifacts by installing **git-lfs**:

Makefile

@@ -184,29 +184,3 @@ test-smolvla-ete-eval:
 # backend, so it does not require a real model checkpoint or GPU.
 annotation-e2e:
 	uv run python -m tests.annotations.run_e2e_smoke
-
-# Docstring & doctest checks. See docs/source/writing_docstrings.mdx for the standard these enforce.
-
-# Run the examples in the docstrings listed in utils/documentation_tests.txt. Hardware and GPU examples are
-# skipped by content (see src/lerobot/utils/doctest_utils.py); CI sets both flags.
-doctest:
-	@files=$$(grep -v '^\s*#' utils/documentation_tests.txt | grep -v '^\s*$$'); \
-	if [ -z "$$files" ]; then \
-		echo "utils/documentation_tests.txt lists no files; nothing to run."; \
-	else \
-		SKIP_HARDWARE_DOCTEST=1 uv run pytest --doctest-modules --no-header -q $$files; \
-	fi
-
-check-doctest-list:
-	uv run python utils/check_doctest_list.py
-
-fix-doctest-list:
-	uv run python utils/check_doctest_list.py --fix_and_overwrite
-
-check-docstrings:
-	uv run python utils/check_docstrings.py
-	uv run python utils/check_config_docstrings.py
-
-fix-docstrings:
-	uv run python utils/check_docstrings.py --fix_and_overwrite
-	uv run python utils/check_doctest_list.py --fix_and_overwrite

conftest.py

@@ -1,60 +0,0 @@
-# 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.
-
-"""Root conftest: makes doctest collection use LeRobot's parser.
-
-This only affects `--doctest-modules` runs (see `make doctest`). The test suite itself is configured by
-`tests/conftest.py`.
-"""
-
-import doctest
-
-import _pytest.doctest
-
-from lerobot.utils.doctest_utils import LeRobotDoctestModule, LeRobotDocTestParser
-
-# Lets an example opt out of output comparison with `# doctest: +IGNORE_RESULT`, for calls whose output is
-# a progress bar or otherwise not reproducible.
-IGNORE_RESULT = doctest.register_optionflag("IGNORE_RESULT")
-
-OutputChecker = doctest.OutputChecker
-
-
-class CustomOutputChecker(OutputChecker):
-    """An output checker that honours the `IGNORE_RESULT` flag."""
-
-    def check_output(self, want, got, optionflags):
-        """Return `True` when `IGNORE_RESULT` is set, otherwise defer to stdlib.
-
-        Args:
-            want (`str`):
-                The expected output.
-            got (`str`):
-                The actual output.
-            optionflags (`int`):
-                Bitmask of active doctest option flags.
-
-        Returns:
-            `bool`: Whether the output is considered a match.
-        """
-        if IGNORE_RESULT & optionflags:
-            return True
-        return OutputChecker.check_output(self, want, got, optionflags)
-
-
-# Reassigning these module attributes is how doctest behaviour is customised; mypy sees it as assigning to
-# a type, which is exactly what is intended here.
-doctest.OutputChecker = CustomOutputChecker  # type: ignore[misc]
-_pytest.doctest.DoctestModule = LeRobotDoctestModule
-doctest.DocTestParser = LeRobotDocTestParser  # type: ignore[misc]

docs/source/_toctree.yml

@@ -191,30 +191,6 @@
 - sections:
   - local: contributing
     title: Contribute to LeRobot
-  - local: writing_docstrings
-    title: Writing docstrings
   - local: backwardcomp
     title: Backward compatibility
   title: "About"
-- sections:
-  - local: api/robots
-    title: Robots
-  - local: api/teleoperators
-    title: Teleoperators
-  - local: api/cameras
-    title: Cameras
-  - local: api/motors
-    title: Motors
-  - local: api/datasets
-    title: Datasets
-  - local: api/policies
-    title: Policies
-  - local: api/processor
-    title: Processors
-  - local: api/envs
-    title: Environments
-  - local: api/configs
-    title: Configuration
-  - local: api/rl
-    title: Reinforcement Learning
-  title: "API Reference"

docs/source/action_representations.mdx

@@ -33,7 +33,7 @@ LeRobot provides processor steps for converting between joint and EE spaces usin
 ```python
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 
@@ -44,7 +44,7 @@ kinematics = RobotKinematics(
 )
 
 # Joints → EE (for observations: "where is my gripper?")
-fk_step = ForwardKinematicsJointsToEE(kinematics=kinematics, motor_names=[...])
+fk_step = ForwardKinematicsJointsToEEObservation(kinematics=kinematics, motor_names=[...])
 
 # EE → Joints (for actions: "move my gripper here")
 ik_step = InverseKinematicsEEToJoints(kinematics=kinematics, motor_names=[...])
@@ -197,7 +197,7 @@ Here is how the different processors compose. Each arrow is a processor step, an
 ```
                     ┌─────────────────────────────────────────┐
    Action Space     │   Joint Space  ←──IK──→  EE Space      │
-                    │   ForwardKinematicsJointsToEE           │
+                    │   ForwardKinematicsJointsToEEAction     │
                     │   InverseKinematicsEEToJoints           │
                     └─────────────────────────────────────────┘
 

docs/source/api/cameras.mdx

@@ -1,24 +0,0 @@
-# Cameras
-
-Cameras supply the image observations a policy sees. Every backend — OpenCV, Intel RealSense, Reachy 2 —
-implements the [`Camera`] interface, so swapping hardware does not change the code that reads frames.
-
-See the [Cameras guide](../cameras) for choosing and configuring a camera, and
-[Third-Party Cameras & Sensors](../third_party_sensors) for devices outside the core set.
-
-## Camera
-
-[[autodoc]] lerobot.cameras.Camera
-    - connect
-    - disconnect
-    - read
-    - async_read
-    - find_cameras
-
-## CameraConfig
-
-[[autodoc]] lerobot.cameras.CameraConfig
-
-## make_cameras_from_configs
-
-[[autodoc]] lerobot.cameras.make_cameras_from_configs

docs/source/api/configs.mdx

@@ -1,27 +0,0 @@
-# Configuration
-
-LeRobot configuration is plain dataclasses parsed by [draccus](https://github.com/dlwh/draccus), so every
-field is settable from the CLI. [`TrainPipelineConfig`] is the top-level object for `lerobot-train`.
-
-Polymorphic configs (policies, robots, environments) use `draccus.ChoiceRegistry`: a subclass registers
-itself with `@register_subclass("name")` and is then selectable by that name on the command line.
-
-## TrainPipelineConfig
-
-[[autodoc]] lerobot.configs.train.TrainPipelineConfig
-
-## PreTrainedConfig
-
-[[autodoc]] lerobot.configs.PreTrainedConfig
-
-## DatasetConfig
-
-[[autodoc]] lerobot.configs.DatasetConfig
-
-## EvalConfig
-
-[[autodoc]] lerobot.configs.EvalConfig
-
-## WandBConfig
-
-[[autodoc]] lerobot.configs.WandBConfig

docs/source/api/datasets.mdx

@@ -1,23 +0,0 @@
-# Datasets
-
-[`LeRobotDataset`] is the format every LeRobot script reads and writes. It is episode-aware, decodes video
-observations on the fly, and round-trips to the Hugging Face Hub.
-
-See [Using LeRobotDataset](../lerobot-dataset-v3) for the format and the common operations,
-[Porting Large Datasets](../porting_datasets_v3) for migration, and [Tools](../tools) for the CLI.
-
-## LeRobotDataset
-
-[[autodoc]] lerobot.datasets.LeRobotDataset
-
-## LeRobotDatasetMetadata
-
-[[autodoc]] lerobot.datasets.LeRobotDatasetMetadata
-
-## MultiLeRobotDataset
-
-[[autodoc]] lerobot.datasets.MultiLeRobotDataset
-
-## StreamingLeRobotDataset
-
-[[autodoc]] lerobot.datasets.StreamingLeRobotDataset

docs/source/api/envs.mdx

@@ -1,19 +0,0 @@
-# Environments
-
-Simulation environments are configured through [`EnvConfig`] and built by [`make_env`]. Each subclass
-declares its `gym_kwargs` and how to construct the vectorised environments.
-
-See [Environments from the Hub](../envhub) for using published environments and
-[Adding a New Benchmark](../adding_benchmarks) for contributing one.
-
-## EnvConfig
-
-[[autodoc]] lerobot.envs.EnvConfig
-
-## make_env
-
-[[autodoc]] lerobot.envs.make_env
-
-## make_env_config
-
-[[autodoc]] lerobot.envs.make_env_config

docs/source/api/motors.mdx

@@ -1,23 +0,0 @@
-# Motors
-
-`MotorsBus` is the low-level interface to a chain of servos on a serial bus. Robots use it to read positions
-and write goal positions; you rarely touch it directly unless you are adding hardware.
-
-See [Bring Your Own Hardware](../integrate_hardware) for adding a new bus, and
-[Updating Feetech Firmware](../feetech) and [Damiao Motors and CAN Bus](../damiao) for device-specific notes.
-
-## MotorsBus
-
-[[autodoc]] lerobot.motors.motors_bus.MotorsBus
-
-## Motor
-
-[[autodoc]] lerobot.motors.Motor
-
-## MotorCalibration
-
-[[autodoc]] lerobot.motors.MotorCalibration
-
-## MotorNormMode
-
-[[autodoc]] lerobot.motors.MotorNormMode

docs/source/api/policies.mdx

@@ -1,20 +0,0 @@
-# Policies
-
-Every policy inherits [`PreTrainedPolicy`], which combines a `torch.nn.Module` with the Hub mixin, so any
-policy can be pushed to and loaded from the Hugging Face Hub with the same two calls.
-
-Each policy has its own guide with training recipes and results — [ACT](../act), [SmolVLA](../smolvla),
-[π₀](../pi0), [π₀.₅](../pi05) and the rest are listed under Policies. To add one, see
-[Adding a Policy](../bring_your_own_policies).
-
-## PreTrainedPolicy
-
-[[autodoc]] lerobot.policies.pretrained.PreTrainedPolicy
-
-## PreTrainedConfig
-
-[[autodoc]] lerobot.configs.PreTrainedConfig
-
-## make_policy
-
-[[autodoc]] lerobot.policies.factory.make_policy

docs/source/api/processor.mdx

@@ -1,20 +0,0 @@
-# Processors
-
-Processors are the data transformation layer between a robot, a dataset and a policy. A pipeline is a chain
-of [`ProcessorStep`]s; each step declares how it transforms both the data and the feature contract.
-
-See [Introduction to Robot Processors](../introduction_processors) for the concepts,
-[Implement your own processor](../implement_your_own_processor) to write a step, and
-[Debug your processor pipeline](../debug_processor_pipeline) when a pipeline misbehaves.
-
-## ProcessorStep
-
-[[autodoc]] lerobot.processor.pipeline.ProcessorStep
-
-## DataProcessorPipeline
-
-[[autodoc]] lerobot.processor.pipeline.DataProcessorPipeline
-
-## PolicyProcessorPipeline
-
-[[autodoc]] lerobot.processor.pipeline.PolicyProcessorPipeline

docs/source/api/rl.mdx

@@ -1,87 +0,0 @@
-# Reinforcement Learning
-
-`lerobot.rl` is the distributed actor/learner reinforcement-learning stack behind
-[Train a Robot with RL](../hilserl) (HIL-SERL) and [Train RL in Simulation](../hilserl_sim). Algorithms,
-the replay buffer, data sources, and the trainer are gRPC-free and usable standalone; the actor/learner
-entry points (`actor`, `learner`, `learner_service`) additionally require `pip install 'lerobot[hilserl]'`.
-
-## TrainRLServerPipelineConfig
-
-Top-level configuration for both the `lerobot-actor` and `lerobot-learner` CLIs.
-
-[[autodoc]] lerobot.rl.train_rl.TrainRLServerPipelineConfig
-
-## RLAlgorithm
-
-Abstract base every RL algorithm subclasses.
-
-[[autodoc]] lerobot.rl.algorithms.base.RLAlgorithm
-
-## RLAlgorithmConfig
-
-[[autodoc]] lerobot.rl.algorithms.configs.RLAlgorithmConfig
-
-## TrainingStats
-
-[[autodoc]] lerobot.rl.algorithms.configs.TrainingStats
-
-## make_algorithm
-
-[[autodoc]] lerobot.rl.algorithms.factory.make_algorithm
-
-## make_algorithm_config
-
-[[autodoc]] lerobot.rl.algorithms.factory.make_algorithm_config
-
-## get_algorithm_class
-
-[[autodoc]] lerobot.rl.algorithms.factory.get_algorithm_class
-
-## SAC
-
-[[autodoc]] lerobot.rl.algorithms.sac.sac_algorithm.SACAlgorithm
-    - all
-
-[[autodoc]] lerobot.rl.algorithms.sac.configuration_sac.SACAlgorithmConfig
-
-## ReplayBuffer
-
-In-memory replay buffer of transitions, sampled in batches for off-policy training.
-
-[[autodoc]] lerobot.rl.buffer.ReplayBuffer
-    - all
-
-[[autodoc]] lerobot.rl.buffer.BatchTransition
-
-## DataMixer
-
-Abstract interface for combining online and offline data sources into training batches.
-
-[[autodoc]] lerobot.rl.data_sources.DataMixer
-    - all
-
-[[autodoc]] lerobot.rl.data_sources.OnlineOfflineMixer
-    - all
-
-## RLTrainer
-
-Unified training-step orchestrator: holds the algorithm, a `DataMixer`, and an optional preprocessor.
-
-[[autodoc]] lerobot.rl.trainer.RLTrainer
-    - all
-
-## Actor / learner CLIs
-
-The distributed actor and learner processes communicate over gRPC; see [Train a Robot with
-RL](../hilserl) for the full workflow.
-
-[[autodoc]] lerobot.rl.actor.actor_cli
-
-[[autodoc]] lerobot.rl.learner.train_cli
-
-[[autodoc]] lerobot.rl.learner_service.LearnerService
-    - all
-
-## eval_policy
-
-[[autodoc]] lerobot.rl.eval_policy.eval_policy

docs/source/api/robots.mdx

@@ -1,147 +0,0 @@
-# Robots
-
-Every robot in LeRobot implements the [`Robot`] interface: connect, read an observation, send an action,
-disconnect. Writing a policy or a recording script against that interface means it works with any supported
-arm without change.
-
-This page is the generated reference. For wiring, calibration and first-run instructions, start with the
-hardware guides — [SO-101](../so101), [LeKiwi](../lekiwi), [Hope Jr](../hope_jr), [Reachy 2](../reachy2),
-[OpenArm](../openarm) — or [Imitation Learning for Robots](../il_robots) for the end-to-end workflow. To add
-a robot of your own, see [Bring Your Own Hardware](../integrate_hardware).
-
-## Robot
-
-The abstract base class. Subclasses implement every method below; the contract described here is what a
-policy or recording loop can rely on.
-
-[[autodoc]] lerobot.robots.Robot
-    - connect
-    - disconnect
-    - configure
-    - calibrate
-    - get_observation
-    - send_action
-    - observation_features
-    - action_features
-    - is_connected
-    - is_calibrated
-
-## RobotConfig
-
-[[autodoc]] lerobot.robots.RobotConfig
-
-## make_robot_from_config
-
-[[autodoc]] lerobot.robots.make_robot_from_config
-
-## SO-100 and SO-101 followers
-
-`SO100Follower` and `SO101Follower` are aliases of the same `SOFollower` class; the two arms differ in their
-configuration, not their control code. `SO100FollowerConfig` and `SO101FollowerConfig` are likewise aliases
-of `SOFollowerRobotConfig`.
-
-[[autodoc]] lerobot.robots.so_follower.SOFollower
-    - all
-
-[[autodoc]] lerobot.robots.so_follower.SOFollowerRobotConfig
-
-## BiSOFollower
-
-Two SO followers driven as one bimanual robot.
-
-[[autodoc]] lerobot.robots.bi_so_follower.BiSOFollower
-    - all
-
-[[autodoc]] lerobot.robots.bi_so_follower.BiSOFollowerConfig
-
-## KochFollower
-
-[[autodoc]] lerobot.robots.koch_follower.KochFollower
-    - all
-
-[[autodoc]] lerobot.robots.koch_follower.KochFollowerConfig
-
-## LeKiwi
-
-`LeKiwi` runs on the robot itself. `LeKiwiClient` is the host-side proxy that talks to it over the network
-and presents the same [`Robot`] interface.
-
-[[autodoc]] lerobot.robots.lekiwi.LeKiwi
-    - all
-
-[[autodoc]] lerobot.robots.lekiwi.LeKiwiConfig
-
-[[autodoc]] lerobot.robots.lekiwi.LeKiwiClient
-    - all
-
-[[autodoc]] lerobot.robots.lekiwi.LeKiwiClientConfig
-
-## OpenArmFollower
-
-[[autodoc]] lerobot.robots.openarm_follower.OpenArmFollower
-    - all
-
-[[autodoc]] lerobot.robots.openarm_follower.OpenArmFollowerConfig
-
-## BiOpenArmFollower
-
-[[autodoc]] lerobot.robots.bi_openarm_follower.BiOpenArmFollower
-    - all
-
-[[autodoc]] lerobot.robots.bi_openarm_follower.BiOpenArmFollowerConfig
-
-## OmxFollower
-
-[[autodoc]] lerobot.robots.omx_follower.OmxFollower
-    - all
-
-[[autodoc]] lerobot.robots.omx_follower.OmxFollowerConfig
-
-## Reachy2Robot
-
-[[autodoc]] lerobot.robots.reachy2.Reachy2Robot
-    - all
-
-[[autodoc]] lerobot.robots.reachy2.Reachy2RobotConfig
-
-## UnitreeG1
-
-[[autodoc]] lerobot.robots.unitree_g1.UnitreeG1
-    - all
-
-[[autodoc]] lerobot.robots.unitree_g1.UnitreeG1Config
-
-## Hope Jr
-
-The Hope Jr humanoid is exposed as two independent robots, an arm and a hand.
-
-[[autodoc]] lerobot.robots.hope_jr.HopeJrArm
-    - all
-
-[[autodoc]] lerobot.robots.hope_jr.HopeJrArmConfig
-
-[[autodoc]] lerobot.robots.hope_jr.HopeJrHand
-    - all
-
-[[autodoc]] lerobot.robots.hope_jr.HopeJrHandConfig
-
-## RebotB601Follower
-
-[[autodoc]] lerobot.robots.rebot_b601_follower.RebotB601Follower
-    - all
-
-[[autodoc]] lerobot.robots.rebot_b601_follower.RebotB601FollowerRobotConfig
-
-## BiRebotB601Follower
-
-[[autodoc]] lerobot.robots.bi_rebot_b601_follower.BiRebotB601Follower
-    - all
-
-[[autodoc]] lerobot.robots.bi_rebot_b601_follower.BiRebotB601FollowerConfig
-
-## EarthRoverMiniPlus
-
-[[autodoc]] lerobot.robots.earthrover_mini_plus.EarthRoverMiniPlus
-    - all
-
-[[autodoc]] lerobot.robots.earthrover_mini_plus.EarthRoverMiniPlusConfig

docs/source/api/teleoperators.mdx

@@ -1,30 +0,0 @@
-# Teleoperators
-
-A teleoperator produces actions for a robot to follow — a leader arm, a gamepad, a keyboard, a phone. All of
-them implement the [`Teleoperator`] interface, so a recording script written against it works with any input
-device.
-
-See [Phone teleoperation](../phone_teleop) and [Isaac Teleop](../isaac_teleop) for setup guides, and
-[Imitation Learning for Robots](../il_robots) for the recording workflow.
-
-## Teleoperator
-
-[[autodoc]] lerobot.teleoperators.Teleoperator
-    - connect
-    - disconnect
-    - configure
-    - calibrate
-    - get_action
-    - send_feedback
-    - action_features
-    - feedback_features
-    - is_connected
-    - is_calibrated
-
-## TeleoperatorConfig
-
-[[autodoc]] lerobot.teleoperators.TeleoperatorConfig
-
-## make_teleoperator_from_config
-
-[[autodoc]] lerobot.teleoperators.make_teleoperator_from_config

docs/source/bring_your_own_policies.mdx

@@ -161,16 +161,6 @@ The methods called by the train/eval loops:
 
 Batches are flat dictionaries keyed by the constants in [`lerobot.utils.constants`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/utils/constants.py): `OBS_STATE` (`observation.state.<motor>`), `OBS_IMAGES` (`observation.images.<camera>`), `OBS_LANGUAGE`, `ACTION`, etc. Reuse the constants — don't invent new prefixes.
 
-If your model is large enough to warrant [sharded multi-GPU training](./multi_gpu_training#sharded-training-fsdp), also declare its FSDP wrap units — the repeated block classes sharding operates on:
-
-```python
-class MyPolicy(PreTrainedPolicy):
-    ...
-    _fsdp_wrap_modules = ["MyTransformerBlock"]
-```
-
-With this one declaration, `--parallelism.dp_shard=N` works out of the box for your policy (users can still override it with `--accelerator.fsdp.wrap_modules`). Without any wrap source, sharded runs fail at startup by design.
-
 ### Processor functions
 
 LeRobot uses `PolicyProcessorPipeline`s to normalize inputs and de-normalize outputs around your policy. For a concrete reference, see [`processor_act.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/act/processor_act.py) or [`processor_diffusion.py`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/policies/diffusion/processor_diffusion.py).
@@ -310,7 +300,7 @@ The file names are load-bearing: the factory does lazy imports by name, and the
 Two places need to know about your policy. All by name.
 
 1. **`policies/__init__.py`** — re-export `MyPolicyConfig` and add it to `__all__`. This import is what registers your policy: `@PreTrainedConfig.register_subclass("my_policy")` runs, and from then on the factory resolves everything by convention. **Don't** re-export the modeling class; it loads lazily through the factory (so `import lerobot` stays fast).
-2. **`templates/lerobot_modelcard_template.md` and the root `README.md`** — the template is what the end-of-training publisher renders into the model card of every checkpoint trained with your policy: add a one-line description of your policy in the `model_name` branches, map it in `policy_docs` so cards link to your MDX guide, and optionally add an architecture image to `diagrams`. Then add your policy to the models table in the root `README.md`, under the right category, linking to your doc page.
+2. **`templates/lerobot_modelcard_template.md` and the root `README.md`** — the template is what `push_model_to_hub` renders into the model card of every checkpoint trained with your policy: add a one-line description of your policy in the `model_name` branches, map it in `policy_docs` so cards link to your MDX guide, and optionally add an architecture image to `diagrams`. Then add your policy to the models table in the root `README.md`, under the right category, linking to your doc page.
 
 Mirror an existing policy that's structurally similar to yours; the diff is small.
 
@@ -354,7 +344,7 @@ A new policy is much easier to review — and far more useful — when it ships
 
 **Pick at least one in-tree benchmark.** LeRobot ships sim benchmarks with per-benchmark Docker images (LIBERO, LIBERO-plus, Meta-World, RoboTwin 2.0, RoboCasa365, RoboCerebra, RoboMME, VLABench and more). Pick the one that matches your policy's modality — VLAs usually go to LIBERO or VLABench; image-only BC to LIBERO or Meta-World. The full list lives under [Benchmarks](./libero) in the docs sidebar.
 
-**Push the checkpoint & processors** to the Hub under `lerobot/<policy>_<benchmark>` (or your namespace if you don't have write access; a maintainer can mirror it). The easiest way is training with `--policy.repo_id=<namespace>/<repo>` and `--policy.push_to_hub=true`: `lerobot-train` publishes the model, both processors, and a model card at the end of the run. To publish an existing checkpoint after the fact, upload its `pretrained_model/` directory (e.g. `huggingface-cli upload`), or use `lerobot-convert-dcp --push_to_hub=...` for sharded-format checkpoints.
+**Push the checkpoint & processors** to the Hub under `lerobot/<policy>_<benchmark>` (or your namespace if you don't have write access; a maintainer can mirror it). Use `PreTrainedPolicy.push_model_to_hub` so the repo gets `config.json`, `model.safetensors`, and a model card.
 
 **Report results in your policy's MDX**, with the exact `lerobot-eval` command and hardware so anyone can re-run:
 

docs/source/hilserl.mdx

@@ -145,7 +145,7 @@ The environment processor (`env_processor`) handles incoming observations and en
 1. **VanillaObservationProcessorStep**: Converts raw robot observations into standardized format
 2. **JointVelocityProcessorStep** (optional): Adds joint velocity information to observations
 3. **MotorCurrentProcessorStep** (optional): Adds motor current readings to observations
-4. **ForwardKinematicsJointsToEE** (optional): Computes end-effector pose from joint positions
+4. **ForwardKinematicsJointsToEEObservation** (optional): Computes end-effector pose from joint positions
 5. **ImageCropResizeProcessorStep** (optional): Crops and resizes camera images
 6. **TimeLimitProcessorStep** (optional): Enforces episode time limits
 7. **GripperPenaltyProcessorStep** (optional): Applies penalties for inappropriate gripper usage
@@ -413,7 +413,7 @@ We support using a gamepad or a keyboard or the leader arm of the robot.
 
 HIL-Serl learns actions in the end-effector space of the robot. Therefore, the teleoperation will control the end-effector's x,y,z displacements.
 
-The end-effector transformation is applied by the processor pipeline (`InverseKinematicsRLStep`, `EEBoundsAndSafety`, `EEReferenceAndDelta`, `GripperVelocityToJoint`) configured under `env.processor.inverse_kinematics` (`InverseKinematicsConfig`) and `env.processor.gripper` / `env.processor.max_gripper_pos`. The defaults related to the end-effector space are:
+The end-effector transformation is applied by the processor pipeline (`EEReferenceAndDelta`, `EEBoundsAndSafety`, `GripperVelocityToJoint`, `InverseKinematicsEEToJoints`, `AddIKSolutionStep`) configured under `env.processor.inverse_kinematics` (`InverseKinematicsConfig`) and `env.processor.gripper` / `env.processor.max_gripper_pos`. The defaults related to the end-effector space are:
 
 <!-- prettier-ignore-start -->
 ```python

docs/source/language_and_recipes.mdx

@@ -108,7 +108,6 @@ own binding plus a matching image block, e.g.
 
 ```yaml
 ask_vqa_top:
-  route: vqa
   bindings:
     vqa_query: "emitted_at(t, style=vqa, role=user, camera=observation.images.top)"
     vqa: "emitted_at(t, style=vqa, role=assistant, camera=observation.images.top)"
@@ -128,9 +127,7 @@ ask_vqa_top:
       }
 ```
 
-Add one such sub-recipe per camera the dataset records. The explicit
-`route: vqa` marker makes a matching sparse VQA annotation take precedence
-over normal weighted blend selection; component names are purely descriptive.
+Add one such sub-recipe per camera the dataset records.
 
 ## Layer 3 — training format
 
@@ -144,20 +141,7 @@ sample["target_message_indices"]
 
 The renderer does not apply a tokenizer chat template. Policy processors decide how to serialize the messages for their backbone, which keeps the same dataset usable across SmolVLA, Pi0.5, and any future VLM that expects OpenAI-style chat messages.
 
-## Blends
-
-Blend recipes select one weighted sub-recipe deterministically from the sample index.
-`recipes/subtask_mem.yaml` trains the compact core blend — high-level subtask prediction, low-level execution, and memory. `recipes/subtask_mem_vqa_speech.yaml` is the fuller variant that also adds VQA and spoken interjection responses.
-
-`recipes/subtask_joint.yaml` demonstrates joint sequence training rather than a
-weighted blend. For the same sample, its assistant subtask is supervised with
-text cross-entropy on the `low_level` stream while action prediction remains
-active, matching the joint setup from the π0.5 paper. Enable
-`--policy.joint_subtask_conditioning=true` to use that subtask conditioning at inference.
-
 ## Graceful absence
 
-If both language columns are missing, `None`, or empty, `RenderMessagesStep` uses
-the task string as low-level supervision when available and otherwise leaves the
-sample unchanged. For an annotated sample, if no recipe branch applies and no
-task fallback exists, rendering returns `None`, allowing a loader to retry another sample.
+If both language columns are missing, `None`, or empty, `RenderMessagesStep` is a no-op.
+If an event-scoped branch is selected on a frame without the required event row, rendering returns `None`, allowing a loader to retry another sample.

docs/source/multi_gpu_training.mdx

@@ -1,29 +1,28 @@
 # Multi-GPU Training
 
-LeRobot trains on multiple GPUs through [Hugging Face Accelerate](https://huggingface.co/docs/accelerate). Three data-parallel layouts are supported:
-
-| Layout   | What it does                                                  | Config                                                  |
-| -------- | ------------------------------------------------------------- | ------------------------------------------------------- |
-| **DDP**  | Replicates the full model on every GPU                        | default on any multi-GPU launch                         |
-| **FSDP** | Shards parameters, gradients, and optimizer state across GPUs | `--parallelism.dp_shard=N`                              |
-| **HSDP** | Shards within groups of GPUs, replicates across groups        | `--parallelism.dp_replicate=R --parallelism.dp_shard=S` |
+This guide shows you how to train policies on multiple GPUs using [Hugging Face Accelerate](https://huggingface.co/docs/accelerate).
 
 ## Installation
 
-`accelerate` is included in the `training` extra:
+`accelerate` is included in the `training` extra. Install it with:
 
 ```bash
 pip install 'lerobot[training]'
 ```
 
-## Launching
+## Training with Multiple GPUs
 
-Distributed training can be launched through both `torchrun` and `accelerate launch`. Accelerate is used as a plain launcher: it does not manage the training configuration, and every distributed training setting lives in LeRobot's own config system.
+You can launch training in two ways:
 
-With `torchrun`:
+### Option 1: Without config (specify parameters directly)
+
+You can specify all parameters directly in the command without running `accelerate config`:
 
 ```bash
-torchrun --nproc-per-node=2 $(which lerobot-train) \
+accelerate launch \
+  --multi_gpu \
+  --num_processes=2 \
+  $(which lerobot-train) \
   --dataset.repo_id=${HF_USER}/my_dataset \
   --policy.type=act \
   --policy.repo_id=${HF_USER}/my_trained_policy \
@@ -32,145 +31,150 @@ torchrun --nproc-per-node=2 $(which lerobot-train) \
   --wandb.enable=true
 ```
 
-With `accelerate launch` (as a plain launcher):
+**Key accelerate parameters:**
+
+- `--multi_gpu`: Enable multi-GPU training
+- `--num_processes=2`: Number of GPUs to use
+- `--mixed_precision=fp16`: Use fp16 mixed precision (or `bf16` if supported)
+
+### Option 2: Using accelerate config
+
+If you prefer to save your configuration, you can optionally configure accelerate for your hardware setup by running:
 
 ```bash
+accelerate config
+```
+
+This interactive setup will ask you questions about your training environment (number of GPUs, mixed precision settings, etc.) and saves the configuration for future use. For a simple multi-GPU setup on a single machine, you can use these recommended settings:
+
+- Compute environment: This machine
+- Number of machines: 1
+- Number of processes: (number of GPUs you want to use)
+- GPU ids to use: (leave empty to use all)
+- Mixed precision: fp16 or bf16 (recommended for faster training)
+
+Then launch training with:
+
+```bash
+accelerate launch $(which lerobot-train) \
+  --dataset.repo_id=${HF_USER}/my_dataset \
+  --policy.type=act \
+  --policy.repo_id=${HF_USER}/my_trained_policy \
+  --output_dir=outputs/train/act_multi_gpu \
+  --job_name=act_multi_gpu \
+  --wandb.enable=true
+```
+
+## How It Works
+
+When you launch training with accelerate:
+
+1. **Automatic detection**: LeRobot automatically detects if it's running under accelerate
+2. **Data distribution**: Your batch is automatically split across GPUs
+3. **Gradient synchronization**: Gradients are synchronized across GPUs during backpropagation
+4. **Single process logging**: Only the main process logs to wandb and saves checkpoints
+
+## Learning Rate and Training Steps Scaling
+
+**Important:** LeRobot does **NOT** automatically scale learning rates or training steps based on the number of GPUs. This gives you full control over your training hyperparameters.
+
+### Why No Automatic Scaling?
+
+Many distributed training frameworks automatically scale the learning rate by the number of GPUs (e.g., `lr = base_lr × num_gpus`).
+However, LeRobot keeps the learning rate exactly as you specify it.
+
+### When and How to Scale
+
+If you want to scale your hyperparameters when using multiple GPUs, you should do it manually:
+
+**Learning Rate Scaling:**
+
+```bash
+# Example: 2 GPUs with linear LR scaling
+# Base LR: 1e-4, with 2 GPUs -> 2e-4
 accelerate launch --num_processes=2 $(which lerobot-train) \
-  --dataset.repo_id=${HF_USER}/my_dataset \
-  --policy.type=act \
-  --policy.repo_id=${HF_USER}/my_trained_policy \
-  --output_dir=outputs/train/act_multi_gpu \
-  --job_name=act_multi_gpu \
-  --wandb.enable=true
+  --optimizer.lr=2e-4 \
+  --dataset.repo_id=lerobot/pusht \
+  --policy.type=act
 ```
 
-With no `--parallelism.*` flags, a multi-process launch runs plain DDP. Multi-node runs use the standard `torchrun --nnodes/--node-rank/--rdzv-endpoint` flags (or `accelerate launch --num_machines/--machine_rank/--main_process_ip`).
+**Training Steps Scaling:**
 
-> [!WARNING]
-> Accelerate's YAML config files (`accelerate launch --config_file some.yaml`, `accelerate config`) are not supported. They configure the engine through environment variables, bypassing LeRobot's configuration system, so `train_config.json` would no longer describe the settings a run actually used. `lerobot-train` therefore refuses to start when [accelerate environment variables](https://huggingface.co/docs/accelerate/usage_guides/fsdp) are set. Put the settings in `--parallelism.*` / `--accelerator.*` flags instead, or set `LEROBOT_ALLOW_ACCELERATE_ENV=1` to acknowledge the override and proceed anyway.
-
-## Batch semantics, learning rate, and steps
-
-Each of the `dp_replicate × dp_shard` data-parallel workers loads its own `--batch_size` micro-batch every step, so one training step consumes `batch_size × dp_world_size` samples, and `× gradient_accumulation_steps` of those go into each optimizer update:
-
-```
-effective_batch_size = batch_size × dp_world_size × gradient_accumulation_steps
-```
-
-The training banner prints this factorization at startup. `--steps` counts loop steps (micro-batches per worker), not optimizer updates.
-
-Gradient accumulation is a first-class flag:
+Since the effective batch size `bs` increases with multiple GPUs (batch_size × num_gpus), you may want to reduce the number of training steps proportionally:
 
 ```bash
-torchrun --nproc-per-node=2 $(which lerobot-train) \
-  --batch_size=8 --accelerator.gradient_accumulation.steps=4 ...
+# Example: 2 GPUs with effective batch size 2x larger
+# Original: batch_size=8, steps=100000
+# With 2 GPUs: batch_size=8 (16 in total), steps=50000
+accelerate launch --num_processes=2 $(which lerobot-train) \
+  --batch_size=8 \
+  --steps=50000 \
+  --dataset.repo_id=lerobot/pusht \
+  --policy.type=act
 ```
 
-**LeRobot does not auto-scale the learning rate or the number of steps** when the effective batch size grows. If you scale out and want equivalent training, please adjust manually, e.g. with 2 GPUs: double `--optimizer.lr` (linear scaling), or halve `--steps`.
+## Training Large Models with FSDP
 
-## Sharded training (FSDP)
+DDP replicates the full model on every GPU, so a model that doesn't fit on one GPU won't fit under
+DDP either. For large models, use **FSDP** (Fully Sharded Data Parallel), which shards parameters,
+gradients, and optimizer state across GPUs. See the [accelerate FSDP guide](https://huggingface.co/docs/accelerate/usage_guides/fsdp) for background.
 
-If a model is too large to train with DDP, shard it with FSDP2:
+An example on how to launch LeRobot training with FSDP across 4 GPUs (1 machine):
 
 ```bash
-torchrun --nproc-per-node=4 $(which lerobot-train) \
+accelerate launch --config_file fsdp.yaml --num_processes=4 $(which lerobot-train) \
   --dataset.repo_id=${HF_USER}/my_dataset \
   --policy.type=<your_policy> \
-  --parallelism.dp_shard=4 \
-  --accelerator.mixed_precision=bf16 \
   --output_dir=outputs/train/my_policy_fsdp
 ```
 
-`--parallelism.dp_shard=-1` shards over however many processes the launcher started.
+A minimal `fsdp.yaml` (FSDP1; shards params/grads/optimizer — ZeRO-3-equivalent):
 
-### Wrap units
-
-FSDP shards the model in units (typically the repeated transformer block) and gathers one unit at a time during forward/backward. Policies declare their wrap units via `_fsdp_wrap_modules` on the policy class. For example, ACT declares `["ACTEncoderLayer", "ACTDecoderLayer"]` and FastWAM declares `["MoTLayer"]`. For a policy without a `_fsdp_wrap_modules` declaration, pass one of the flags below. You can specify the module class name explicitly, or use a size-based policy instead:
-
-```bash
---accelerator.fsdp.wrap_modules='["MyTransformerBlock"]'   # explicit class names
---accelerator.fsdp.min_num_params=1000000                  # or: wrap every submodule above 1M params
+```yaml
+compute_environment: LOCAL_MACHINE
+distributed_type: FSDP
+mixed_precision: bf16
+num_machines: 1
+num_processes: 4
+fsdp_config:
+  fsdp_version: 1
+  fsdp_sharding_strategy: FULL_SHARD # params + grads + optimizer (ZeRO-3)
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: <YourTransformerBlock> # repeated block class to shard
+  fsdp_use_orig_params: true # required: optimizer is built pre-prepare
+  fsdp_state_dict_type: FULL_STATE_DICT
 ```
 
-If a policy doesn't declare `_fsdp_wrap_modules` and no `--accelerator.fsdp.wrap_modules` or `--accelerator.fsdp.min_num_params` is passed, the run fails at startup rather than silently wrapping only the root module (which would forfeit all sharding memory savings).
+Set `fsdp_transformer_layer_cls_to_wrap` to your model's repeated transformer-block class so each
+block is sharded as its own unit. `fsdp_use_orig_params: true` is required because LeRobot builds the
+optimizer before `accelerator.prepare()`.
 
-Other sharding settings:
+### FSDP checkpoints
 
-- `--accelerator.fsdp.reshard_after_forward`: whether to keep each unit's parameters resident after forward.
-- `--accelerator.fsdp.cpu_offload`: keeps parameters, gradients and optimizer states on CPU.
-- `--accelerator.fsdp.ignored_modules`: a regex of module paths to keep unsharded.
+LeRobot gathers the full state dict across all ranks and the main process writes it as a single
+`model.safetensors`, loadable as usual with `Policy.from_pretrained(...)`. Two things to look out for:
 
-### HSDP
-
-Hybrid Sharded Data Parallel: parameters, gradients and optimizer states are sharded across `dp_shard` ranks, and that sharding is replicated `dp_replicate` times. Parameter all-gathers and gradient reduce-scatters stay inside a shard group; only the all-reduce that synchronizes the replicas crosses between groups. The two degrees must multiply to the world size:
-
-```bash
-# 16 GPUs = 2 nodes × 8: shard within each node, replicate across nodes
-torchrun --nnodes=2 --nproc-per-node=8 ... $(which lerobot-train) \
-  --parallelism.dp_replicate=2 --parallelism.dp_shard=8 ...
-```
-
-## Checkpoints
-
-Every checkpoint contains a `pretrained_model/` directory and a `training_state/` directory:
-
-```text
-005000/  # the training step at that checkpoint
-├── pretrained_model/
-│   ├── config.json  # policy config
-│   ├── train_config.json  # the full training config
-│   ├── model.safetensors  # full weights (checkpoint_format ∈ {safetensors, safetensors_dcp}, or any non-sharded run)
-│   ├── pytorch_model_fsdp_0/  # DCP weight shards (checkpoint_format ∈ {dcp, safetensors_dcp})
-│   ├── policy_preprocessor.json  # preprocessor config (when the run has a preprocessor)
-│   ├── policy_preprocessor_step_*.safetensors  # state of the stateful preprocessor steps
-│   ├── policy_postprocessor.json  # postprocessor config (when the run has a postprocessor)
-│   └── policy_postprocessor_step_*.safetensors  # state of the stateful postprocessor steps
-└── training_state/
-    ├── training_step.json  # step counter, topology, and batch semantics
-    ├── rng_state.safetensors  # rng states
-    ├── scheduler_state.json  # scheduler state (when the run has a scheduler)
-    ├── optimizer_state.safetensors  # full optimizer state (non-sharded runs)
-    ├── optimizer_param_groups.json  # optimizer param groups (non-sharded runs)
-    └── optimizer_0/  # DCP optimizer shards (sharded runs)
-```
-
-During single-GPU or DDP training, the pipeline serializes each state dict into a single file: `model.safetensors` for the model and `optimizer_state.safetensors` for the optimizer.
-
-During sharded training, the optimizer state is saved as DCP shards under `training_state/optimizer_0/`, and the layout of the model under `pretrained_model/` can be configured through `--checkpoint_format`:
-
-| `--checkpoint_format`     | Weights artifact                             | Use when                                                              |
-| ------------------------- | -------------------------------------------- | --------------------------------------------------------------------- |
-| `safetensors` _(default)_ | single `model.safetensors` only              | you want every checkpoint immediately loadable with `from_pretrained` |
-| `dcp`                     | `pytorch_model_fsdp_0/` shard directory only | gathering the full weights makes saves and resumes too slow           |
-| `safetensors_dcp`         | both                                         | you want fast resume _and_ immediately loadable checkpoints           |
-
-Two things to know about gathered (`safetensors`) checkpoints from sharded runs:
-
-- **They store fp32 weights.** Under mixed precision training, FSDP keeps an fp32 master copy, and the checkpoint saves the master copy to make sure training resumes consistently.
-- The gather is collective (all ranks participate) but only the main process writes.
-
-### Converting DCP checkpoints
-
-`lerobot-convert-dcp` merges a DCP shard directory into a regular `model.safetensors`, offline and without GPUs:
-
-```bash
-lerobot-convert-dcp --checkpoint_dir=outputs/train/run/checkpoints/005000
-lerobot-convert-dcp --checkpoint_dir=... --delete_dcp=true --push_to_hub=${HF_USER}/my_policy
-```
-
-`--push_to_hub` publishes the converted directory as a model repo.
-
-### Resuming
-
-Resume with `--resume=true --config_path=.../checkpoints/last/pretrained_model/train_config.json`. Resuming from a DCP checkpoint supports resharding the model and optimizer state to the _current_ topology, which means you can resume with a different `dp_replicate/dp_shard` split. The data sampler can always resume at the right epoch and offset, but is only _sample-exact_ when the world size and batch size match the original run (a warning is logged otherwise).
-
-> [!NOTE]
-> FSDP checkpoints written by LeRobot 0.6.x and earlier used a different on-disk layout (a gathered full optimizer state) and **cannot be resumed**.
+- **Checkpoints store fp32 weights.** Under mixed precision (`bf16`/`fp16`) FSDP keeps an fp32 master
+  copy, and the checkpoint saves it (~2× the bf16 size on disk) so training can resume consistently
+  with the fp32 optimizer state; `from_pretrained` casts back to the policy dtype on load. FSDP-specific
+  caveat: an fp32 checkpoint is materialized in full precision on the target device _before_ casting,
+  so loading it for inference on a tight GPU can OOM even when the bf16 model would fit — load on CPU
+  first, or cast `model.safetensors` to the deployment dtype offline.
+- The sharded optimizer state is gathered into a full (world-size-independent) state dict and saved
+  alongside the model in the same `optimizer_state.safetensors` / `optimizer_param_groups.json`
+  format as single-GPU training, so **resume-from-checkpoint is supported** with `--resume=true`.
+  Resume reshards both the model and the optimizer state to the _current_ FSDP topology, so you can
+  resume an FSDP checkpoint on a different number of GPUs. Note that the data sampler is only
+  sample-exact when the world size and batch size match the original run (a warning is logged
+  otherwise); the optimizer/model state itself is unaffected.
 
 ## Notes
 
-- Checkpoint saves and end-of-training publishes are collective (every rank enters them). Gathered weights, sidecar files and Hub uploads are written by the main process alone.
-- Metrics are reduced across ranks before logging: losses are averaged, and `samples/s` reports cluster-wide throughput.
-- Learning-rate scheduling is stepped once per training step regardless of the number of processes (`step_scheduler_with_optimizer=False` is baked in).
+- The `--policy.use_amp` flag in `lerobot-train` is only used when **not** running with accelerate. When using accelerate, mixed precision is controlled by accelerate's configuration.
+- Training logs, checkpoints, and hub uploads are only done by the main process to avoid conflicts. Non-main processes have console logging disabled to prevent duplicate output.
+- The effective batch size is `batch_size × num_gpus`. If you use 4 GPUs with `--batch_size=8`, your effective batch size is 32.
+- Learning rate scheduling is handled correctly across multiple processes—LeRobot sets `step_scheduler_with_optimizer=False` to prevent accelerate from adjusting scheduler steps based on the number of processes.
+- When saving or pushing models, LeRobot automatically unwraps the model from accelerate's distributed wrapper to ensure compatibility.
+- WandB integration automatically initializes only on the main process, preventing multiple runs from being created.
 
-For background on the underlying machinery, see the [Accelerate FSDP guide](https://huggingface.co/docs/accelerate/usage_guides/fsdp). To go deeper on large-scale training, check out the [Ultrascale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).
+For more advanced configurations and troubleshooting, see the [Accelerate documentation](https://huggingface.co/docs/accelerate). If you want to learn more about how to train on a large number of GPUs, checkout this awesome guide: [Ultrascale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).

docs/source/phone_teleop.mdx

@@ -187,7 +187,7 @@ We use different IK initial guesses in the kinematic steps. As initial guess eit
 - EEBoundsAndSafety: clamps the EE pose to a workspace and rate‑limits jumps for safety. Also declares `action.ee.*` features.
 - InverseKinematicsEEToJoints: turns an EE pose into joint positions with IK. `initial_guess_current_joints=True` is recommended for closed‑loop control; set `False` for open‑loop replay for stability.
 - GripperVelocityToJoint: integrates a velocity‑like gripper input into an absolute gripper position using the current measured state.
-- ForwardKinematicsJointsToEE: computes `observation.state.ee.*` from observed joints for logging and training on EE state.
+- ForwardKinematicsJointsToEEObservation: computes `observation.state.ee.*` from observed joints for logging and training on EE state.
 
 ### Troubleshooting
 

docs/source/processors_robots_teleop.mdx

@@ -58,7 +58,7 @@ robot_ee_to_joints_processor = RobotProcessorPipeline[RobotAction, RobotAction](
 
 robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation]( # robot obs -> dataset obs
     steps=[
-        ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
+        ForwardKinematicsJointsToEEObservation(kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys()))
     ],
     to_transition=observation_to_transition,
     to_output=transition_to_observation,

docs/source/torch_accelerators.mdx

@@ -40,15 +40,3 @@ lerobot-eval \
 ```
 
 However, in most cases, presence of an accelerator is detected automatically and `policy.device` parameter can be omitted from CLI commands.
-
-## Mixed precision
-
-Training precision is owned by `--accelerator.mixed_precision`, which accepts `no` (default) and `bf16`:
-
-```bash
-lerobot-train \
-    --policy.type=act \
-    --accelerator.mixed_precision=bf16 ...
-```
-
-`bf16` requires an accelerator that supports it.

docs/source/writing_docstrings.mdx

@@ -1,287 +0,0 @@
-# Writing docstrings
-
-LeRobot's API reference is generated directly from the docstrings in `src/lerobot/`. A docstring is not a
-comment — it is the published documentation for that object, and the format below is what the renderer and
-the CI checks parse.
-
-This page is the contract. If you are adding or editing anything public in `src/lerobot/`, follow it.
-
-> [!IMPORTANT]
-> **An undocumented public method is an invisible one.** `[[autodoc]]` silently skips members that have no
-> docstring — no warning, no error, it simply does not appear on the rendered page. Coverage and
-> API-reference completeness are the same problem.
-
-## The format in one example
-
-Google section headers, Hugging Face type formatting. Both, not one or the other.
-
-````python
-def send_action(self, action: RobotAction, rate_hz: float = 30.0) -> RobotAction:
-    """Command the robot to move to a target joint configuration.
-
-    Values are clipped by the configured maximum relative target before reaching the motors, so the
-    returned action may differ from the requested one.
-
-    Args:
-        action (`dict[str, float]`):
-            Target values keyed by motor name, e.g. `{"shoulder_pan.pos": 0.0}`. Keys must match the
-            robot's action features.
-        rate_hz (`float`, *optional*, defaults to `30.0`):
-            Control loop frequency.
-
-    Returns:
-        `dict[str, float]`: The action actually written to the motors after safety clipping.
-
-    Raises:
-        DeviceNotConnectedError: If the robot has not been connected.
-
-    Example:
-        ```python
-        >>> from lerobot.robots.so_follower import SO101Follower, SO101FollowerConfig
-        >>> robot = SO101Follower(SO101FollowerConfig(port="/dev/ttyACM0"))  # doctest: +SKIP
-        >>> robot.connect()  # doctest: +SKIP
-        >>> robot.send_action({"shoulder_pan.pos": 0.0})  # doctest: +SKIP
-        ```
-    """
-````
-
-Cross-references are omitted from the examples on this page — see [Cross-references](#cross-references) for
-their syntax and why they cannot be shown inside a code block.
-
-## Rules
-
-### Sections
-
-`Args:` · `Returns:` · `Raises:` · `Yields:` · `Example:` · `Note:`
-
-In that order. No other section headers. A one-line summary comes first, then an optional free-form
-description, then the sections.
-
-### The `Args:` line is machine-parsed
-
-```
-name (`type`, *optional*, defaults to `X`):
-    Description, indented on its own line.
-```
-
-The `*optional*, defaults to` clause is **checked against the real signature default** by
-`make check-docstrings`. It is not decorative — if you write a default that has drifted from the code, CI
-fails. Omit the clause entirely for required parameters:
-
-```python
-    Args:
-        port (`str`):
-            Serial port the arm is connected to, e.g. `/dev/ttyACM0`.
-        max_relative_target (`float | dict[str, float]`, *optional*):
-            Caps the magnitude of the relative positional target vector. `None` disables clipping.
-        use_degrees (`bool`, *optional*, defaults to `True`):
-            Keep `True` for backward compatibility with existing policies and datasets.
-```
-
-Types go in backticks. Use `*optional*` with no `defaults to` when the default is `None` or is otherwise not
-worth restating.
-
-### `Returns:` is type-first
-
-One indented line, type first, then a colon, then the description:
-
-```python
-    Returns:
-        `dict[str, float]`: The action actually written to the motors after safety clipping.
-```
-
-`Yields:` takes the same shape.
-
-### `**Attributes**:`, never `Attributes:`
-
-doc-builder parses a bare `Attributes:` as a **synonym for `Parameters:`**, so your attributes get rendered
-as constructor arguments. This is silent and wrong. Whenever the attributes differ from the constructor
-parameters, use the bold form with a `--` separator:
-
-```python
-class Robot(abc.ABC):
-    """The base abstract class for all LeRobot-compatible robots.
-
-    **Attributes**:
-        - **config_class** (`type[RobotConfig]`) -- The expected configuration class for this robot.
-        - **name** (`str`) -- The unique robot name used to identify this robot type.
-    """
-```
-
-Note `--`, not `:`.
-
-### Cross-references
-
-Use doc-builder's bracket syntax: a square-bracketed backtick-quoted path. **Sphinx roles (`:pymeth:`,
-`:pyattr:`) are not supported** and render as literal text on the page.
-
-| Want                         | Write                               |
-| ---------------------------- | ----------------------------------- |
-| Class in the main package    | [`Robot`]                   |
-| Method, show the full path   | [`Robot.connect`]           |
-| Method, show the bare name   | [`~Robot.connect`]          |
-| Nested path                  | [`~robots.Robot.connect`]   |
-| Object in another HF library | [`~accelerate.Accelerator`] |
-
-The `~` strips the path from the **link text only**; the link still resolves to the full path.
-
-> [!NOTE]
-> doc-builder resolves this syntax everywhere in a page — including inside fenced code blocks. That is why
-> the docstring examples on this page use plain prose instead of cross-references: a code block containing
-> one would render the resolved link rather than the syntax you need to type. In your own docstrings, use
-> cross-references freely; this restriction only affects documentation _about_ the syntax.
-
-### Callouts
-
-Use GitHub-style blockquotes:
-
-```markdown
-> [!TIP]
-> Call this once at startup — it takes about two seconds.
-
-> [!WARNING]
-> Torque is disabled on disconnect. The arm will drop if it is holding a load.
-```
-
-The `<Tip>` component is legacy per doc-builder; don't add new ones.
-
-### Examples must be fenced
-
-An example lives inside a fenced ` ```python ` block containing `>>> `. The fence is what makes it render
-as a code block, and it is what the doctest preprocessor's regex looks for:
-
-````python
-    Example:
-        ```python
-        >>> from lerobot.robots.so_follower import SO101FollowerConfig
-        >>> cfg = SO101FollowerConfig(port="/dev/ttyACM0")
-        >>> cfg.use_degrees
-        True
-        ```
-````
-
-> [!WARNING]
-> An unfenced `>>>` is still collected — doctest finds prompts anywhere in a docstring. What you lose is the
-> rendering, so it shows up as a wall of prose on the page. Every example needs the fence.
-
-Every example either executes in CI or carries `# doctest: +SKIP`. Anything that touches hardware, a GPU, or
-downloads from the Hub gets `+SKIP`:
-
-````python
-    Example:
-        ```python
-        >>> robot.connect()  # doctest: +SKIP
-        >>> policy = ACTPolicy.from_pretrained("lerobot/act_aloha_sim_transfer_cube_human")  # doctest: +SKIP
-        ```
-````
-
-Add files containing runnable examples to `utils/documentation_tests.txt`.
-
-Put examples on the three to five genuine entry points of a module. Examples on trivial accessors are noise.
-
-## Three patterns you will hit constantly
-
-### Config dataclasses
-
-Configuration fields are historically documented with `#` comments above each field. **doc-builder cannot
-see inline comments** — such a class renders with every field listed and not a single description. Move them
-into an `Args:` block on the class docstring:
-
-```python
-@dataclass
-class SOFollowerConfig:
-    """Configuration for SO-family follower arms.
-
-    Args:
-        port (`str`):
-            Serial port the arm is connected to, e.g. `/dev/ttyACM0`.
-        max_relative_target (`float | dict[str, float]`, *optional*):
-            Caps the magnitude of the relative positional target vector. A scalar applies to all motors;
-            a dict maps motor name to a per-motor cap. `None` disables clipping.
-        use_degrees (`bool`, *optional*, defaults to `True`):
-            Keep `True` for backward compatibility with existing policies and datasets.
-    """
-
-    port: str
-    max_relative_target: float | dict[str, float] | None = None
-    use_degrees: bool = True
-```
-
-> [!IMPORTANT]
-> **doc-builder does not inherit docstrings from base classes.** LeRobot's registered config classes are
-> often thin multiple-inheritance shims:
->
-> ```python
-> @RobotConfig.register_subclass("so101_follower")
-> @dataclass
-> class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
->     pass
-> ```
->
-> That class renders **every** field — including the ones it inherits — with no descriptions at all, no
-> matter how well the bases are documented. The `Args:` block must live on the concrete class that
-> `[[autodoc]]` names, and it must cover inherited fields too.
-
-### Base class, then concrete subclass
-
-The abstract base carries the canonical contract. Subclasses document only what deviates — port semantics,
-calibration quirks, motor layout, supported feature keys. Do not copy the base contract into every subclass.
-
-`Robot`, `Teleoperator`, `Camera`, `MotorsBus`, `ProcessorStep`, and `PreTrainedPolicy` all follow this
-shape.
-
-### Module-level aliases
-
-Several public names are aliases rather than distinct classes:
-
-```python
-SO100FollowerConfig = SOFollowerRobotConfig
-SO101FollowerConfig = SOFollowerRobotConfig
-```
-
-`[[autodoc]]` resolves the alias and renders the **canonical** class name, so a `## SO101FollowerConfig`
-heading will show `class lerobot.robots.so_follower.SOFollowerRobotConfig` in the body. Document the
-canonical class once, and mention the aliases in the page's prose rather than giving each alias its own
-autodoc block.
-
-## What not to document
-
-- **Private members.** Anything starting with `_` is not part of the public API.
-- **The type annotation restated as prose.** `port (`str`): A string.` adds nothing. Say what it is for.
-- **Vendored upstream code.** `src/lerobot/policies/molmoact2/molmoact2_hf_model/` is vendored from
-  `transformers` and already carries upstream-style docstrings. Leave it alone — restyling it only creates
-  conflicts on the next sync. It is excluded from the API reference and from the docstring checks.
-
-## How this is enforced
-
-| Check                     | What it catches                                                                                            |
-| ------------------------- | ---------------------------------------------------------------------------------------------------------- |
-| `make check-docstrings`   | An `Args:` entry that doesn't match the signature; a documented default that has drifted from the real one |
-| `make doctest`            | Examples that no longer run                                                                                |
-| `make check-doctest-list` | Stale or unsorted entries in `utils/documentation_tests.txt`                                               |
-| `ruff` (`D` rules)        | Google-convention style violations                                                                         |
-| `interrogate`             | Docstring coverage falling below the current threshold                                                     |
-| doc-builder               | A `[[autodoc]]` path that points at something that doesn't exist — this breaks the docs build              |
-
-Run them together before opening a PR:
-
-```bash
-make check-docstrings && make doctest && pre-commit run --all-files
-```
-
-Then render the page and actually look at it:
-
-```bash
-doc-builder build lerobot docs/source/ --build_dir /tmp/doc-build
-```
-
-## Checklist
-
-- [ ] Every public member you touched has a docstring.
-- [ ] Every `Args:` entry matches the signature, including the `*optional*, defaults to` clause.
-- [ ] `Returns:` is type-first on one indented line.
-- [ ] No bare `Attributes:` — use `**Attributes**:` with `--` separators.
-- [ ] No Sphinx roles — cross-references use &#91;`~module.Class.method`&#93;.
-- [ ] Examples are inside a fenced ` ```python ` block, and either run in CI or carry `# doctest: +SKIP`.
-- [ ] Config dataclass fields are in an `Args:` block on the concrete class, not `#` comments.
-- [ ] The rendered page has been eyeballed.

examples/phone_to_so100/evaluate.py

@@ -36,7 +36,7 @@ from lerobot.processor import (
 )
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 from lerobot.utils.constants import ACTION, OBS_STR
@@ -95,7 +95,7 @@ def main():
     # Build pipeline to convert joints observation to EE observation
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEObservation(
                 kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
             )
         ],

examples/phone_to_so100/record.py

@@ -29,7 +29,7 @@ from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     EEBoundsAndSafety,
     EEReferenceAndDelta,
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     GripperVelocityToJoint,
     InverseKinematicsEEToJoints,
 )
@@ -111,7 +111,7 @@ def main():
     # Build pipeline to convert joint observation to EE observation (FK).
     robot_joints_to_ee_pose = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEObservation(
                 kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
             )
         ],

examples/phone_to_so100/rollout.py

@@ -38,7 +38,7 @@ from lerobot.processor import (
 )
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
@@ -75,7 +75,7 @@ def main():
     )
 
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        steps=[ForwardKinematicsJointsToEEObservation(kinematics=kinematics_solver, motor_names=motor_names)],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
     )

examples/so100_to_so100_EE/evaluate.py

@@ -36,7 +36,7 @@ from lerobot.processor import (
 )
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 from lerobot.utils.constants import ACTION, OBS_STR
@@ -95,7 +95,7 @@ def main():
     # Build pipeline to convert joints observation to EE observation
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEObservation(
                 kinematics=kinematics_solver, motor_names=list(robot.bus.motors.keys())
             )
         ],

examples/so100_to_so100_EE/record.py

@@ -29,7 +29,8 @@ from lerobot.processor import (
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     EEBoundsAndSafety,
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEAction,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 from lerobot.scripts.lerobot_record import record_loop
@@ -78,7 +79,7 @@ def main():
     # Build pipeline to convert follower joints to EE observation.
     follower_joints_to_ee = RobotProcessorPipeline[RobotObservation, RobotObservation](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEObservation(
                 kinematics=follower_kinematics_solver, motor_names=list(follower.bus.motors.keys())
             ),
         ],
@@ -89,7 +90,7 @@ def main():
     # Build pipeline to convert leader joints to EE action.
     leader_joints_to_ee = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEAction(
                 kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
             ),
         ],

examples/so100_to_so100_EE/rollout.py

@@ -36,7 +36,7 @@ from lerobot.processor import (
 )
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEObservation,
     InverseKinematicsEEToJoints,
 )
 from lerobot.rollout import BaseStrategyConfig, RolloutConfig, build_rollout_context
@@ -78,7 +78,7 @@ def main():
 
     # Joint-space observation → EE-space observation (consumed by the policy).
     robot_joints_to_ee_pose_processor = RobotProcessorPipeline[RobotObservation, RobotObservation](
-        steps=[ForwardKinematicsJointsToEE(kinematics=kinematics_solver, motor_names=motor_names)],
+        steps=[ForwardKinematicsJointsToEEObservation(kinematics=kinematics_solver, motor_names=motor_names)],
         to_transition=observation_to_transition,
         to_output=transition_to_observation,
     )

examples/so100_to_so100_EE/teleoperate.py

@@ -27,7 +27,7 @@ from lerobot.processor import (
 from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
 from lerobot.robots.so_follower.robot_kinematic_processor import (
     EEBoundsAndSafety,
-    ForwardKinematicsJointsToEE,
+    ForwardKinematicsJointsToEEAction,
     InverseKinematicsEEToJoints,
 )
 from lerobot.teleoperators.so_leader import SO100Leader, SO100LeaderConfig
@@ -65,7 +65,7 @@ def main():
     # Build pipeline to convert teleop joints to EE action
     leader_to_ee = RobotProcessorPipeline[RobotAction, RobotAction](
         steps=[
-            ForwardKinematicsJointsToEE(
+            ForwardKinematicsJointsToEEAction(
                 kinematics=leader_kinematics_solver, motor_names=list(leader.bus.motors.keys())
             ),
         ],

pyproject.toml

@@ -346,7 +346,6 @@ lerobot-record="lerobot.scripts.lerobot_record:main"
 lerobot-replay="lerobot.scripts.lerobot_replay:main"
 lerobot-setup-motors="lerobot.scripts.lerobot_setup_motors:main"
 lerobot-teleoperate="lerobot.scripts.lerobot_teleoperate:main"
-lerobot-convert-dcp="lerobot.scripts.lerobot_convert_dcp:main"
 lerobot-eval="lerobot.scripts.lerobot_eval:main"
 lerobot-train="lerobot.scripts.lerobot_train:main"
 lerobot-train-tokenizer="lerobot.scripts.lerobot_train_tokenizer:main"
@@ -401,63 +400,19 @@ exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 # N: pep8-naming
 # TODO: Uncomment rules when ready to use
 select = [
-    "E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM", "D" #, "A", "S", "RUF"
+    "E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM" #, "A", "S", "D", "RUF"
 ]
 ignore = [
     "E501", # Line too long
     "T201", # Print statement found
     "T203", # Pprint statement found
     "B008", # Perform function call in argument defaults
-    # D100/D104: module- and package-level docstrings. The API reference is generated from class and
-    # function docstrings; a banner at the top of every file and every __init__.py would not appear on any
-    # rendered page. Coverage of the things that do get rendered is enforced by interrogate instead.
-    "D100",
-    "D104",
 ]
 
 [tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["F401", "F403", "E402", "D104"]
+"__init__.py" = ["F401", "F403", "E402"]
 # E402: conditional-import guards (TYPE_CHECKING / is_package_available) must precede the imports they protect
 "src/lerobot/scripts/convert_dataset_v21_to_v30.py" = ["E402"]
-
-# D (pydocstyle) is enabled globally, but only holds for code that has been converted to the docstring
-# standard in docs/source/writing_docstrings.mdx. Every module below is still on the old style; each entry
-# is deleted as that module is converted, and this block can be removed once it is empty.
-#
-# Not part of the API reference and not planned for conversion: tests, examples, benchmarks, templates,
-# CI helper scripts and the packaging shim.
-"tests/**" = ["D"]
-"examples/**" = ["D"]
-"benchmarks/**" = ["D"]
-"scripts/**" = ["D"]
-"setup.py" = ["D"]
-"src/lerobot/templates/**" = ["D"]
-# Vendored from transformers; keeps its upstream docstring style so syncs stay clean.
-"src/lerobot/policies/molmoact2/molmoact2_hf_model/**" = ["D"]
-# Awaiting conversion, one PR per module.
-"src/lerobot/annotations/**" = ["D"]
-"src/lerobot/async_inference/**" = ["D"]
-"src/lerobot/cameras/**" = ["D"]
-"src/lerobot/common/**" = ["D"]
-"src/lerobot/configs/**" = ["D"]
-"src/lerobot/data_processing/**" = ["D"]
-"src/lerobot/datasets/**" = ["D"]
-"src/lerobot/distributed/**" = ["D"]
-"src/lerobot/envs/**" = ["D"]
-"src/lerobot/jobs/**" = ["D"]
-"src/lerobot/model/**" = ["D"]
-"src/lerobot/motors/**" = ["D"]
-"src/lerobot/optim/**" = ["D"]
-"src/lerobot/policies/**" = ["D"]
-"src/lerobot/processor/**" = ["D"]
-"src/lerobot/rewards/**" = ["D"]
-"src/lerobot/rollout/**" = ["D"]
-"src/lerobot/scripts/**" = ["D"]
-"src/lerobot/teleoperators/**" = ["D"]
-"src/lerobot/transforms/**" = ["D"]
-"src/lerobot/transport/**" = ["D"]
-"src/lerobot/utils/**" = ["D"]
-"src/lerobot/lerobot_types.py" = ["D"]
 [tool.ruff.lint.isort]
 combine-as-imports = true
 known-first-party = ["lerobot"]
@@ -501,34 +456,25 @@ default.extend-ignore-identifiers-re = [
     "seperated_timestep",
 ]
 
-# Docstring coverage gate. `fail-under` is a RATCHET, not a target: it is set just below the currently
-# measured coverage so it passes today, and is raised in the same PR that documents a module. Never set it
-# to a value that fails on main. The destination is 100; see docs/source/writing_docstrings.mdx.
-[tool.interrogate]
-ignore-init-module = true
-ignore-init-method = true
-ignore-nested-functions = false
-ignore-magic = false
-ignore-semiprivate = false
-ignore-private = false
-ignore-property-decorators = false
-ignore-module = false
-ignore-setters = false
-fail-under = 55.5
-output-format = "term-missing"
-color = true
-paths = ["src/lerobot"]
-exclude = ["src/lerobot/policies/molmoact2/molmoact2_hf_model"]
+# TODO: Uncomment when ready to use
+# [tool.interrogate]
+# ignore-init-module = true
+# ignore-init-method = true
+# ignore-nested-functions = false
+# ignore-magic = false
+# ignore-semiprivate = false
+# ignore-private = false
+# ignore-property-decorators = false
+# ignore-module = false
+# ignore-setters = false
+# fail-under = 80
+# output-format = "term-missing"
+# color = true
+# paths = ["src/lerobot"]
 
 # TODO: Enable mypy gradually module by module across multiple PRs
 # Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
 
-[tool.pytest.ini_options]
-markers = [
-    "multigpu: distributed tests needing 2-4 GPUs (CI: docker_publish.yml lane)",
-    "multigpu_heavy: 8-GPU sweeps and soak tests; never run in CI",
-]
-
 [tool.mypy]
 python_version = "3.12"
 ignore_missing_imports = true
@@ -575,15 +521,6 @@ disallow_untyped_defs = true
 disallow_incomplete_defs = true
 check_untyped_defs = true
 
-[[tool.mypy.overrides]]
-module = "lerobot.distributed.*"
-ignore_errors = false
-
-# extra strictness for the distributed engine
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-check_untyped_defs = true
-
 [[tool.mypy.overrides]]
 module = "lerobot.optim.*"
 ignore_errors = false

src/lerobot/__init__.py

@@ -14,7 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""LeRobot -- PyTorch library for real-world robotics.
+"""
+LeRobot -- PyTorch library for real-world robotics.
 
 Provides datasets, pretrained policies, and tools for training, evaluation,
 data collection, and robot control. Integrates with Hugging Face Hub for

src/lerobot/__version__.py

@@ -13,7 +13,7 @@
 # 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.
-"""To enable `lerobot.__version__`."""
+"""To enable `lerobot.__version__`"""
 
 from importlib.metadata import PackageNotFoundError, version
 

src/lerobot/cameras/camera.py

@@ -33,10 +33,10 @@ class Camera(abc.ABC):
     - Connection/disconnection
     - Frame capture (sync/async/latest)
 
-    **Attributes**:
-        - **fps** (`int | None`) -- Configured frames per second.
-        - **width** (`int | None`) -- Frame width in pixels.
-        - **height** (`int | None`) -- Frame height in pixels.
+    Attributes:
+        fps (int | None): Configured frames per second
+        width (int | None): Frame width in pixels
+        height (int | None): Frame height in pixels
     """
 
     def __init__(self, config: CameraConfig):

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -40,20 +40,17 @@ class OpenCVCameraConfig(CameraConfig):
     OpenCVCameraConfig(0, 30, 1280, 720, fourcc="YUYV")     # With YUYV format
     ```
 
-    **Attributes**:
-        - **index_or_path** (`int | Path`) -- Either an integer representing the camera device index, or a
-          Path object pointing to a video file.
-        - **fps** -- Requested frames per second for the color stream.
-        - **width** -- Requested frame width in pixels for the color stream.
-        - **height** -- Requested frame height in pixels for the color stream.
-        - **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
-        - **rotation** (`Cv2Rotation`) -- Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no
-          rotation.
-        - **warmup_s** (`int`) -- Time reading frames before returning from connect (in seconds)
-        - **fourcc** (`str | None`) -- FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults
-          to None (auto-detect).
-        - **backend** (`Cv2Backends`) -- OpenCV backend identifier
-          (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
+    Attributes:
+        index_or_path: Either an integer representing the camera device index,
+                      or a Path object pointing to a video file.
+        fps: Requested frames per second for the color stream.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
+        warmup_s: Time reading frames before returning from connect (in seconds)
+        fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
+        backend: OpenCV backend identifier (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -43,16 +43,16 @@ class Reachy2CameraConfig(CameraConfig):
     )  # Left teleop camera, 640x480 @ 30FPS
     ```
 
-    **Attributes**:
-        - **name** (`str`) -- Name of the camera device. Can be "teleop" or "depth".
-        - **image_type** (`str`) -- Type of image stream. For "teleop" camera, can be "left" or "right". For
-          "depth" camera, can be "rgb" or "depth". (depth is not supported yet)
-        - **fps** -- Requested frames per second for the color stream. Not configurable for Reachy 2 cameras.
-        - **width** -- Requested frame width in pixels for the color stream.
-        - **height** -- Requested frame height in pixels for the color stream.
-        - **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
-        - **ip_address** (`str | None`) -- IP address of the robot. Defaults to "localhost".
-        - **port** (`int`) -- Port number for the camera server. Defaults to 50065.
+    Attributes:
+        name: Name of the camera device. Can be "teleop" or "depth".
+        image_type: Type of image stream. For "teleop" camera, can be "left" or "right".
+                    For "depth" camera, can be "rgb" or "depth". (depth is not supported yet)
+        fps: Requested frames per second for the color stream. Not configurable for Reachy 2 cameras.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        ip_address: IP address of the robot. Defaults to "localhost".
+        port: Port number for the camera server. Defaults to 50065.
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -36,28 +36,27 @@ class RealSenseCameraConfig(CameraConfig):
     RealSenseCameraConfig("0123456789", 30, 640, 480, rotation=Cv2Rotation.ROTATE_90)  # With 90° rotation
     ```
 
-    **Attributes**:
-        - **fps** -- Requested frames per second for the color stream.
-        - **width** -- Requested frame width in pixels for the color stream.
-        - **height** -- Requested frame height in pixels for the color stream.
-        - **serial_number_or_name** (`str`) -- Unique serial number or human-readable name to identify the
-          camera.
-        - **color_mode** (`ColorMode`) -- Color mode for image output (RGB or BGR). Defaults to RGB.
-        - **use_rgb** (`bool`) -- Whether to enable the color stream. Defaults to True.
-        - **use_depth** (`bool`) -- Whether to enable depth stream. Defaults to False.
-        - **rotation** (`Cv2Rotation`) -- Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no
-          rotation.
-        - **warmup_s** (`int`) -- Time reading frames before returning from connect (in seconds)
-        - **exposure** (`int | None`) -- Manual exposure value for the color sensor. When set, auto-exposure
-          is disabled and this fixed value is used. Valid ranges are camera-model specific and reported if the
-          value is rejected. Defaults to None (leave unchanged).
-        - **gain** (`int | None`) -- Manual gain value for the color sensor. When set, auto-exposure is
-          disabled and this fixed gain is used, which also freezes exposure at its current value when no
-          exposure is configured. Valid ranges are camera-model specific and reported if the value is
-          rejected. Defaults to None (leave unchanged).
-        - **white_balance** (`int | None`) -- Manual white balance value for the color sensor. When set, auto
-          white balance is disabled and this fixed value is used. Valid ranges are camera-model specific and
-          reported if the value is rejected. Defaults to None (leave unchanged).
+    Attributes:
+        fps: Requested frames per second for the color stream.
+        width: Requested frame width in pixels for the color stream.
+        height: Requested frame height in pixels for the color stream.
+        serial_number_or_name: Unique serial number or human-readable name to identify the camera.
+        color_mode: Color mode for image output (RGB or BGR). Defaults to RGB.
+        use_rgb: Whether to enable the color stream. Defaults to True.
+        use_depth: Whether to enable depth stream. Defaults to False.
+        rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
+        warmup_s: Time reading frames before returning from connect (in seconds)
+        exposure: Manual exposure value for the color sensor. When set, auto-exposure is
+            disabled and this fixed value is used. Valid ranges are camera-model specific
+            and reported if the value is rejected. Defaults to None (leave unchanged).
+        gain: Manual gain value for the color sensor. When set, auto-exposure is disabled
+            and this fixed gain is used, which also freezes exposure at its current value
+            when no exposure is configured. Valid ranges are camera-model specific and
+            reported if the value is rejected. Defaults to None (leave unchanged).
+        white_balance: Manual white balance value for the color sensor. When set, auto
+            white balance is disabled and this fixed value is used. Valid ranges are
+            camera-model specific and reported if the value is rejected. Defaults to None
+            (leave unchanged).
 
     Note:
         - Either name or serial_number must be specified.

src/lerobot/common/train_utils.py

@@ -13,41 +13,16 @@
 # 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.
-"""Training-output persistence: checkpoints, two-phase resume, and hub publishing.
-
-Rank discipline: every function here that can
-contain a collective is documented as such and must run on ALL ranks; rank-0-only file writes
-sit under one grouped ``is_main_process()`` gate per contiguous region, placed below all
-collectives. The leaf save/load helpers carry no rank gates of their own — the exception is
-``PreTrainedPolicy._save_pretrained``, whose gate is internal because its collective gather and
-its writes live in the same method.
-"""
-
-import logging
-from importlib.resources import files
 from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, Any
 
-import torch.distributed as dist
-from huggingface_hub import HfApi, ModelCard, ModelCardData, snapshot_download
+from huggingface_hub import HfApi, snapshot_download
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LRScheduler
 
-from lerobot.__version__ import __version__
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.rewards import RewardModelConfig
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.distributed.checkpoint import (
-    is_sharded_module,
-    load_sharded_model,
-    load_sharded_optimizer,
-    save_sharded_model,
-    save_sharded_optimizer,
-)
-from lerobot.distributed.utils import is_main_process
 from lerobot.optim import (
     load_optimizer_state,
+    load_optimizer_state_dict,
     load_scheduler_state,
     save_optimizer_state,
     save_scheduler_state,
@@ -65,39 +40,14 @@ from lerobot.utils.hub import find_latest_hub_checkpoint
 from lerobot.utils.io_utils import load_json, write_json
 from lerobot.utils.random_utils import load_rng_state, save_rng_state
 
-if TYPE_CHECKING:
-    from accelerate import Accelerator
-
-    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
-    from lerobot.rewards.pretrained import PreTrainedRewardModel
-
 
 def get_step_identifier(step: int, total_steps: int) -> str:
-    """Format a step number as the zero-padded identifier used for checkpoint directory names.
-
-    Args:
-        step (int): The training step to format.
-        total_steps (int): The total number of training steps; sets the padding width
-            (minimum 6 digits).
-
-    Returns:
-        str: The zero-padded step identifier, e.g. `"005000"`.
-    """
     num_digits = max(6, len(str(total_steps)))
     return f"{step:0{num_digits}d}"
 
 
 def get_step_checkpoint_dir(output_dir: Path, total_steps: int, step: int) -> Path:
-    """Returns the checkpoint sub-directory corresponding to the step number.
-
-    Args:
-        output_dir (Path): The training run's output directory.
-        total_steps (int): The total number of training steps; sets the identifier padding.
-        step (int): The training step of the checkpoint.
-
-    Returns:
-        Path: The checkpoint step directory, `output_dir/checkpoints/<step-identifier>`.
-    """
+    """Returns the checkpoint sub-directory corresponding to the step number."""
     step_identifier = get_step_identifier(step, total_steps)
     return output_dir / CHECKPOINTS_DIR / step_identifier
 
@@ -113,15 +63,37 @@ def should_save_checkpoint(step: int, save_freq: int, total_steps: int) -> bool:
     return (save_freq > 0 and step % save_freq == 0) or step == total_steps
 
 
-def update_last_checkpoint(checkpoint_dir: Path) -> None:
-    """Point the `last` symlink in the checkpoints directory at the given checkpoint.
+def save_training_step(
+    step: int, save_dir: Path, num_processes: int | None = None, batch_size: int | None = None
+) -> None:
+    state: dict = {"step": step}
+    # num_processes and batch_size are recorded so a resumed run can detect a changed world size or
+    # batch size: the sampler's resume offset is computed from the (num_processes, batch_size) that
+    # produced `step`, since both scale how many sampler positions a step consumes (see
+    # compute_sampler_state).
+    if num_processes is not None:
+        state["num_processes"] = num_processes
+    if batch_size is not None:
+        state["batch_size"] = batch_size
+    write_json(state, save_dir / TRAINING_STEP)
 
-    Any existing `last` symlink is replaced. The link target is relative to the checkpoints
-    directory, so the tree stays valid when the run directory is moved.
 
-    Args:
-        checkpoint_dir (Path): The checkpoint step directory the `last` link should target.
-    """
+def load_training_step(save_dir: Path) -> int:
+    training_step = load_json(save_dir / TRAINING_STEP)
+    return training_step["step"]
+
+
+def load_training_num_processes(checkpoint_dir: Path) -> int | None:
+    """World size recorded at checkpoint time, or None for checkpoints written before it was stored."""
+    return load_json(checkpoint_dir / TRAINING_STATE_DIR / TRAINING_STEP).get("num_processes")
+
+
+def load_training_batch_size(checkpoint_dir: Path) -> int | None:
+    """Per-process batch size recorded at checkpoint time, or None for older checkpoints."""
+    return load_json(checkpoint_dir / TRAINING_STATE_DIR / TRAINING_STEP).get("batch_size")
+
+
+def update_last_checkpoint(checkpoint_dir: Path) -> Path:
     last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK
     if last_checkpoint_dir.is_symlink():
         last_checkpoint_dir.unlink()
@@ -129,68 +101,6 @@ def update_last_checkpoint(checkpoint_dir: Path) -> None:
     last_checkpoint_dir.symlink_to(relative_target)
 
 
-# ---------------------------------------------------------------------------------------------
-# training_step.json
-# ---------------------------------------------------------------------------------------------
-
-
-def save_training_metadata(step: int, save_dir: Path, cfg: TrainPipelineConfig) -> None:
-    """Record the step counter plus everything a resume needs to reason about topology changes.
-
-    `step` counts loop iterations (= micro-batches), so
-    the sampler resume offset is `step x batch_size x dp_world_size` with no grad-accum factor.
-    `grad_accum_steps` and the parallelism snapshot are recorded so a resume can warn precisely
-    when the optimizer-update cadence or the sharding topology changed.
-
-    Args:
-        step (int): The training step (micro-batch counter) to record.
-        save_dir (Path): The `training_state/` directory to write `training_step.json` into.
-        cfg (TrainPipelineConfig): The training config whose batch size, gradient-accumulation,
-            and parallelism settings are snapshotted alongside the step.
-    """
-    state: dict[str, Any] = {
-        "step": step,
-        "dp_world_size": cfg.parallelism.dp_world_size,
-        "batch_size": cfg.batch_size,
-        "grad_accum_steps": cfg.accelerator.gradient_accumulation.steps,
-        "parallelism": {
-            "dp_replicate": cfg.parallelism.dp_replicate,
-            "dp_shard": cfg.parallelism.dp_shard,
-            "ring_degree": cfg.parallelism.context_parallel.ring_degree,
-            "ulysses_degree": cfg.parallelism.context_parallel.ulysses_degree,
-        },
-    }
-    write_json(state, save_dir / TRAINING_STEP)
-
-
-def load_training_metadata(training_state_dir: Path) -> dict[str, Any]:
-    """Read everything `save_training_metadata` recorded, in a single pass.
-
-    Every key is always present: fields a checkpoint predates come back as None, so a caller
-    reading `metadata["batch_size"]` gets a KeyError on a typo rather than a silent None.
-
-    Args:
-        training_state_dir (Path): The checkpoint's `training_state/` directory.
-
-    Returns:
-        dict[str, Any]: `step` plus the `dp_world_size`, `batch_size`, `grad_accum_steps` and
-            `parallelism` snapshot recorded alongside it (None where not recorded).
-    """
-    state = load_json(training_state_dir / TRAINING_STEP)
-    return {
-        "step": int(state["step"]),
-        "dp_world_size": state.get("dp_world_size", state.get("num_processes")),
-        "batch_size": state.get("batch_size"),
-        "grad_accum_steps": state.get("grad_accum_steps"),
-        "parallelism": state.get("parallelism"),
-    }
-
-
-# ---------------------------------------------------------------------------------------------
-# Checkpoint save
-# ---------------------------------------------------------------------------------------------
-
-
 def save_checkpoint(
     checkpoint_dir: Path,
     step: int,
@@ -200,301 +110,192 @@ def save_checkpoint(
     scheduler: LRScheduler | None = None,
     preprocessor: PolicyProcessorPipeline | None = None,
     postprocessor: PolicyProcessorPipeline | None = None,
-    accelerator: "Accelerator | None" = None,
+    num_processes: int | None = None,
+    batch_size: int | None = None,
+    model_state_dict: dict | None = None,
+    optim_state_dict: dict | None = None,
 ) -> None:
     """This function creates the following directory structure:
 
     005000/  #  training step at checkpoint
     ├── pretrained_model/
     │   ├── config.json  # policy config
-    │   ├── model.safetensors  # policy weights (checkpoint_format ∈ {safetensors, safetensors_dcp}, or any non-sharded run)
-    │   ├── pytorch_model_fsdp_0/  # DCP model shards (checkpoint_format ∈ {dcp, safetensors_dcp})
+    │   ├── model.safetensors  # policy weights
     │   ├── train_config.json  # train config
-    │   ├── policy_preprocessor.json  # preprocessor config (if preprocessor provided)
-    │   ├── policy_preprocessor_step_*.safetensors  # state of the stateful preprocessor steps
-    │   ├── policy_postprocessor.json  # postprocessor config (if postprocessor provided)
-    │   └── policy_postprocessor_step_*.safetensors  # state of the stateful postprocessor steps
+    │   ├── processor.json  # processor config (if preprocessor provided)
+    │   └── step_*.safetensors  # processor state files (if any)
     └── training_state/
-        ├── optimizer_param_groups.json  # optimizer param groups (non-sharded runs)
-        ├── optimizer_state.safetensors  # optimizer state (non-sharded runs)
-        ├── optimizer_0/  # DCP optimizer shards (sharded runs)
+        ├── optimizer_param_groups.json  #  optimizer param groups
+        ├── optimizer_state.safetensors  # optimizer state
         ├── rng_state.safetensors  # rng states
-        ├── scheduler_state.json  # scheduler state (if scheduler provided)
-        └── training_step.json  # training step + dp_world_size/batch_size/grad_accum + topology
-
-    Collective: MUST be called on every rank. Rank-0-only writes are gated internally, so the
-    call site needs no rank branches.
+        ├── scheduler_state.json  # scheduler state
+        └── training_step.json  # training step
 
     Args:
-        checkpoint_dir (Path): The checkpoint step directory to write (e.g. `.../checkpoints/005000`).
-        step (int): The training step at that checkpoint.
         cfg (TrainPipelineConfig): The training config used for this run.
+        step (int): The training step at that checkpoint.
         policy (PreTrainedPolicy): The policy to save.
-        optimizer (Optimizer): The optimizer to save the state from.
+        optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None.
         scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
-        preprocessor (PolicyProcessorPipeline | None, optional): The preprocessor/pipeline to save.
+        preprocessor: The preprocessor/pipeline to save. Defaults to None.
+        postprocessor: The postprocessor/pipeline to save. Defaults to None.
+        num_processes (int | None, optional): Distributed world size to record for sample-exact
+            resume. Defaults to None (not recorded).
+        batch_size (int | None, optional): Per-process batch size to record for sample-exact
+            resume. Defaults to None (not recorded).
+        model_state_dict: Pre-gathered full (unsharded) model state dict. Required under FSDP,
+            where `policy.state_dict()` would return sharded tensors; the caller gathers it via a
+            cross-rank collective and passes it here so rank 0 can write it directly. It holds
+            FSDP's fp32 master weights and is saved as-is (the loader casts to the policy dtype on
+            read). When None (DDP / single-GPU), the model is saved the normal way. Defaults to None.
+        optim_state_dict: Pre-gathered full (unsharded) optimizer state dict. Required under FSDP
+            (gathered alongside `model_state_dict` via `gather_fsdp_state_dicts`); saved in the same
+            safetensors format as the single-GPU path. When None, `optimizer.state_dict()` is used.
             Defaults to None.
-        postprocessor (PolicyProcessorPipeline | None, optional): The postprocessor/pipeline to save.
-            Defaults to None.
-        accelerator (Accelerator | None, optional): The accelerator the policy was prepared with;
-            used to unwrap the model and required on sharded runs, where it owns the DCP save
-            channels. Defaults to None (plain single-process saves).
     """
     pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
-    fmt = cfg.checkpoint_format
-    policy_to_save = accelerator.unwrap_model(policy) if accelerator is not None else policy
-    sharded = is_sharded_module(policy_to_save)
-
-    # -- model artifact(s): the two collective-capable calls ----------------------------------
+    policy.save_pretrained(pretrained_dir, state_dict=model_state_dict)
+    cfg.save_pretrained(pretrained_dir)
     if cfg.peft is not None:
-        # PeftModel.save_pretrained is an external API with no internal rank gate, and the
-        # adapters are replicated (PEFT x sharded is rejected at validation): main rank writes.
-        if is_main_process():
-            policy_to_save.save_pretrained(pretrained_dir)
-    elif fmt.wants_safetensors or not sharded:
-        # Collective when sharded (full gather); writes happen on the main process only in all
-        # multi-rank layouts (the gate lives inside _save_pretrained, next to its collective gather).
-        policy_to_save.save_pretrained(pretrained_dir)
-    if fmt.wants_dcp and sharded:
-        save_sharded_model(accelerator, policy_to_save, pretrained_dir)
-
-    # -- sidecar configs: ONE gate for the whole contiguous rank-0-only region ----------------
-    if is_main_process():
-        if fmt.wants_dcp and not fmt.wants_safetensors:
-            # save_pretrained did not run: keep the DCP-only checkpoint self-describing.
-            policy_to_save.config.save_pretrained(pretrained_dir)
-        cfg.save_pretrained(pretrained_dir)
-        if cfg.peft is not None:
-            # PEFT's save_pretrained writes only adapter weights + config; the policy config
-            # needed to reload the base model is written explicitly.
-            policy_to_save.config.save_pretrained(pretrained_dir)
-        if preprocessor is not None:
-            preprocessor.save_pretrained(pretrained_dir)
-        if postprocessor is not None:
-            postprocessor.save_pretrained(pretrained_dir)
-
+        # When using PEFT, policy.save_pretrained will only write the adapter weights + config, not the
+        # policy config which we need for loading the model. In this case we'll write it ourselves.
+        policy.config.save_pretrained(pretrained_dir)
+    if preprocessor is not None:
+        preprocessor.save_pretrained(pretrained_dir)
+    if postprocessor is not None:
+        postprocessor.save_pretrained(pretrained_dir)
     save_training_state(
-        checkpoint_dir, step, cfg, optimizer, scheduler, accelerator, sharded=sharded, model=policy_to_save
+        checkpoint_dir,
+        step,
+        optimizer,
+        scheduler,
+        num_processes=num_processes,
+        batch_size=batch_size,
+        optim_state_dict=optim_state_dict,
     )
-    if accelerator is not None:
-        accelerator.wait_for_everyone()
 
 
 def save_training_state(
     checkpoint_dir: Path,
-    step: int,
-    cfg: TrainPipelineConfig,
-    optimizer: Optimizer | dict[str, Optimizer] | None = None,
+    train_step: int,
+    optimizer: Optimizer | None = None,
     scheduler: LRScheduler | None = None,
-    accelerator: "Accelerator | None" = None,
-    *,
-    sharded: bool = False,
-    model: PreTrainedPolicy | None = None,
+    num_processes: int | None = None,
+    batch_size: int | None = None,
+    optim_state_dict: dict | None = None,
 ) -> None:
-    """Write training_state/. Collective under sharding: call on every rank.
+    """
+    Saves the training step, optimizer state, scheduler state, and rng state.
 
     Args:
-        checkpoint_dir (Path): The checkpoint step directory; `training_state/` is created inside it.
-        step (int): The training step at that checkpoint.
-        cfg (TrainPipelineConfig): The training config used for this run (its topology and
-            accumulation settings are recorded in `training_step.json`).
-        optimizer (Optimizer | dict[str, Optimizer] | None, optional): The optimizer(s) to save
-            the state from. Defaults to None.
-        scheduler (LRScheduler | None, optional): The scheduler to save the state from.
+        save_dir (Path): The directory to save artifacts to.
+        train_step (int): Current training step.
+        optimizer (Optimizer | None, optional): The optimizer from which to save the state_dict.
             Defaults to None.
-        accelerator (Accelerator | None, optional): Required when `sharded` is True — it owns
-            the DCP optimizer save channel. Defaults to None.
-        sharded (bool): The model's sharding state, computed once in `save_checkpoint` and
-            threaded here so the two sites cannot disagree. Defaults to False.
-        model (PreTrainedPolicy | None, optional): Required only for the sharded optimizer
-            channel: torch's optimizer DCP APIs are model-coupled (the state dict is keyed by
-            model FQNs), so accelerate's `save_fsdp_optimizer` needs the sharded module
-            alongside the optimizer. Defaults to None.
+        scheduler (LRScheduler | None, optional): The scheduler from which to save the state_dict.
+            Defaults to None.
+        num_processes (int | None, optional): Distributed world size to record. Defaults to None.
+        batch_size (int | None, optional): Per-process batch size to record. Defaults to None.
+        optim_state_dict: Pre-gathered full optimizer state dict (for FSDP). Saved instead of
+            `optimizer.state_dict()` when provided. Defaults to None.
     """
     save_dir = checkpoint_dir / TRAINING_STATE_DIR
-    # All ranks: the directory must exist before the DCP optimizer collective writes into it
-    # (exist_ok makes the concurrent mkdir race-free on shared filesystems).
     save_dir.mkdir(parents=True, exist_ok=True)
-
-    if optimizer is not None and sharded:
-        if accelerator is None or model is None:
-            raise ValueError("Saving a sharded optimizer state requires the accelerator and model.")
-        # Collective — all ranks write their DCP shards into optimizer_0/.
-        save_sharded_optimizer(accelerator, optimizer, model, save_dir)
-
-    if is_main_process():  # ONE grouped gate for the whole rank-0-only region
-        save_training_metadata(step, save_dir, cfg)
-        save_rng_state(save_dir)
-        if scheduler is not None:
-            save_scheduler_state(scheduler, save_dir)
-        if optimizer is not None and not sharded:
-            save_optimizer_state(optimizer, save_dir)
+    save_training_step(train_step, save_dir, num_processes=num_processes, batch_size=batch_size)
+    save_rng_state(save_dir)
+    if optimizer is not None:
+        save_optimizer_state(optimizer, save_dir, optim_state_dict=optim_state_dict)
+    if scheduler is not None:
+        save_scheduler_state(scheduler, save_dir)
 
 
-# ---------------------------------------------------------------------------------------------
-# Two-phase resume
-# ---------------------------------------------------------------------------------------------
-
-
-def resume_before_prepare(cfg: TrainPipelineConfig) -> int:
-    """Phase 1 — before `accelerator.prepare()`: restore RNG and return the step counter.
-
-    Pure loaders only. The sampler resume offset is *derived* from the returned step inside the
-    dataloader factory, and everything bound to sharded objects (model DCP shards, optimizer,
-    scheduler) loads in `resume_after_prepare`.
+def load_training_state(
+    checkpoint_dir: Path, optimizer: Optimizer, scheduler: LRScheduler | None, load_optimizer: bool = True
+) -> tuple[int, Optimizer, LRScheduler | None]:
+    """
+    Loads the training step, optimizer state, scheduler state, and rng state.
+    This is used to resume a training run.
 
     Args:
-        cfg (TrainPipelineConfig): The resumed training config; `cfg.checkpoint_path` locates
-            the checkpoint to restore from.
+        checkpoint_dir (Path): The checkpoint directory. Should contain a 'training_state' dir.
+        optimizer (Optimizer): The optimizer to load the state_dict to.
+        scheduler (LRScheduler | None): The scheduler to load the state_dict to (can be None).
+        load_optimizer (bool, optional): Whether to load the optimizer state from disk. Defaults to
+            True. Set to False under FSDP, where the sharded optimizer state must be loaded after
+            `accelerator.prepare()` via `load_fsdp_optimizer_state` (the optimizer is returned
+            untouched here).
+
+    Raises:
+        NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir
 
     Returns:
-        int: The training step recorded in the checkpoint (micro-batch counter).
-
-    Raises:
-        NotADirectoryError: If the checkpoint has no `training_state/` directory.
-        ValueError: If the resumed topology crosses the sharded/non-sharded boundary relative
-            to the one recorded in the checkpoint.
+        tuple[int, Optimizer, LRScheduler | None]: training step, optimizer and scheduler with their
+            state_dict loaded.
     """
-    training_state_dir = cfg.checkpoint_path / TRAINING_STATE_DIR
+    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
     if not training_state_dir.is_dir():
         raise NotADirectoryError(training_state_dir)
-    metadata = load_training_metadata(training_state_dir)
-    _guard_resume_changes(cfg, metadata)
+
     load_rng_state(training_state_dir)
-    return metadata["step"]
-
-
-def _guard_resume_changes(cfg: TrainPipelineConfig, metadata: dict[str, Any]) -> None:
-    """Check the resumed run settings against the ones recorded in the checkpoint.
-
-    Two tiers, both driven by the checkpoint's recorded parallelism snapshot:
-
-    - **Hard error** when the resume crosses the sharded/non-sharded boundary in either
-      direction: the checkpoint's training-state artifacts only support resuming on the same
-      kind of topology (resharding works across sizes, not across kinds). Checkpoints without
-      a recorded snapshot skip this check.
-    - **One warning** naming every other recorded setting that differs — those changes are
-      legal (DCP reshards weights and optimizer state across topologies and the sampler offset
-      adapts), but a changed ``grad_accum_steps`` shifts the optimizer-update cadence, so the
-      resume says precisely what differs. The sampler-exactness warnings
-      (``dp_world_size``/``batch_size``) live with the sampler math in the dataloader factory.
-
-    Args:
-        cfg (TrainPipelineConfig): The resumed training config, compared against the settings
-            recorded in the checkpoint.
-        metadata (dict[str, Any]): The checkpoint's recorded training metadata, as returned by
-            `load_training_metadata`.
-
-    Raises:
-        ValueError: If the checkpoint records a sharded topology and the resumed run is
-            non-sharded, or vice versa.
-    """
-    snapshot = metadata["parallelism"]
-
-    if snapshot is not None:
-        recorded_sharded = (
-            snapshot.get("dp_shard", 1) != 1
-            or snapshot.get("ring_degree", 1) * snapshot.get("ulysses_degree", 1) > 1
-        )
-        if recorded_sharded != cfg.parallelism.is_sharded:
-            raise ValueError(
-                f"Cannot resume: the checkpoint was written with a "
-                f"{'sharded' if recorded_sharded else 'non-sharded'} topology "
-                f"(dp_replicate={snapshot.get('dp_replicate')}, dp_shard={snapshot.get('dp_shard')}) "
-                f"but this run is {'sharded' if cfg.parallelism.is_sharded else 'non-sharded'} "
-                f"(dp_replicate={cfg.parallelism.dp_replicate}, dp_shard={cfg.parallelism.dp_shard})."
-            )
-
-    recorded = {
-        "grad_accum_steps": (
-            metadata["grad_accum_steps"],
-            cfg.accelerator.gradient_accumulation.steps,
-        ),
-    }
-    if snapshot is not None:
-        recorded.update(
-            {
-                "dp_replicate": (snapshot.get("dp_replicate"), cfg.parallelism.dp_replicate),
-                "dp_shard": (snapshot.get("dp_shard"), cfg.parallelism.dp_shard),
-                "ring_degree": (
-                    snapshot.get("ring_degree"),
-                    cfg.parallelism.context_parallel.ring_degree,
-                ),
-                "ulysses_degree": (
-                    snapshot.get("ulysses_degree"),
-                    cfg.parallelism.context_parallel.ulysses_degree,
-                ),
-            }
-        )
-    changed = [f"{key}: {was} -> {now}" for key, (was, now) in recorded.items() if was not in (None, now)]
-    if changed and is_main_process():
-        logging.warning(
-            "Resuming with settings that differ from the checkpoint: " + "; ".join(changed) + ". "
-            "Topology changes reshard safely via DCP; a changed grad_accum_steps shifts the "
-            "optimizer-update cadence (the step counter keeps counting micro-batches)."
-        )
-
-
-def resume_after_prepare(
-    cfg: TrainPipelineConfig,
-    accelerator: "Accelerator",
-    policy: PreTrainedPolicy,
-    optimizer: Optimizer | dict[str, Optimizer],
-    scheduler: LRScheduler | None,
-) -> None:
-    """Phase 2 — after `accelerator.prepare()`: model (DCP) -> optimizer -> scheduler.
-
-    Collective under sharding: call on every rank. The model-weight source follows the
-    checkpoint's own recorded `checkpoint_format` (on resume, `cfg` was parsed from the
-    checkpoint's train_config.json): DCP-bearing formats load shards here into the prepared
-    model (whose construction skipped the safetensors load); the safetensors format was already
-    loaded by `from_pretrained` before sharding — no model step here.
-
-    Args:
-        cfg (TrainPipelineConfig): The resumed training config; `cfg.checkpoint_path` locates
-            the checkpoint and `cfg.checkpoint_format` selects the model-weight source.
-        accelerator (Accelerator): The accelerator the policy was prepared with; it unwraps the
-            model and owns the DCP load channels.
-        policy (PreTrainedPolicy): The prepared (possibly sharded) policy to load weights into.
-        optimizer (Optimizer | dict[str, Optimizer]): The prepared optimizer(s) to restore.
-        scheduler (LRScheduler | None): The scheduler to restore, or None if the run has none.
-
-    Raises:
-        FileNotFoundError: If the checkpoint format declares DCP model shards but the shard
-            directory is missing (e.g. it was pruned before upload).
-    """
-    checkpoint_dir = cfg.checkpoint_path
-    pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
-    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
-    unwrapped = accelerator.unwrap_model(policy)
-    sharded = is_sharded_module(unwrapped)
-
-    if cfg.checkpoint_format.wants_dcp:
-        from accelerate.utils.constants import FSDP_MODEL_NAME
-
-        dcp_dir = pretrained_dir / f"{FSDP_MODEL_NAME}_0"
-        if not dcp_dir.is_dir():
-            raise FileNotFoundError(
-                f"checkpoint_format={cfg.checkpoint_format.value} declares DCP model shards, "
-                f"but {dcp_dir} is missing. If the shards were pruned, convert what remains "
-                "with `lerobot-convert-dcp` or resume from a safetensors checkpoint."
-            )
-        load_sharded_model(accelerator, unwrapped, pretrained_dir)
-
-    if sharded:
-        # Requires the prepared optimizer: FSDP2's prepare rebinds param groups to DTensors but
-        # never migrates optimizer.state — DCP reshards it here (works across topology changes).
-        load_sharded_optimizer(accelerator, optimizer, unwrapped, training_state_dir)
-    else:
-        load_optimizer_state(optimizer, training_state_dir)
-
+    step = load_training_step(training_state_dir)
+    if load_optimizer:
+        optimizer = load_optimizer_state(optimizer, training_state_dir)
     if scheduler is not None:
-        load_scheduler_state(scheduler, training_state_dir)
+        scheduler = load_scheduler_state(scheduler, training_state_dir)
+
+    return step, optimizer, scheduler
 
 
-# ---------------------------------------------------------------------------------------------
-# Hub: checkpoint push (resume artifact) and publishing (distribution artifact)
-# ---------------------------------------------------------------------------------------------
+def gather_fsdp_state_dicts(model, optimizer) -> tuple[dict, dict]:
+    """Gather the full (unsharded) model and optimizer state dicts under FSDP.
+
+    `model.state_dict()` and `FSDP.optim_state_dict(...)` are cross-rank collectives, so this must be
+    called on *every* rank with the prepared (FSDP-wrapped) `model` and `optimizer`. With
+    `rank0_only=True` and `offload_to_cpu=True`, every rank runs the all-gather but only rank 0
+    materializes the full dicts (the others get empty dicts) and they are kept on CPU to bound GPU
+    memory. The returned optimizer state dict is keyed by parameter FQNs and is world-size
+    independent; `load_fsdp_optimizer_state` reshards it on resume.
+
+    Returns:
+        (model_state_dict, optim_state_dict): full dicts on rank 0, empty dicts on other ranks.
+    """
+    from torch.distributed.fsdp import (
+        FullOptimStateDictConfig,
+        FullStateDictConfig,
+        FullyShardedDataParallel as FSDP,  # noqa F401
+        StateDictType,
+    )
+
+    state_cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+    optim_cfg = FullOptimStateDictConfig(offload_to_cpu=True, rank0_only=True)
+    with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
+        model_state_dict = model.state_dict()
+        optim_state_dict = FSDP.optim_state_dict(model, optimizer)
+    return model_state_dict, optim_state_dict
+
+
+def load_fsdp_optimizer_state(model, optimizer, checkpoint_dir: Path) -> None:
+    """Load the FSDP optimizer state (saved as safetensors) and reshard it into the optimizer.
+
+    This is a cross-rank collective and must be called on every rank *after* `accelerator.prepare()`
+    with the prepared (FSDP-wrapped) `model` and `optimizer`. The saved state is the full,
+    world-size-independent optimizer state (keyed by parameter FQNs); `FSDP.optim_state_dict_to_load`
+    reshards it to the current FSDP topology, so resume on a different number of GPUs works.
+    """
+    from torch.distributed.fsdp import (
+        FullOptimStateDictConfig,
+        FullStateDictConfig,
+        FullyShardedDataParallel as FSDP,  # noqa F401
+        StateDictType,
+    )
+
+    # Every rank reads the same full state from the (shared) checkpoint dir, so rank0_only=False.
+    full_osd = load_optimizer_state_dict(checkpoint_dir / TRAINING_STATE_DIR)
+    state_cfg = FullStateDictConfig(rank0_only=False)
+    optim_cfg = FullOptimStateDictConfig(rank0_only=False)
+    with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, state_cfg, optim_cfg):
+        sharded_osd = FSDP.optim_state_dict_to_load(model=model, optim=optimizer, optim_state_dict=full_osd)
+    optimizer.load_state_dict(sharded_osd)
 
 
 def push_checkpoint_to_hub(
@@ -510,16 +311,6 @@ def push_checkpoint_to_hub(
     The model repo is created idempotently, and the commit is tagged with the
     checkpoint step so a checkpoint can be recovered with
     --policy.pretrained_revision=<step> instead of a commit sha.
-
-    The directory is uploaded verbatim — including DCP shards under the DCP formats: this tree
-    exists for *resume*, not distribution, and `resolve_resume_checkpoint` downloads it back
-    symmetrically.
-
-    Args:
-        checkpoint_dir (Path): The local checkpoint step directory to upload.
-        repo_id (str): The Hub model repo to push to (created idempotently if missing).
-        private (bool | None): Whether a newly created repo should be private. Defaults to
-            None (public unless the organization's default is private).
     """
     api = HfApi()
     api.create_repo(repo_id=repo_id, repo_type="model", private=private, exist_ok=True)
@@ -547,16 +338,6 @@ def resolve_resume_checkpoint(repo_id: str, output_dir: Path) -> Path:
     into `output_dir/checkpoints/<step>/`, recreate the local `last` symlink, and return that local
     checkpoint dir. Used to resume training from the Hub on a machine (or HF Jobs pod) that does not
     have the original local run dir.
-
-    Args:
-        repo_id (str): The Hub model repo holding `checkpoints/<step>/` subtrees.
-        output_dir (Path): The local run directory to download the checkpoint into.
-
-    Returns:
-        Path: The local checkpoint step directory, `output_dir/checkpoints/<step>`.
-
-    Raises:
-        FileNotFoundError: If the repo contains no checkpoints under `checkpoints/`.
     """
     latest = find_latest_hub_checkpoint(repo_id)
     if latest is None:
@@ -573,214 +354,3 @@ def resolve_resume_checkpoint(repo_id: str, output_dir: Path) -> Path:
     checkpoint_dir = output_dir / latest
     update_last_checkpoint(checkpoint_dir)
     return checkpoint_dir
-
-
-def publish_trained_model(
-    cfg: TrainPipelineConfig,
-    model: "PreTrainedPolicy | PreTrainedRewardModel",
-    preprocessor: PolicyProcessorPipeline | None,
-    postprocessor: PolicyProcessorPipeline | None,
-    dataset_meta: "LeRobotDatasetMetadata | None",
-    *,
-    peft_model: Any | None = None,
-) -> None:
-    """Publish the complete training bundle as a distributable model repo.
-
-    Collective-safe: call on ALL ranks — the model commit gathers sharded weights through
-    `save_pretrained`; uploads happen on the main process only (gated inside
-    `HubMixin.push_to_hub` and here). Commits, in order: (1) the model (skipped for PEFT —
-    adapters replace full weights), (2) the preprocessor, (3) the postprocessor, (4) the bundle
-    sidecar: README.md model card + train_config.json (+ adapter weights and the wrapped
-    policy's config in the PEFT case). Every commit uploads a freshly assembled directory, so
-    a published repo carries only the distributable artifacts.
-
-    Args:
-        cfg (TrainPipelineConfig): The training config; saved as `train_config.json` and used
-            to render the model card.
-        model (PreTrainedPolicy | PreTrainedRewardModel): The trained model to publish; its
-            config supplies the target repo id, visibility, license, and tags.
-        preprocessor (PolicyProcessorPipeline | None): The preprocessor pipeline to publish
-            alongside the model, if any.
-        postprocessor (PolicyProcessorPipeline | None): The postprocessor pipeline to publish
-            alongside the model, if any.
-        dataset_meta (LeRobotDatasetMetadata | None): Dataset metadata for the model card, if
-            available.
-        peft_model (Any | None): The PEFT wrapper when training adapters; its adapter weights
-            replace the full model weights in the published repo. Defaults to None.
-
-    Raises:
-        ValueError: If the model config carries no repo id (`--policy.repo_id`).
-    """
-    model_cfg = model.config
-    repo_id = model_cfg.repo_id
-    if not repo_id:
-        raise ValueError("Publishing requires a repo id (--policy.repo_id).")
-    ignore = ["*.tmp", "*.log"]
-
-    if peft_model is None:
-        # Calls are made on the exact objects that own each method (never through PEFT's
-        # attribute forwarding), so the peft branch below never touches this path.
-        model.push_to_hub(repo_id, private=model_cfg.private, ignore_patterns=ignore)
-    if preprocessor is not None:
-        preprocessor.push_to_hub(repo_id, private=model_cfg.private)
-    if postprocessor is not None:
-        postprocessor.push_to_hub(repo_id, private=model_cfg.private)
-
-    if is_main_process():
-        api = HfApi()
-        repo_id = api.create_repo(repo_id=repo_id, private=model_cfg.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)
-            if peft_model is not None:
-                peft_model.save_pretrained(saved_path)  # adapter weights + adapter config
-                model.config.save_pretrained(saved_path)  # PEFT cannot write the policy config
-            card = generate_model_card(model_cfg, cfg=cfg, dataset_meta=dataset_meta)
-            card.save(str(saved_path / "README.md"))
-            cfg.save_pretrained(saved_path)  # train_config.json
-            commit_info = api.upload_folder(
-                repo_id=repo_id,
-                repo_type="model",
-                folder_path=saved_path,
-                commit_message="Upload model card and train config",
-                allow_patterns=["*.safetensors", "*.json", "*.yaml", "*.md"],
-                ignore_patterns=ignore,
-            )
-        # Contract: lerobot.jobs.hf.submit_to_hf watches for this exact "Model pushed to <url>"
-        # line to end a remote run early. Keep the wording and URL format in sync.
-        logging.info(f"Model pushed to {commit_info.repo_url.url}")
-
-    if dist.is_initialized():
-        dist.barrier()
-
-
-# ---------------------------------------------------------------------------------------------
-# Model card
-# ---------------------------------------------------------------------------------------------
-
-_BASE_MODEL_MAPPING = {
-    "smolvla": "lerobot/smolvla_base",
-    "pi0": "lerobot/pi0_base",
-    "pi05": "lerobot/pi05_base",
-    "pi0_fast": "lerobot/pi0fast-base",
-    "xvla": "lerobot/xvla-base",
-}
-
-
-def build_card_context(
-    cfg: TrainPipelineConfig | None,
-    dataset_meta: "LeRobotDatasetMetadata | None",
-    input_features: dict | None,
-    output_features: dict | None,
-) -> dict:
-    """Collect optional data for the model-card template.
-
-    Returns plain values only (no Markdown) — the template in
-    ``lerobot/templates/lerobot_modelcard_template.md`` decides how and whether to show
-    each one. Everything is best-effort: anything unavailable is left empty/None and the
-    template simply skips that section, so this never breaks a Hub push.
-
-    Args:
-        cfg (TrainPipelineConfig | None): The training config supplying the training section,
-            if available.
-        dataset_meta (LeRobotDatasetMetadata | None): Dataset metadata supplying the dataset,
-            robot-type, and camera sections, if available.
-        input_features (dict | None): The policy's input feature declarations, if any.
-        output_features (dict | None): The policy's output feature declarations, if any.
-
-    Returns:
-        dict: Template context with `training`, `input_features`, `output_features`,
-            `dataset`, `robot_type`, and `cameras` entries; unavailable pieces stay
-            empty/None.
-    """
-    context = {
-        "training": None,
-        "input_features": input_features or {},
-        "output_features": output_features or {},
-        "dataset": None,
-        "robot_type": None,
-        "cameras": [],
-    }
-
-    if cfg is not None:
-        optimizer = getattr(cfg, "optimizer", None)
-        context["training"] = {
-            "steps": cfg.steps,
-            "batch_size": cfg.batch_size,
-            "seed": cfg.seed,
-            "optimizer": getattr(optimizer, "type", None) if optimizer else None,
-            "lr": getattr(optimizer, "lr", None) if optimizer else None,
-            "lerobot_version": __version__,
-        }
-
-    if dataset_meta is not None:
-        context["dataset"] = {
-            "repo_id": dataset_meta.repo_id,
-            "episodes": dataset_meta.total_episodes,
-            "frames": dataset_meta.total_frames,
-            "fps": dataset_meta.fps,
-            "tasks": [str(task) for task in dataset_meta.tasks.index],
-        }
-        context["robot_type"] = dataset_meta.robot_type
-        context["cameras"] = [key.split(".")[-1] for key in dataset_meta.camera_keys]
-
-    return context
-
-
-def generate_model_card(
-    model_cfg: PreTrainedConfig | RewardModelConfig,
-    cfg: TrainPipelineConfig | None = None,
-    dataset_meta: "LeRobotDatasetMetadata | None" = None,
-) -> ModelCard:
-    """Render the LeRobot model card for a trained policy or reward model.
-
-    A free function on purpose: every template variable comes from arguments — the model
-    config, the training config, and the dataset metadata — none from a live model, so a card
-    can also be rendered from a checkpoint's `config.json` alone (see `lerobot-convert-dcp`).
-    The config type selects the template: reward models get the reward-model card, policies the
-    policy card with the training/dataset sections.
-
-    Args:
-        model_cfg (PreTrainedConfig | RewardModelConfig): The model config providing type,
-            license, tags, repo id, and — for policies — the feature declarations.
-        cfg (TrainPipelineConfig | None, optional): The training config for the training and
-            dataset card sections. Defaults to None.
-        dataset_meta (LeRobotDatasetMetadata | None, optional): Dataset metadata for the
-            dataset card sections. Defaults to None.
-
-    Returns:
-        ModelCard: The rendered and validated LeRobot model card.
-    """
-    model_type = model_cfg.type
-    base_model = _BASE_MODEL_MAPPING.get(model_type)
-
-    if isinstance(model_cfg, RewardModelConfig):
-        tags = {"robotics", "lerobot", "reward-model", model_type}
-        template_card = (
-            files("lerobot.templates")
-            .joinpath("lerobot_rewardmodel_modelcard_template.md")
-            .read_text("utf-8")
-        )
-        context: dict[str, Any] = {}  # the reward template renders from card_data alone
-    else:
-        tags = {"robotics", "lerobot", model_type}
-        template_card = (
-            files("lerobot.templates").joinpath("lerobot_modelcard_template.md").read_text("utf-8")
-        )
-        context = build_card_context(cfg, dataset_meta, model_cfg.input_features, model_cfg.output_features)
-        # Used by the template to pre-fill commands and the "Fine-tuned from" line.
-        context["policy_repo_id"] = model_cfg.repo_id
-        context["base_model"] = base_model
-
-    card_data = ModelCardData(
-        license=model_cfg.license or "apache-2.0",
-        library_name="lerobot",
-        pipeline_tag="robotics",
-        tags=list(tags.union(model_cfg.tags or [])),
-        model_name=model_type,
-        datasets=cfg.dataset.repo_id if cfg is not None else None,
-        base_model=base_model,
-    )
-    card = ModelCard.from_template(card_data, template_str=template_card, **context)
-    card.validate()
-    return card

src/lerobot/configs/accelerator.py

@@ -1,273 +0,0 @@
-#!/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.
-"""Execution-runtime configuration: everything handed to (or applied by) the `Accelerator`.
-
-Each sub-config mirrors the plain-typed subset of the corresponding accelerate object and
-builds it at runtime (the way ``OptimizerConfig.build()`` constructs a ``torch.optim.Optimizer``),
-so the whole tree round-trips through the CLI and ``train_config.json`` and parsing a config
-never imports accelerate.
-"""
-
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import TYPE_CHECKING
-
-from lerobot.configs.parallelism import ParallelismConfig
-
-if TYPE_CHECKING:
-    from accelerate import Accelerator
-    from accelerate.utils import (
-        DistributedDataParallelKwargs,
-        FullyShardedDataParallelPlugin,
-        GradientAccumulationPlugin,
-    )
-
-
-@dataclass
-class FSDPConfig:
-    """Mirror of the `FullyShardedDataParallelPlugin` subset LeRobot supports (FSDP2 only).
-
-    Exactly one wrap policy applies: `wrap_modules` (module *class names* forming the FSDP
-    units — and, later, the activation-checkpointing units) or `min_num_params` (size-based).
-    When both are None, the policy's own `_fsdp_wrap_modules` declaration is used; a run where
-    no wrap source exists at all fails loudly rather than silently wrapping only the root.
-    """
-
-    reshard_after_forward: bool = True
-    wrap_modules: list[str] | None = None
-    min_num_params: int | None = None
-    cpu_offload: bool = False
-    # Regex matched against module FQNs to exclude their parameters from sharding.
-    ignored_modules: str | None = None
-
-    def __post_init__(self) -> None:
-        """Validate the wrap-policy fields.
-
-        Raises:
-            ValueError: If both ``wrap_modules`` and ``min_num_params`` are set (they are
-                mutually exclusive wrap policies), or if ``min_num_params`` is < 1.
-        """
-        if self.wrap_modules is not None and self.min_num_params is not None:
-            raise ValueError(
-                "fsdp.wrap_modules and fsdp.min_num_params are mutually exclusive wrap policies."
-            )
-        if self.min_num_params is not None and self.min_num_params < 1:
-            raise ValueError(f"fsdp.min_num_params must be >= 1, got {self.min_num_params}.")
-
-    def build_plugin(self) -> "FullyShardedDataParallelPlugin":
-        """Build the FSDP2 plugin for `Accelerator(fsdp_plugin=...)`.
-
-        Returns:
-            FullyShardedDataParallelPlugin: FSDP2 (`fsdp_version=2`) plugin carrying the
-                mirrored wrap policy, resharding, CPU-offload, and ignored-modules settings.
-        """
-        from accelerate.utils import FullyShardedDataParallelPlugin
-
-        use_size_policy = self.min_num_params is not None
-        return FullyShardedDataParallelPlugin(
-            fsdp_version=2,
-            reshard_after_forward=self.reshard_after_forward,
-            auto_wrap_policy="size_based_wrap" if use_size_policy else "transformer_based_wrap",
-            # May legitimately still be None here: the policy-declared default is applied right
-            # before `accelerator.prepare()` (see lerobot.distributed.factory.set_fsdp_wrap_modules).
-            transformer_cls_names_to_wrap=list(self.wrap_modules) if self.wrap_modules else None,
-            min_num_params=self.min_num_params,
-            cpu_offload=self.cpu_offload,
-            ignored_modules=self.ignored_modules,
-            # state_dict_type stays at the FSDP2 default (SHARDED_STATE_DICT) and is never
-            # switched: full gathers go through torch's state-dict API, which does not consult
-            # the plugin. activation_checkpointing stays False: AC is LeRobot-owned.
-        )
-
-
-@dataclass
-class DDPConfig:
-    """Mirror of the `DistributedDataParallelKwargs` subset LeRobot exposes."""
-
-    # Today's in-script default, kept for models with conditional computation.
-    find_unused_parameters: bool = True
-    gradient_as_bucket_view: bool = False
-    static_graph: bool = False
-
-    def build_kwargs_handler(self) -> "DistributedDataParallelKwargs":
-        """Build the DDP kwargs handler for `Accelerator(kwargs_handlers=[...])`.
-
-        Returns:
-            DistributedDataParallelKwargs: Handler carrying the mirrored DDP fields, applied
-                by accelerate when it wraps the model in `DistributedDataParallel`.
-        """
-        from accelerate.utils import DistributedDataParallelKwargs
-
-        return DistributedDataParallelKwargs(
-            find_unused_parameters=self.find_unused_parameters,
-            gradient_as_bucket_view=self.gradient_as_bucket_view,
-            static_graph=self.static_graph,
-        )
-
-
-@dataclass
-class GradientAccumulationConfig:
-    """Mirror of the `GradientAccumulationPlugin` subset LeRobot supports.
-
-    Only the step count is a knob. ``sync_with_dataloader`` is pinned to False by
-    :meth:`build_plugin`: the training loop cycles a finite dataloader, so accelerate's default
-    of syncing at every dataloader end would force an optimizer step at every dataset epoch
-    boundary instead of every ``steps`` micro-batches.
-    """
-
-    steps: int = 1
-
-    def __post_init__(self) -> None:
-        """Validate the accumulation step count.
-
-        Raises:
-            ValueError: If ``steps`` is < 1.
-        """
-        if self.steps < 1:
-            raise ValueError(f"gradient_accumulation.steps must be >= 1, got {self.steps}.")
-
-    def build_plugin(self) -> "GradientAccumulationPlugin":
-        """Build the plugin for `Accelerator(gradient_accumulation_plugin=...)`.
-
-        A named plugin argument, not a `kwargs_handlers` entry: accelerate consumes this object
-        through its dedicated constructor parameter — the `KwargsHandler` base class only lends
-        it `to_kwargs()`, so the consumption site, not the inheritance, decides its role.
-
-        Returns:
-            GradientAccumulationPlugin: Carrying the mirrored step count, with
-                ``sync_with_dataloader=False`` pinned (see the class docstring).
-        """
-        from accelerate.utils import GradientAccumulationPlugin
-
-        return GradientAccumulationPlugin(num_steps=self.steps, sync_with_dataloader=False)
-
-
-@dataclass
-class CompileConfig:
-    """torch.compile knobs — a configured placeholder: wiring lands in a later round.
-
-    The setup-order contract it will follow is already fixed: compile applies
-    after CP dispatch install and activation checkpointing, before `fully_shard`, regionally
-    (per wrap unit) — the only combination proven with FSDP2.
-    """
-
-    enabled: bool = False
-    backend: str = "inductor"
-    mode: str | None = None
-    regional: bool = True
-
-
-class ActivationCheckpointingMode(str, Enum):
-    NONE = "none"
-    FULL = "full"
-
-
-@dataclass
-class ActivationCheckpointingConfig:
-    """Activation-checkpointing knobs — a configured placeholder: wiring lands in a later round.
-
-    AC units will coincide with the FSDP wrap units (one declaration drives both), applied
-    before torch.compile and `fully_shard` (the same ordering contract as CompileConfig).
-    """
-
-    mode: ActivationCheckpointingMode = ActivationCheckpointingMode.NONE
-
-
-@dataclass
-class AcceleratorConfig:
-    """Builds the `Accelerator` — the runtime counterpart of the `parallelism` topology.
-
-    `mixed_precision` selects accelerate-native AMP for DDP/single-GPU runs and the FSDP2
-    `MixedPrecisionPolicy` for sharded runs (accelerate derives it). Sharded runs support
-    "no" and "bf16" only; fp16's GradScaler-over-DTensor path is unverified and fails fast
-    at config validation.
-    """
-
-    mixed_precision: str = "no"
-    gradient_accumulation: GradientAccumulationConfig = field(default_factory=GradientAccumulationConfig)
-    fsdp: FSDPConfig = field(default_factory=FSDPConfig)
-    ddp: DDPConfig = field(default_factory=DDPConfig)
-    compile: CompileConfig = field(default_factory=CompileConfig)
-    activation_checkpointing: ActivationCheckpointingConfig = field(
-        default_factory=ActivationCheckpointingConfig
-    )
-
-    def __post_init__(self) -> None:
-        """Validate the accelerate-facing scalar fields.
-
-        Raises:
-            ValueError: If ``mixed_precision`` is not one of ``"no"``, ``"fp16"``, ``"bf16"``.
-        """
-        if self.mixed_precision not in ("no", "fp16", "bf16"):
-            raise ValueError(
-                f"mixed_precision must be one of 'no', 'fp16', 'bf16', got {self.mixed_precision!r}."
-            )
-
-    def build(self, parallelism: ParallelismConfig, *, cpu: bool = False) -> "Accelerator":
-        """Translate the mirrored fields into a ready `Accelerator` (call once per process).
-
-        `parallelism` must already be resolved against the world size. The degradation matrix
-        is encoded here and nowhere else: sharded -> FSDP2 (+HSDP via the accelerate
-        `ParallelismConfig` mesh), replicated-only -> DDP kwargs, single process -> plain.
-
-        Args:
-            parallelism (ParallelismConfig): The resolved process topology; selects which
-                accelerate path (FSDP2 mesh, DDP kwargs handler, or plain) is configured.
-            cpu (bool): Force CPU execution even when CUDA is available. Defaults to False.
-
-        Returns:
-            Accelerator: The configured accelerate entry point for this process.
-        """
-        from accelerate import Accelerator
-
-        kwargs: dict = {
-            # LeRobot steps its scheduler manually once per training step; accelerate must not
-            # rescale scheduler stepping by num_processes.
-            "step_scheduler_with_optimizer": False,
-            "gradient_accumulation_plugin": self.gradient_accumulation.build_plugin(),
-            "mixed_precision": self.mixed_precision,
-            "cpu": cpu,
-        }
-        if parallelism.is_sharded:
-            kwargs["fsdp_plugin"] = self.fsdp.build_plugin()
-            kwargs["parallelism_config"] = _accelerate_parallelism_config(parallelism)
-        elif parallelism.is_replicated_only:
-            kwargs["kwargs_handlers"] = [self.ddp.build_kwargs_handler()]
-        return Accelerator(**kwargs)
-
-
-def _accelerate_parallelism_config(parallelism: ParallelismConfig) -> object:
-    """LeRobot topology -> accelerate `ParallelismConfig`.
-
-    CP is declared honestly (`cp_size = ring x ulysses`) so accelerate builds the canonical
-    mesh, folds CP into the FSDP shard group (`dp_shard_cp`), and duplicates batches within CP
-    groups. The ring/ulysses sub-structure stays private to `lerobot.distributed.ParallelDims`.
-
-    Args:
-        parallelism (ParallelismConfig): The resolved LeRobot topology to translate.
-
-    Returns:
-        object: The accelerate `ParallelismConfig` mirroring `dp_replicate`, `dp_shard`, and
-            the collapsed `cp_size` (annotated as `object` so importing this module never
-            imports accelerate).
-    """
-    from accelerate.parallelism_config import ParallelismConfig as AccelerateParallelismConfig
-
-    return AccelerateParallelismConfig(
-        dp_replicate_size=parallelism.dp_replicate,
-        dp_shard_size=parallelism.dp_shard,
-        cp_size=parallelism.cp_size,
-    )

src/lerobot/configs/default.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
 from dataclasses import dataclass, field
 
 from lerobot.transforms import ImageTransformsConfig
@@ -22,8 +21,6 @@ from lerobot.utils.import_utils import get_safe_default_video_backend
 
 from .video import DEFAULT_DEPTH_UNIT, DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT
 
-logger = logging.getLogger(__name__)
-
 
 @dataclass
 class DatasetConfig:
@@ -39,8 +36,6 @@ class DatasetConfig:
     # looked up under $HF_LEROBOT_HOME/repo_id and Hub downloads use a revision-safe cache under $HF_LEROBOT_HOME/hub.
     root: str | None = None
     episodes: list[int] | None = None
-    # Episode indices to drop (e.g. corrupt or heterogeneous ones). Applied on top of `episodes`.
-    exclude_episodes: list[int] | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)
     revision: str | None = None
     use_imagenet_stats: bool = True
@@ -80,14 +75,6 @@ class DatasetConfig:
             if len(self.episodes) != len(set(self.episodes)):
                 duplicates = sorted({ep for ep in self.episodes if self.episodes.count(ep) > 1})
                 raise ValueError(f"Episode indices contain duplicates: {duplicates}")
-        if self.exclude_episodes is not None:
-            negative_episodes = [episode for episode in self.exclude_episodes if episode < 0]
-            if negative_episodes:
-                logger.warning(
-                    "Ignoring negative exclude_episodes entries: %s",
-                    negative_episodes,
-                )
-                self.exclude_episodes = [episode for episode in self.exclude_episodes if episode >= 0]
 
 
 @dataclass

src/lerobot/configs/parallelism.py

@@ -1,190 +0,0 @@
-#!/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.
-"""Declarative process topology for distributed training and inference.
-
-The mesh convention (canonical row-major rank layout, outermost first)::
-
-    (dp_replicate, dp_shard, ring, ulysses)
-
-- ``dp_replicate x dp_shard`` is the data-parallel world: HSDP replicates over
-  ``dp_replicate`` and shards parameters over ``dp_shard``. FSDP2's actual shard
-  group folds context parallelism in (``dp_shard x ring x ulysses``), matching
-  accelerate's ``dp_shard_cp`` flattening and torchtitan's ``fsdp`` axis.
-- ``ring`` is the outer and ``ulysses`` the inner context-parallel dim
-  (diffusers convention: ulysses all-to-all exchanges run over adjacent, typically
-  NVLink-connected ranks).
-- ``cfg_parallel`` (classifier-free-guidance parallelism) is a branch-parallel,
-  inference-only dim that sits between dp and the sequence dims. It never
-  affects weight sharding or checkpoints.
-
-This module is pure configuration: plain-typed dataclasses that draccus can
-round-trip through the CLI and ``train_config.json``. Runtime objects (device
-meshes, process groups) live in :mod:`lerobot.distributed`.
-"""
-
-import os
-from dataclasses import dataclass, field
-
-
-@dataclass
-class ContextParallelConfig:
-    """Ring x Ulysses context parallelism (sequence parallelism for attention).
-
-    Both degrees are configured placeholders in this release: the CP engine is not implemented
-    yet, and enabling either degree > 1 fails fast at config validation. The fields exist now so
-    that the CLI surface, checkpoint metadata, and mesh math are stable when the engine lands.
-    """
-
-    ring_degree: int = 1
-    ulysses_degree: int = 1
-
-    def __post_init__(self) -> None:
-        """Validate the declared context-parallel degrees.
-
-        Raises:
-            ValueError: If ``ring_degree`` or ``ulysses_degree`` is < 1.
-        """
-        if self.ring_degree < 1 or self.ulysses_degree < 1:
-            raise ValueError(
-                f"Context-parallel degrees must be >= 1, got ring_degree={self.ring_degree}, "
-                f"ulysses_degree={self.ulysses_degree}."
-            )
-
-    @property
-    def size(self) -> int:
-        """Total number of ranks a full sequence is sharded across."""
-        return self.ring_degree * self.ulysses_degree
-
-
-@dataclass
-class ParallelismConfig:
-    """Degrees of every parallelism dim. Invariant: their product equals the world size.
-
-    Degradations are expressed purely through the degrees (no mode flags):
-
-    - single process: all degrees 1;
-    - DDP: ``dp_replicate == world_size`` (auto-filled when every sharding field is left at its
-      default — plain ``torchrun`` keeps today's out-of-the-box behavior);
-    - FSDP: ``dp_shard > 1`` (or ``-1`` to fill the remaining world into the shard dim);
-    - HSDP: ``dp_replicate > 1`` and ``dp_shard > 1``.
-
-    ``resolve()`` turns the declared degrees into concrete ones once the world size is known and
-    is the single place the world-size equation is enforced. It is called by
-    :func:`lerobot.distributed.factory.make_accelerator`; the config is inert until then.
-    """
-
-    dp_replicate: int = 1
-    # -1 is an explicit opt-in sentinel: shard over world_size // (dp_replicate * cp).
-    dp_shard: int = 1
-    context_parallel: ContextParallelConfig = field(default_factory=ContextParallelConfig)
-    # Classifier-free-guidance parallelism — inference-only (cosmos/vllm-omni precedent:
-    # cond/uncond branches on different ranks). Reserved for the serving round; training
-    # validates it to 1. Meaningful values are 1 or 2 (Cosmos3 has two CFG branches).
-    cfg_parallel: int = 1
-
-    def __post_init__(self) -> None:
-        """Validate the declared degrees (world-size-independent checks only).
-
-        Raises:
-            ValueError: If ``dp_replicate`` is < 1, ``dp_shard`` is neither >= 1 nor the
-                ``-1`` infer sentinel, or ``cfg_parallel`` is not 1 or 2.
-        """
-        if self.dp_replicate < 1:
-            raise ValueError(f"dp_replicate must be >= 1, got {self.dp_replicate}.")
-        if self.dp_shard < 1 and self.dp_shard != -1:
-            raise ValueError(f"dp_shard must be >= 1, or -1 to infer, got {self.dp_shard}.")
-        if self.cfg_parallel not in (1, 2):
-            raise ValueError(f"cfg_parallel must be 1 or 2, got {self.cfg_parallel}.")
-
-    @property
-    def cp_size(self) -> int:
-        """Total context-parallel size (``ring_degree * ulysses_degree``)."""
-        return self.context_parallel.size
-
-    @property
-    def is_sharded(self) -> bool:
-        """True when the run uses FSDP2 (parameters sharded); selects the sharded engine path."""
-        return self.dp_shard != 1 or self.cp_size > 1
-
-    @property
-    def is_replicated_only(self) -> bool:
-        """True for plain DDP (weights replicated, no sharding)."""
-        return not self.is_sharded and self.dp_replicate > 1
-
-    @property
-    def dp_world_size(self) -> int:
-        """Number of distinct data-parallel workers (batches are sharded this many ways).
-
-        Returns:
-            int: ``dp_replicate * dp_shard``.
-
-        Raises:
-            RuntimeError: If accessed while ``dp_shard`` is still the ``-1`` sentinel, i.e.
-                before :meth:`resolve` has bound the degrees to a world size.
-        """
-        if self.dp_shard == -1:
-            raise RuntimeError("dp_world_size is undefined before resolve() fills dp_shard=-1.")
-        return self.dp_replicate * self.dp_shard
-
-    def resolve(self, world_size: int) -> None:
-        """Bind the declared degrees to a concrete world size (idempotent).
-
-        Fills the ``dp_shard=-1`` sentinel, auto-fills ``dp_replicate`` for the DDP degradation,
-        and enforces ``dp_replicate * dp_shard * cp == world_size`` with every degree echoed on
-        failure.
-
-        Args:
-            world_size (int): Total number of launched processes (torchrun's ``WORLD_SIZE``).
-
-        Raises:
-            ValueError: If a context-parallel degree is > 1 (the CP engine is not implemented
-                yet), if ``dp_shard=-1`` cannot be inferred because ``world_size`` is not
-                divisible by ``dp_replicate * cp``, or if the resolved degrees do not multiply
-                to ``world_size``.
-        """
-        if self.cp_size > 1:
-            raise ValueError(
-                "Context parallelism is not implemented yet: ring_degree and ulysses_degree "
-                "must be 1. The fields are reserved for the CP engine round."
-            )
-        if self.is_sharded:
-            if self.dp_shard == -1:
-                self.dp_shard, remainder = divmod(world_size, self.dp_replicate * self.cp_size)
-                if remainder or self.dp_shard < 1:
-                    raise ValueError(
-                        f"Cannot infer dp_shard: world_size={world_size} is not divisible by "
-                        f"dp_replicate={self.dp_replicate} * cp={self.cp_size}."
-                    )
-        elif self.dp_replicate == 1:
-            # Untouched config on a multi-process launch: fill the DDP degradation.
-            self.dp_replicate = world_size
-        total = self.dp_replicate * self.dp_shard * self.cp_size
-        if total != world_size:
-            raise ValueError(
-                f"Parallelism degrees do not multiply to the world size: dp_replicate="
-                f"{self.dp_replicate} * dp_shard={self.dp_shard} * ring="
-                f"{self.context_parallel.ring_degree} * ulysses="
-                f"{self.context_parallel.ulysses_degree} = {total} != WORLD_SIZE={world_size}."
-            )
-
-
-def world_size_from_env() -> int:
-    """World size as set by torchrun (or 1 outside distributed launches).
-
-    Returns:
-        int: The ``WORLD_SIZE`` environment variable, or 1 when unset.
-    """
-    return int(os.environ.get("WORLD_SIZE", "1"))

src/lerobot/configs/recipe.py

@@ -23,7 +23,6 @@ from typing import Any, Literal, get_args
 
 MessageRole = Literal["user", "assistant", "system", "tool"]
 MessageStream = Literal["high_level", "low_level"]
-RecipeRoute = Literal["vqa"]
 
 DEFAULT_BINDINGS = {
     "subtask": "active_at(t, style=subtask)",
@@ -41,7 +40,6 @@ discovery (here) and rendered-message substitution (in ``language_render``)."""
 
 _VALID_ROLES = frozenset(get_args(MessageRole))
 _VALID_STREAMS = frozenset(get_args(MessageStream))
-_VALID_ROUTES = frozenset(get_args(RecipeRoute))
 
 
 @dataclass
@@ -80,7 +78,7 @@ class MessageTurn:
             raise ValueError(f"Unsupported message stream: {self.stream!r}")
         if self.content is None and self.tool_calls_from is None:
             raise ValueError("MessageTurn.content is required unless tool_calls_from is set.")
-        if self.content is not None and not isinstance(self.content, str | list):
+        if self.content is not None and not isinstance(self.content, (str, list)):
             raise TypeError("MessageTurn.content must be a string, a list of HF-style blocks, or None.")
         if isinstance(self.content, list):
             for block in self.content:
@@ -101,16 +99,13 @@ class TrainingRecipe:
 
     A recipe is either a *message recipe* (``messages`` plus optional
     ``bindings``) or a *blend recipe* (``blend`` mapping names to weighted
-    sub-recipes). ``weight`` and ``route`` are only meaningful inside a blend;
-    ``route: vqa`` gives sparse VQA annotations priority over normal weighted
-    selection.
+    sub-recipes). ``weight`` is only meaningful inside a blend.
     """
 
     messages: list[MessageTurn] | None = None
     bindings: dict[str, str] | None = None
     blend: dict[str, TrainingRecipe] | None = None
     weight: float | None = None
-    route: RecipeRoute | None = None
 
     def __post_init__(self) -> None:
         """Validate that exactly one of ``messages`` or ``blend`` is set."""
@@ -118,10 +113,6 @@ class TrainingRecipe:
             raise ValueError("TrainingRecipe must set only one of messages or blend.")
         if self.messages is None and self.blend is None:
             raise ValueError("TrainingRecipe must set one of messages or blend.")
-        if self.route is not None and self.route not in _VALID_ROUTES:
-            raise ValueError(f"Unsupported recipe route: {self.route!r}")
-        if self.blend is not None and self.route is not None:
-            raise ValueError("TrainingRecipe.route may only be set on a message recipe inside a blend.")
 
         if self.messages is not None:
             self._validate_message_recipe()
@@ -156,9 +147,8 @@ class TrainingRecipe:
         return cls.from_dict(data)
 
     def _validate_message_recipe(self) -> None:
-        """Validate bindings and require text or low-level action supervision."""
-        if self.messages is None:
-            raise ValueError("Cannot validate a message recipe without messages.")
+        """Ensure every templated binding is known and at least one turn is a target."""
+        assert self.messages is not None
         known_bindings = set(DEFAULT_BINDINGS) | set(self.bindings or {}) | {"task"}
 
         for turn in self.messages:
@@ -166,19 +156,12 @@ class TrainingRecipe:
             if missing:
                 raise ValueError(f"MessageTurn references unknown binding(s): {sorted(missing)}")
 
-        has_target = any(turn.target for turn in self.messages)
-        has_low_level = any(turn.stream == "low_level" for turn in self.messages)
-        if not (has_target or has_low_level):
-            raise ValueError(
-                "Message recipes must contain at least one supervised turn — "
-                "either ``target: true`` (text CE) or ``stream: low_level`` "
-                "(flow/action loss)."
-            )
+        if not any(turn.target for turn in self.messages):
+            raise ValueError("Message recipes must contain at least one target turn.")
 
     def _validate_blend_recipe(self) -> None:
         """Ensure each blend component is a non-empty, weighted message recipe."""
-        if self.blend is None:
-            raise ValueError("Cannot validate a blend recipe without blend components.")
+        assert self.blend is not None
         if not self.blend:
             raise ValueError("Blend recipes must contain at least one component.")
 

src/lerobot/configs/recipes/subtask.yaml

@@ -1,16 +0,0 @@
-# Predicts subtasks from tasks and trains subtask-conditioned action flow without memory or plans.
-# Requires `subtask` annotations; samples with missing `if_present` bindings do not render.
-
-blend:
-
-  high_level_subtask:
-    weight: 0.30
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: assistant, content: "${subtask}", stream: high_level, target: true, if_present: subtask}
-
-  low_level_execution:
-    weight: 0.70
-    messages:
-      # The low-level stream trains action flow on the generated or annotated subtask.
-      - {role: user, content: "${subtask}", stream: low_level, if_present: subtask}

src/lerobot/configs/recipes/subtask_joint.yaml

@@ -1,13 +0,0 @@
-# Paper-style joint sequence (pi0.5 §IV-B): one sample supervises the subtask
-# text with CE and, because the assistant turn is part of the prefix, conditions
-# the FAST and flow action losses on the same annotated subtask in one forward.
-# The supervised span is attended causally; the action losses see task + subtask.
-#
-# Pair with `--policy.joint_subtask_conditioning=true` at inference so the flow
-# prefix reproduces this layout (task turn with state + causal generated subtask).
-# Samples without a `subtask` annotation fall back to a plain task-prompt
-# low-level sample via `if_present`.
-
-messages:
-  - {role: user, content: "${task}", stream: low_level}
-  - {role: assistant, content: "${subtask}", stream: low_level, target: true, if_present: subtask}

src/lerobot/configs/recipes/subtask_mem.yaml

@@ -1,30 +0,0 @@
-# Trains subtask prediction, subtask-conditioned action flow, and memory updates without plans.
-# Requires `subtask` and `memory`; missing `if_present` bindings skip the affected sub-recipe.
-
-blend:
-
-  high_level_subtask:
-    weight: 0.25
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: assistant, content: "${subtask}", stream: high_level, target: true, if_present: subtask}
-
-  low_level_execution:
-    weight: 0.60
-    messages:
-      # The low-level stream trains action flow on the generated or annotated subtask.
-      - {role: user, content: "${subtask}", stream: low_level, if_present: subtask}
-
-  memory_update:
-    # `active_at` densifies sparse boundaries while preserving the prior-memory/subtask mapping.
-    # Inference controls update timing through `subtask_change` events.
-    weight: 0.15
-    bindings:
-      prior_memory: "nth_prev(style=memory, offset=1)"
-      current_memory: "active_at(t, style=memory)"
-      completed_subtask: "nth_prev(style=subtask, offset=1)"
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: assistant, content: "Previous memory: ${prior_memory}", stream: high_level, if_present: prior_memory}
-      - {role: user, content: "Completed subtask: ${completed_subtask}", stream: high_level, if_present: completed_subtask}
-      - {role: assistant, content: "${current_memory}", stream: high_level, target: true, if_present: current_memory}

src/lerobot/configs/recipes/subtask_mem_vqa_speech.yaml

@@ -1,72 +0,0 @@
-# Adds memory, spoken interjection responses, and camera-grounded VQA to subtask/action training.
-# Missing optional annotations skip only their sub-recipe; `say` tool calls tokenize as `<say>...</say>`.
-
-blend:
-
-  high_level_subtask:
-    weight: 0.25
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: assistant, content: "${subtask}", stream: high_level, target: true, if_present: subtask}
-
-  low_level_execution:
-    weight: 0.40
-    messages:
-      # The low-level stream trains action flow on the generated or annotated subtask.
-      - {role: user, content: "${subtask}", stream: low_level, if_present: subtask}
-
-  memory_update:
-    # `active_at` densifies sparse boundaries while preserving the prior-memory/subtask mapping.
-    # Inference controls update timing through `subtask_change` events.
-    weight: 0.10
-    bindings:
-      prior_memory: "nth_prev(style=memory, offset=1)"
-      current_memory: "active_at(t, style=memory)"
-      completed_subtask: "nth_prev(style=subtask, offset=1)"
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: assistant, content: "Previous memory: ${prior_memory}", stream: high_level, if_present: prior_memory}
-      - {role: user, content: "Completed subtask: ${completed_subtask}", stream: high_level, if_present: completed_subtask}
-      - {role: assistant, content: "${current_memory}", stream: high_level, target: true, if_present: current_memory}
-
-  user_interjection_response:
-    weight: 0.10
-    bindings:
-      interjection: "emitted_at(t, style=interjection)"
-      speech: "emitted_at(t, role=assistant, tool_name=say)"
-    messages:
-      - {role: user, content: "${task}", stream: high_level}
-      - {role: user, content: "${interjection}", stream: high_level, if_present: interjection}
-      # The assistant target is a `say` tool call flattened to a `<say>...</say>` marker.
-      - {role: assistant, stream: high_level, target: true, if_present: speech, tool_calls_from: speech}
-
-  # Each camera uses a separate VQA sub-recipe for view-specific binding.
-  ask_vqa_top:
-    weight: 0.075
-    route: vqa
-    bindings:
-      vqa_query: "emitted_at(t, style=vqa, role=user, camera=observation.images.front)"
-      vqa: "emitted_at(t, style=vqa, role=assistant, camera=observation.images.front)"
-    messages:
-      - role: user
-        stream: high_level
-        if_present: vqa_query
-        content:
-          - {type: image, feature: observation.images.front}
-          - {type: text, text: "${vqa_query}"}
-      - {role: assistant, content: "${vqa}", stream: high_level, target: true, if_present: vqa}
-
-  ask_vqa_wrist:
-    weight: 0.075
-    route: vqa
-    bindings:
-      vqa_query: "emitted_at(t, style=vqa, role=user, camera=observation.images.wrist)"
-      vqa: "emitted_at(t, style=vqa, role=assistant, camera=observation.images.wrist)"
-    messages:
-      - role: user
-        stream: high_level
-        if_present: vqa_query
-        content:
-          - {type: image, feature: observation.images.wrist}
-          - {type: text, text: "${vqa_query}"}
-      - {role: assistant, content: "${vqa}", stream: high_level, target: true, if_present: vqa}

src/lerobot/configs/train.py

@@ -18,7 +18,6 @@ import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
-from enum import Enum
 from pathlib import Path
 from typing import Any
 
@@ -27,8 +26,6 @@ from huggingface_hub import hf_hub_download
 from huggingface_hub.errors import HfHubHTTPError
 
 from lerobot import envs
-from lerobot.configs.accelerator import AcceleratorConfig, ActivationCheckpointingMode
-from lerobot.configs.parallelism import ParallelismConfig
 from lerobot.optim import LRSchedulerConfig, OptimizerConfig
 from lerobot.utils.constants import PRETRAINED_MODEL_DIR
 from lerobot.utils.hub import HubMixin, find_latest_hub_checkpoint
@@ -42,34 +39,6 @@ from .rewards import RewardModelConfig
 TRAIN_CONFIG_NAME = "train_config.json"
 
 
-class CheckpointFormat(str, Enum):
-    """Model-artifact format inside training checkpoints.
-
-    Selects only the *model* artifact; the training_state layout is format-independent (the
-    optimizer channel is always DCP under sharded runs, safetensors+json otherwise).
-
-    - SAFETENSORS (default): a full `model.safetensors` — maximum compatibility, one gather per
-      save under sharding.
-    - DCP: sharded `pytorch_model_fsdp_0/*.distcp` only — fastest save/resume; convert with
-      `lerobot-convert-dcp` before distributing.
-    - SAFETENSORS_AND_DCP: both artifacts, written independently.
-    """
-
-    SAFETENSORS = "safetensors"
-    DCP = "dcp"
-    SAFETENSORS_AND_DCP = "safetensors_dcp"
-
-    @property
-    def wants_safetensors(self) -> bool:
-        """True when a full `model.safetensors` artifact should be written."""
-        return self in (CheckpointFormat.SAFETENSORS, CheckpointFormat.SAFETENSORS_AND_DCP)
-
-    @property
-    def wants_dcp(self) -> bool:
-        """True when sharded DCP model shards (`pytorch_model_fsdp_0/`) should be written."""
-        return self in (CheckpointFormat.DCP, CheckpointFormat.SAFETENSORS_AND_DCP)
-
-
 def _migrate_legacy_rabc_fields(config: dict[str, Any]) -> dict[str, Any] | None:
     """Return migrated payload for legacy RA-BC fields, or None when no migration is needed."""
     legacy_fields = (
@@ -152,16 +121,9 @@ class TrainPipelineConfig(HubMixin):
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.
     # A non-positive value disables periodic saving, keeping only the final checkpoint.
     save_freq: int = 20_000
-    # Model-artifact format inside checkpoints; non-default values require a sharded run.
-    checkpoint_format: CheckpointFormat = CheckpointFormat.SAFETENSORS
     use_policy_training_preset: bool = True
     optimizer: OptimizerConfig | None = None
     scheduler: LRSchedulerConfig | None = None
-    # Process topology: dp_replicate / dp_shard (HSDP) and context-parallel degree placeholders.
-    parallelism: ParallelismConfig = field(default_factory=ParallelismConfig)
-    # Execution runtime handed to the Accelerator: mixed precision, gradient accumulation,
-    # FSDP/DDP tuning knobs, compile & activation-checkpointing placeholders.
-    accelerator: AcceleratorConfig = field(default_factory=AcceleratorConfig)
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
     peft: PeftConfig | None = None
@@ -329,60 +291,6 @@ class TrainPipelineConfig(HubMixin):
         if self.save_checkpoint_to_hub and not (self.policy is not None and self.policy.repo_id):
             raise ValueError("save_checkpoint_to_hub requires --policy.repo_id.")
 
-        self._validate_distributed()
-
-    def _validate_distributed(self) -> None:
-        """Fail-fasts for the distributed-training scope.
-
-        Raises:
-            ValueError: If the config requests anything outside the verified scope: context
-                parallelism or CFG parallelism (reserved placeholders), the compile or
-                activation-checkpointing placeholders, a DCP checkpoint format on a
-                non-sharded run, or — under sharded training — fp16 mixed precision, PEFT,
-                reward-model training, in-training environment evaluation, or multi-optimizer
-                configs.
-        """
-        if self.parallelism.cp_size > 1:
-            raise ValueError(
-                "Context parallelism is not implemented yet: --parallelism.context_parallel.* "
-                "degrees must be 1 (reserved for the CP engine round)."
-            )
-        if self.parallelism.cfg_parallel != 1:
-            raise ValueError(
-                "CFG parallelism is inference-only and must be 1 for training "
-                "(cfg_parallel is reserved for the serving round)."
-            )
-        if self.accelerator.compile.enabled:
-            raise ValueError("--accelerator.compile is a placeholder and not wired yet.")
-        if self.accelerator.activation_checkpointing.mode is not ActivationCheckpointingMode.NONE:
-            raise ValueError("--accelerator.activation_checkpointing is a placeholder and not wired yet.")
-        if self.checkpoint_format is not CheckpointFormat.SAFETENSORS and not self.parallelism.is_sharded:
-            raise ValueError(
-                f"checkpoint_format={self.checkpoint_format.value} requires a sharded run "
-                "(--parallelism.dp_shard != 1); non-sharded checkpoints are always safetensors."
-            )
-        if self.parallelism.is_sharded:
-            if self.accelerator.mixed_precision == "fp16":
-                raise ValueError(
-                    "fp16 is not supported under sharded training (GradScaler over DTensor "
-                    "gradients is unverified); use bf16 or full precision."
-                )
-            if self.peft is not None:
-                raise ValueError("PEFT is not supported under sharded training yet.")
-            if self.is_reward_model_training:
-                raise ValueError(
-                    "Reward-model training is not supported under sharded training yet "
-                    "(reward models declare no FSDP wrap units and have no sharded save path)."
-                )
-            if self.env is not None and self.env_eval_freq > 0:
-                raise ValueError(
-                    "In-training environment evaluation is not supported under sharded training "
-                    "(a rank-0-only rollout of a sharded model deadlocks on collectives); set "
-                    "--env_eval_freq=0 and evaluate with lerobot-eval on saved checkpoints."
-                )
-            if self.optimizer is not None and self.optimizer.builds_multiple_optimizers:
-                raise ValueError("Multi-optimizer configs are not supported under sharded training.")
-
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
         """Keys for draccus pretrained-path loading."""

src/lerobot/data_processing/sarm_annotations/subtask_annotation.py

@@ -76,7 +76,7 @@ import torch
 from pydantic import BaseModel, Field
 from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration
 
-from lerobot.datasets import LeRobotDataset, resolve_episode_indices
+from lerobot.datasets import LeRobotDataset
 
 
 # Pydantic Models for SARM Subtask Annotation
@@ -1049,10 +1049,7 @@ def main():
     torch_dtype = {"bfloat16": torch.bfloat16, "float16": torch.float16, "float32": torch.float32}[args.dtype]
 
     # Determine episodes
-    resolved_episodes = resolve_episode_indices(args.episodes, dataset.meta.total_episodes)
-    episode_indices = (
-        resolved_episodes if resolved_episodes is not None else list(range(dataset.meta.total_episodes))
-    )
+    episode_indices = args.episodes or list(range(dataset.meta.total_episodes))
 
     existing_annotations = load_annotations_from_dataset(dataset.root, prefix="sparse")
     if args.skip_existing:

src/lerobot/datasets/__init__.py

@@ -52,7 +52,7 @@ from .pipeline_features import aggregate_pipeline_dataset_features, create_initi
 from .pyav_utils import check_video_encoder_parameters_pyav, detect_available_encoders_pyav
 from .sampler import EpisodeAwareSampler, compute_sampler_state
 from .streaming_dataset import StreamingLeRobotDataset
-from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card, resolve_episode_indices
+from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card
 from .video_utils import VideoEncodingManager
 
 # NOTE: Low-level I/O functions (cast_stats_to_numpy, get_parquet_file_size_in_mb, etc.)
@@ -97,7 +97,6 @@ __all__ = [
     "reencode_dataset",
     "remove_feature",
     "resolve_delta_timestamps",
-    "resolve_episode_indices",
     "safe_stop_image_writer",
     "split_dataset",
     "write_stats",

src/lerobot/datasets/dataset_reader.py

@@ -39,7 +39,6 @@ from .io_utils import (
     hf_transform_to_torch,
     load_nested_dataset,
 )
-from .utils import resolve_episode_indices
 from .video_utils import decode_video_frames
 
 
@@ -84,7 +83,7 @@ class DatasetReader:
         """
         self._meta = meta
         self.root = root
-        self.episodes = resolve_episode_indices(episodes, meta.total_episodes)
+        self.episodes = episodes
         self._tolerance_s = tolerance_s
         self._video_backend = video_backend
         if image_transforms is not None and not callable(image_transforms):
@@ -164,34 +163,10 @@ class DatasetReader:
     def _load_hf_dataset(self) -> datasets.Dataset:
         """hf_dataset contains all the observations, states, actions, rewards, etc."""
         features = get_hf_features_from_features(self._meta.features)
-        self._validate_language_columns_declared(features)
         hf_dataset = load_nested_dataset(self.root / "data", features=features, episodes=self.episodes)
         hf_dataset.set_transform(hf_transform_to_torch)
         return hf_dataset
 
-    def _validate_language_columns_declared(self, features: datasets.Features) -> None:
-        """Require language columns stored in Parquet to be declared in metadata."""
-        # Leave empty datasets to fail through the normal loading path.
-        try:
-            sample = next((self.root / "data").glob("*/*.parquet"))
-        except StopIteration:
-            return
-
-        from pyarrow import parquet as _pq  # noqa: PLC0415
-
-        # LeRobot shards are schema-uniform, so one schema represents the dataset.
-        schema_names = set(_pq.read_schema(sample).names)
-        from .language import LANGUAGE_COLUMNS  # noqa: PLC0415
-
-        missing = sorted(set(LANGUAGE_COLUMNS) & schema_names - set(features))
-        if missing:
-            raise ValueError(
-                f"Dataset Parquet files contain language feature(s) missing from metadata: {missing}. "
-                "Metadata must describe the stored data; add the entries returned by "
-                "lerobot.datasets.language.language_feature_info() to meta/info.json['features'] "
-                "or rerun the annotation pipeline's metadata synchronization."
-            )
-
     def _check_cached_episodes_sufficient(self) -> bool:
         """Check if the cached dataset contains all requested episodes and their video files."""
         if self.hf_dataset is None or len(self.hf_dataset) == 0:

src/lerobot/datasets/factory.py

@@ -29,7 +29,6 @@ from .dataset_metadata import LeRobotDatasetMetadata
 from .lerobot_dataset import LeRobotDataset
 from .multi_dataset import MultiLeRobotDataset
 from .streaming_dataset import StreamingLeRobotDataset
-from .utils import resolve_episode_indices
 
 
 def resolve_delta_timestamps(
@@ -91,9 +90,6 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
             repo_type=cfg.dataset.repo_type,
         )
         delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, ds_meta)
-        episodes = resolve_episode_indices(
-            cfg.dataset.episodes, ds_meta.total_episodes, cfg.dataset.exclude_episodes
-        )
         if not cfg.dataset.streaming:
             if cfg.dataset.repo_type == "bucket":
                 raise ValueError(
@@ -102,7 +98,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
             dataset = LeRobotDataset(
                 cfg.dataset.repo_id,
                 root=cfg.dataset.root,
-                episodes=episodes,
+                episodes=cfg.dataset.episodes,
                 delta_timestamps=delta_timestamps,
                 image_transforms=image_transforms,
                 revision=cfg.dataset.revision,
@@ -115,7 +111,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
             dataset = StreamingLeRobotDataset(
                 cfg.dataset.repo_id,
                 root=cfg.dataset.root,
-                episodes=episodes,
+                episodes=cfg.dataset.episodes,
                 delta_timestamps=delta_timestamps,
                 image_transforms=image_transforms,
                 revision=cfg.dataset.revision,

src/lerobot/datasets/language_render.py

@@ -162,32 +162,14 @@ def render_sample(
     task: str | None = None,
     dataset_ctx: Any | None = None,
 ) -> RenderedMessages | None:
-    """Render recipe-defined messages and supervision for one dataset sample.
+    """Render the chat-style messages for a single dataset sample.
 
-    Resolves bindings against ``persistent`` and ``events`` at frame timestamp
-    ``t``. Blend recipes first route matching sparse VQA annotations, then use
-    deterministic weighted selection for the remaining samples. Returns
-    ``None`` when the selected recipe provides no text or low-level action
-    supervision for this sample.
+    Resolves the recipe's bindings against ``persistent`` and ``events`` rows
+    at frame timestamp ``t``, then expands the recipe's message templates.
+    Returns ``None`` if the resolved sample contains no target message.
     """
     persistent_rows = _normalize_rows(persistent or [])
     event_rows = _normalize_rows(events or [])
-
-    # Route sparse VQA frames to a matching view-specific component before weighted selection.
-    # This avoids dropping annotated frames or selecting VQA without annotations.
-    if recipe.blend is not None:
-        vqa_rendered = _render_vqa_if_present(
-            recipe,
-            persistent=persistent_rows,
-            events=event_rows,
-            t=t,
-            sample_idx=sample_idx,
-            task=task,
-            dataset_ctx=dataset_ctx,
-        )
-        if vqa_rendered is not None:
-            return vqa_rendered
-
     selected_recipe = _select_recipe(recipe, sample_idx)
     bindings = _resolve_bindings(
         selected_recipe,
@@ -201,58 +183,6 @@ def render_sample(
     return _render_message_recipe(selected_recipe, bindings)
 
 
-def _render_vqa_if_present(
-    recipe: TrainingRecipe,
-    *,
-    persistent: Sequence[LanguageRow],
-    events: Sequence[LanguageRow],
-    t: float,
-    sample_idx: int,
-    task: str | None,
-    dataset_ctx: Any | None,
-) -> RenderedMessages | None:
-    """Render a matching VQA component, or return ``None`` for normal selection.
-
-    Multiple matching views are selected deterministically by relative weight.
-    """
-    if recipe.blend is None:
-        return None
-    renderable: list[tuple[float, RenderedMessages]] = []
-    for component in recipe.blend.values():
-        if component.route != "vqa":
-            continue
-        bindings = _resolve_bindings(
-            component,
-            persistent=persistent,
-            events=events,
-            t=t,
-            sample_idx=sample_idx,
-            task=task,
-            dataset_ctx=dataset_ctx,
-        )
-        rendered = _render_message_recipe(component, bindings)
-        if rendered is not None:
-            if component.weight is None:
-                raise ValueError("Routed VQA blend components must define a weight.")
-            renderable.append((component.weight, rendered))
-
-    if not renderable:
-        return None
-    if len(renderable) == 1:
-        return renderable[0][1]
-
-    # Choose among matching cameras by their validated positive relative weights.
-    total = sum(weight for weight, _ in renderable)
-    digest = hashlib.blake2b(f"vqa:{sample_idx}".encode(), digest_size=8).digest()
-    draw = int.from_bytes(digest, "big") / 2**64 * total
-    cumulative = 0.0
-    for weight, rendered in renderable:
-        cumulative += weight
-        if draw < cumulative:
-            return rendered
-    return renderable[-1][1]
-
-
 def _select_recipe(recipe: TrainingRecipe, sample_idx: int) -> TrainingRecipe:
     """Pick a deterministic blend component for ``sample_idx`` (or return ``recipe``)."""
     if recipe.blend is None:
@@ -271,8 +201,7 @@ def _select_recipe(recipe: TrainingRecipe, sample_idx: int) -> TrainingRecipe:
         cumulative += component.weight or 0.0
         if draw < cumulative:
             return component
-    if last_component is None:
-        raise ValueError("Blend recipes must contain at least one component.")
+    assert last_component is not None
     return last_component
 
 
@@ -392,8 +321,7 @@ def _render_message_recipe(
     bindings: dict[str, LanguageRow | str | None],
 ) -> RenderedMessages | None:
     """Expand ``recipe.messages`` into rendered chat messages using ``bindings``."""
-    if recipe.messages is None:
-        raise ValueError("Cannot render a blend recipe as a message recipe.")
+    assert recipe.messages is not None
     messages: list[dict[str, Any]] = []
     streams: list[str | None] = []
     target_indices: list[int] = []
@@ -418,9 +346,7 @@ def _render_message_recipe(
         if turn.target:
             target_indices.append(message_idx)
 
-    # Keep samples with either text targets or low-level action supervision.
-    has_low_level = any(stream == "low_level" for stream in streams)
-    if not target_indices and not has_low_level:
+    if not target_indices:
         return None
 
     rendered = {
@@ -477,12 +403,14 @@ def _validate_rendered(rendered: RenderedMessages) -> None:
 
     if len(streams) != len(messages):
         raise ValueError("message_streams must be aligned with messages.")
-    # Require text or low-level action supervision.
-    if not target_indices and not any(s == "low_level" for s in streams):
-        raise ValueError("Rendered samples must contain a target message or a low_level-stream message.")
+    if not target_indices:
+        raise ValueError("Rendered samples must contain at least one target message.")
     for idx in target_indices:
         if idx < 0 or idx >= len(messages):
             raise ValueError(f"Target message index {idx} is out of bounds.")
+    # ``stream`` is enforced non-None at MessageTurn construction time
+    # (see ``MessageTurn.__post_init__``), so a missing stream here would
+    # mean the dataclass invariant was bypassed; no need to re-check.
 
 
 def _nth_relative(

src/lerobot/datasets/utils.py

@@ -18,7 +18,6 @@ import dataclasses
 import importlib.resources
 import json
 import logging
-from collections.abc import Sequence
 from dataclasses import dataclass, field
 from pathlib import Path
 
@@ -99,47 +98,6 @@ VIDEO_DIR = "videos"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 IMAGE_FILE_PATTERN = "frame-{frame_index:06d}.png"
-
-
-def resolve_episode_indices(
-    episodes: Sequence[int] | None,
-    total_episodes: int,
-    exclude_episodes: Sequence[int] | None = None,
-) -> list[int] | None:
-    """Resolve an optional episode allowlist and exclusion list against dataset bounds.
-
-    ``None`` is preserved when no filtering is requested so callers can retain
-    their native "all episodes" fast path. Invalid indices are ignored with a
-    warning, and the input order is preserved.
-    """
-    if total_episodes < 0:
-        raise ValueError(f"total_episodes must be non-negative, got {total_episodes}")
-
-    if episodes is None and not exclude_episodes:
-        return None
-
-    candidates = list(range(total_episodes)) if episodes is None else list(episodes)
-    invalid = [episode for episode in candidates if not 0 <= episode < total_episodes]
-    if invalid:
-        logger.warning(
-            "Ignoring episode indices outside the dataset range [0, %d): %s",
-            total_episodes,
-            invalid,
-        )
-    candidates = [episode for episode in candidates if 0 <= episode < total_episodes]
-
-    excluded = set(exclude_episodes or [])
-    invalid_excluded = sorted(episode for episode in excluded if not 0 <= episode < total_episodes)
-    if invalid_excluded:
-        logger.warning(
-            "Ignoring excluded episode indices outside the dataset range [0, %d): %s",
-            total_episodes,
-            invalid_excluded,
-        )
-    excluded = {episode for episode in excluded if 0 <= episode < total_episodes}
-    return [episode for episode in candidates if episode not in excluded]
-
-
 DEPTH_FILE_PATTERN = "frame-{frame_index:06d}.tiff"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"

src/lerobot/distributed/__init__.py

@@ -1,43 +0,0 @@
-#!/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.
-"""Distributed-training runtime for LeRobot.
-
-This package owns everything that turns the declarative topology in
-:class:`lerobot.configs.parallelism.ParallelismConfig` into a running engine:
-mesh math (:class:`~lerobot.distributed.parallel_dims.ParallelDims`), the
-`Accelerator` factory (:func:`~lerobot.distributed.factory.make_accelerator`),
-sharding-aware checkpoint helpers, and small rank utilities.
-
-Setup-order contract (normative):
-CP dispatch install -> activation checkpointing -> torch.compile ->
-``fully_shard``/DDP (via ``accelerator.prepare``) -> optimizer rebind.
-Only the last two steps are active today; CP/AC/compile are configured
-placeholders wired in later rounds.
-"""
-
-from .factory import guard_against_env_interference, make_accelerator, set_fsdp_wrap_modules
-from .parallel_dims import ParallelDims
-from .utils import finalize_sharded_policy, is_main_process, strip_accelerate_cp_hooks
-
-__all__ = [
-    "ParallelDims",
-    "finalize_sharded_policy",
-    "guard_against_env_interference",
-    "is_main_process",
-    "make_accelerator",
-    "set_fsdp_wrap_modules",
-    "strip_accelerate_cp_hooks",
-]

src/lerobot/distributed/checkpoint.py

@@ -1,195 +0,0 @@
-#!/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.
-"""Sharding-aware checkpoint primitives.
-
-Two artifact channels with distinct owners:
-
-- the **distributable** ``model.safetensors``: produced by ``PreTrainedPolicy.save_pretrained``
-  through :func:`full_model_state_dict` — a collective full gather when the model is sharded;
-- the **resume** channel (sharded runs): torch DCP directories written/read through accelerate's
-  ``save/load_fsdp_model`` and ``save/load_fsdp_optimizer`` (``pytorch_model_fsdp_0/`` and
-  ``optimizer_0/``, names imported from accelerate constants), which reshard on load across
-  topology changes.
-
-Every function that touches sharded state is a collective and must run on ALL ranks.
-"""
-
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import torch
-from torch import nn
-
-if TYPE_CHECKING:
-    from accelerate import Accelerator
-
-
-def is_sharded_module(module: nn.Module) -> bool:
-    """True when `fully_shard` owns this module's parameters (FSDP2's in-place class swap).
-
-    Args:
-        module (nn.Module): The module to inspect (a torch.compile wrapper is looked through
-            via `_orig_mod`).
-
-    Returns:
-        bool: True when the module (or its compiled `_orig_mod`) is an `FSDPModule`.
-    """
-    from torch.distributed.fsdp import FSDPModule
-
-    if isinstance(module, FSDPModule):
-        return True
-    # torch.compile wraps the sharded module; mirror accelerate's `_orig_mod` check.
-    orig_mod = getattr(module, "_orig_mod", None)
-    return orig_mod is not None and isinstance(orig_mod, FSDPModule)
-
-
-def full_model_state_dict(module: nn.Module) -> dict[str, torch.Tensor]:
-    """The module's full (unsharded) state dict, however its parameters are laid out.
-
-    Sharded modules gather through torch's DCP state-dict API: a COLLECTIVE that must run on
-    every rank; with ``cpu_offload=True`` the full dict materializes on the main rank only and
-    every other rank receives a literal ``{}`` (runtime-verified — a
-    rank-0-gated call deadlocks). Plain modules return ``module.state_dict()`` on every rank.
-
-    Args:
-        module (nn.Module): The (possibly sharded) module to read the state dict from.
-
-    Returns:
-        dict[str, torch.Tensor]: The full state dict — on the main rank only (``{}``
-            elsewhere) when the module is sharded, on every rank otherwise.
-    """
-    if not is_sharded_module(module):
-        return module.state_dict()
-
-    from torch.distributed.checkpoint.state_dict import StateDictOptions, get_model_state_dict
-
-    return get_model_state_dict(module, options=StateDictOptions(full_state_dict=True, cpu_offload=True))
-
-
-def _fsdp_plugin(accelerator: "Accelerator") -> object:
-    """The accelerator's FSDP plugin, required by every DCP save/load helper below.
-
-    Args:
-        accelerator (Accelerator): The accelerator that prepared the sharded model.
-
-    Returns:
-        object: The FSDP plugin held by `accelerator.state`.
-
-    Raises:
-        RuntimeError: If the accelerator was not configured with an FSDP plugin.
-    """
-    plugin = getattr(accelerator.state, "fsdp_plugin", None)
-    if plugin is None:
-        raise RuntimeError("Sharded checkpointing requires an FSDP-prepared Accelerator.")
-    return plugin
-
-
-def save_sharded_model(accelerator: "Accelerator", model: nn.Module, output_dir: Path) -> None:
-    """Write the DCP model shards (`pytorch_model_fsdp_0/`). Collective: call on all ranks.
-
-    Args:
-        accelerator (Accelerator): The accelerator that prepared the sharded model.
-        model (nn.Module): The prepared (sharded) model to save.
-        output_dir (Path): The directory the shard subdirectory is created in.
-    """
-    from accelerate.utils import save_fsdp_model
-
-    # accelerate 1.14's DCP helpers do string containment checks on the path:
-    # always hand them str, never Path.
-    save_fsdp_model(_fsdp_plugin(accelerator), accelerator, model, str(output_dir))
-
-
-def load_sharded_model(accelerator: "Accelerator", model: nn.Module, input_dir: Path) -> None:
-    """Load DCP model shards into the prepared (sharded) model. Collective: call on all ranks.
-
-    Args:
-        accelerator (Accelerator): The accelerator that prepared the sharded model.
-        model (nn.Module): The prepared (sharded) model to load into.
-        input_dir (Path): The directory containing the `pytorch_model_fsdp_0/` shard
-            subdirectory.
-    """
-    from accelerate.utils import load_fsdp_model
-    from accelerate.utils.constants import FSDP_MODEL_NAME
-
-    # Pass the exact shard directory: accelerate's load resolves it with a substring check
-    # ("pytorch_model_fsdp" in the path -> use as-is), which misfires on run paths that happen
-    # to contain the marker; the exact dir makes the check deterministic.
-    load_fsdp_model(_fsdp_plugin(accelerator), accelerator, model, str(input_dir / f"{FSDP_MODEL_NAME}_0"))
-
-
-def save_sharded_optimizer(
-    accelerator: "Accelerator", optimizer: torch.optim.Optimizer, model: nn.Module, output_dir: Path
-) -> None:
-    """Write the DCP optimizer shards (`optimizer_0/`). Collective: call on all ranks.
-
-    Args:
-        accelerator (Accelerator): The accelerator that prepared the model and optimizer.
-        optimizer (torch.optim.Optimizer): The prepared optimizer to save the state from.
-        model (nn.Module): The prepared (sharded) model the optimizer state is keyed by.
-        output_dir (Path): The directory the shard subdirectory is created in.
-    """
-    from accelerate.utils import save_fsdp_optimizer
-
-    save_fsdp_optimizer(_fsdp_plugin(accelerator), accelerator, optimizer, model, str(output_dir))
-
-
-def load_sharded_optimizer(
-    accelerator: "Accelerator", optimizer: torch.optim.Optimizer, model: nn.Module, input_dir: Path
-) -> None:
-    """Load DCP optimizer shards into the prepared optimizer. Collective: call on all ranks.
-
-    Must run AFTER ``accelerator.prepare()``: FSDP2's prepare rebinds the optimizer's param
-    groups to sharded DTensors but never migrates ``optimizer.state`` — the resharding load is
-    the only correct way to restore it.
-
-    Args:
-        accelerator (Accelerator): The accelerator that prepared the model and optimizer.
-        optimizer (torch.optim.Optimizer): The prepared optimizer to restore the state into.
-        model (nn.Module): The prepared (sharded) model the optimizer state is keyed by.
-        input_dir (Path): The directory containing the `optimizer_0/` shard subdirectory.
-    """
-    from accelerate.utils import load_fsdp_optimizer
-    from accelerate.utils.constants import OPTIMIZER_NAME
-
-    # Exact shard directory for the same reason as load_sharded_model: accelerate's substring
-    # check ("optimizer" in the path) would misread e.g. --job_name=optimizer_sweep run paths.
-    load_fsdp_optimizer(
-        _fsdp_plugin(accelerator), accelerator, optimizer, model, str(input_dir / f"{OPTIMIZER_NAME}_0")
-    )
-
-
-def dcp_to_safetensors(dcp_dir: Path, output_dir: Path, *, delete_dcp: bool = False) -> Path:
-    """Merge a DCP shard directory into a single `model.safetensors` (offline, single process).
-
-    Thin wrapper over `accelerate.utils.merge_fsdp_weights`, which loads the shards without a
-    process group, writes safetensors directly, and — when asked — removes the merged shard
-    directory itself, only on the main process and only once the merge has succeeded.
-
-    Args:
-        dcp_dir (Path): The DCP shard directory to merge (e.g. `.../pytorch_model_fsdp_0`).
-        output_dir (Path): The directory the merged `model.safetensors` is written into.
-        delete_dcp (bool): Whether to remove the shard directory once it has been merged.
-            Defaults to False.
-
-    Returns:
-        Path: The written `model.safetensors` file's path.
-    """
-    from accelerate.utils import merge_fsdp_weights
-
-    merge_fsdp_weights(
-        str(dcp_dir), str(output_dir), safe_serialization=True, remove_checkpoint_dir=delete_dcp
-    )
-    return output_dir / "model.safetensors"

src/lerobot/distributed/factory.py

@@ -1,147 +0,0 @@
-#!/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.
-"""The `Accelerator` factory — the only place accelerate gets configured.
-
-`torchrun` is the launcher; every accelerate parameter comes from `TrainPipelineConfig`
-(`cfg.parallelism` + `cfg.accelerator`) so a run is reproducible from its `train_config.json`
-alone. `accelerate launch` without a `--config_file` remains equivalent (it only sets rendezvous
-env vars in that mode); the yaml flow is superseded.
-"""
-
-import os
-from typing import TYPE_CHECKING
-
-from lerobot.configs.parallelism import world_size_from_env
-from lerobot.configs.train import TrainPipelineConfig
-
-if TYPE_CHECKING:
-    from accelerate import Accelerator
-
-    from lerobot.policies.pretrained import PreTrainedPolicy
-
-# Env vars through which `accelerate launch --config_file` (or a stray shell) would configure
-# accelerate behind the config system's back. Plugin `__post_init__`s read these silently as
-# field fallbacks (ACCELERATE_DYNAMO_* enables torch.compile through the default
-# TorchDynamoPlugin; ACCELERATE_GRADIENT_ACCUMULATION_STEPS overrides the explicitly passed
-# value inside Accelerator.__init__), which would make train_config.json lie about what ran.
-_ACCELERATE_ENV_PREFIXES = ("FSDP_", "PARALLELISM_CONFIG_", "ACCELERATE_DYNAMO_")
-_ACCELERATE_ENV_VARS = (
-    "ACCELERATE_USE_FSDP",
-    "ACCELERATE_USE_PARALLELISM_CONFIG",
-    "ACCELERATE_GRADIENT_ACCUMULATION_STEPS",
-)
-_ENV_OVERRIDE = "LEROBOT_ALLOW_ACCELERATE_ENV"
-
-
-def guard_against_env_interference() -> None:
-    """Hard-error when accelerate-configuring env vars are set.
-
-    A silently env-overridden "reproducible" config is worse than a stop: users migrating from
-    the old `accelerate launch --config_file fsdp.yaml` flow get a precise error instead of a
-    config that lies. Set LEROBOT_ALLOW_ACCELERATE_ENV=1 to acknowledge and proceed.
-
-    Raises:
-        RuntimeError: If any accelerate-configuring environment variable is set and the
-            LEROBOT_ALLOW_ACCELERATE_ENV override is not.
-    """
-    if os.environ.get(_ENV_OVERRIDE):
-        return
-    offending = sorted(
-        name
-        for name in os.environ
-        if name in _ACCELERATE_ENV_VARS or name.startswith(_ACCELERATE_ENV_PREFIXES)
-    )
-    if offending:
-        raise RuntimeError(
-            f"Accelerate-configuring environment variables are set: {', '.join(offending)}. "
-            "LeRobot manages accelerate exclusively through TrainPipelineConfig "
-            "(--parallelism.* / --accelerator.*); launch with plain torchrun and remove these "
-            "variables (the `accelerate launch --config_file` flow is superseded), or set "
-            f"{_ENV_OVERRIDE}=1 to acknowledge that they may override your config."
-        )
-
-
-def make_accelerator(cfg: TrainPipelineConfig) -> "Accelerator":
-    """Resolve the topology against the launched world and build the `Accelerator`.
-
-    Must run once per process, before any other component needs the device or the process
-    group (`Accelerator.__init__` initializes both and builds the device mesh).
-
-    Args:
-        cfg (TrainPipelineConfig): The full training config; `cfg.parallelism` is resolved in
-            place against the launched world size and `cfg.accelerator` builds the result.
-
-    Returns:
-        Accelerator: The configured accelerator, with device and process group initialized.
-
-    Raises:
-        ValueError: If `cfg.checkpoint_format` requires DCP but the topology resolved to a
-            non-sharded run.
-    """
-    guard_against_env_interference()
-    cfg.parallelism.resolve(world_size_from_env())
-    # The parse-time format check ran against the declared degrees, where the dp_shard=-1
-    # sentinel counts as sharded; it may resolve to an unsharded run (e.g. -1 at world size 1).
-    # Re-check against the concrete degrees so the recorded format never lies about the
-    # artifacts a checkpoint will actually contain.
-    if cfg.checkpoint_format.wants_dcp and not cfg.parallelism.is_sharded:
-        raise ValueError(
-            f"checkpoint_format={cfg.checkpoint_format.value} requires a sharded run, but the "
-            f"topology resolved to a non-sharded one (dp_replicate={cfg.parallelism.dp_replicate}, "
-            f"dp_shard={cfg.parallelism.dp_shard}); non-sharded checkpoints are always safetensors."
-        )
-    return cfg.accelerator.build(
-        cfg.parallelism,
-        cpu=cfg.trainable_config.device == "cpu",
-    )
-
-
-def set_fsdp_wrap_modules(accelerator: "Accelerator", policy: "PreTrainedPolicy") -> None:
-    """Resolve the FSDP wrap-unit class names onto the plugin before `accelerator.prepare()`.
-
-    Resolution order: user override (`--accelerator.fsdp.wrap_modules`, already on the plugin)
-    -> the policy's `_fsdp_wrap_modules` declaration -> hard error. Root-only wrapping — the
-    silent default when no wrap source exists — is never accepted: it quietly forfeits all
-    sharding memory savings.
-
-    No-op for the size-based policy (`--accelerator.fsdp.min_num_params`), which needs no class
-    names, and for non-sharded runs (no fsdp plugin).
-
-    Args:
-        accelerator (Accelerator): The accelerator whose FSDP plugin receives the wrap-unit
-            class names.
-        policy (PreTrainedPolicy): The trainable whose class may declare `_fsdp_wrap_modules`.
-
-    Raises:
-        ValueError: If sharded class-based wrapping is configured but neither a user override
-            nor a policy declaration supplies wrap-unit class names.
-    """
-    plugin = getattr(accelerator.state, "fsdp_plugin", None)
-    if plugin is None or plugin.min_num_params:
-        return
-    if plugin.transformer_cls_names_to_wrap:  # user override, set at build time
-        return
-    # getattr, not attribute access: non-policy trainables (no `_fsdp_wrap_modules` attribute)
-    # must reach the actionable error below, not an AttributeError.
-    declared = getattr(type(policy), "_fsdp_wrap_modules", None)
-    if not declared:
-        raise ValueError(
-            f"Policy '{type(policy).__name__}' declares no FSDP wrap units. Sharded training "
-            "requires wrap-unit class names: set --accelerator.fsdp.wrap_modules='[\"MyBlock\"]' "
-            "(or --accelerator.fsdp.min_num_params for a size-based policy), or declare "
-            "`_fsdp_wrap_modules` on the policy class."
-        )
-    plugin.transformer_cls_names_to_wrap = list(declared)

src/lerobot/distributed/parallel_dims.py

@@ -1,112 +0,0 @@
-#!/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.
-"""Runtime mesh math derived from the declarative :class:`ParallelismConfig`.
-
-`ParallelDims` is the training script's single source of truth for topology-derived numbers
-(data-parallel world size and rank, sample accounting inputs) and — once the CP engine lands —
-the owner of LeRobot's private ``(dp_replicate, dp_shard, ring, ulysses)`` mesh. It is a runtime
-object and is never serialized (the config it derives from is what lands in
-``train_config.json``).
-"""
-
-from dataclasses import dataclass
-
-import torch.distributed as dist
-
-from lerobot.configs.parallelism import ParallelismConfig
-
-
-@dataclass(frozen=True)
-class ParallelDims:
-    """Concrete parallelism degrees bound to a world size (canonical row-major rank layout)."""
-
-    dp_replicate: int
-    dp_shard: int
-    ring: int
-    ulysses: int
-    world_size: int
-    device_type: str
-
-    @classmethod
-    def from_config(cls, cfg: ParallelismConfig, world_size: int, device_type: str) -> "ParallelDims":
-        """Bind a *resolved* config to the actual runtime world size (cross-checked here).
-
-        Args:
-            cfg (ParallelismConfig): The declarative topology, already resolved via
-                `ParallelismConfig.resolve(world_size)`.
-            world_size (int): The launched world size the declared degrees must multiply to.
-            device_type (str): The accelerator device type backing the mesh (e.g. "cuda").
-
-        Returns:
-            ParallelDims: The concrete parallelism degrees bound to this world.
-
-        Raises:
-            ValueError: If the config is unresolved (`dp_shard == -1`) or its degrees do not
-                multiply to `world_size`.
-        """
-        total = cfg.dp_replicate * cfg.dp_shard * cfg.cp_size
-        if cfg.dp_shard == -1 or total != world_size:
-            raise ValueError(
-                f"ParallelismConfig is not resolved against this world: dp_replicate="
-                f"{cfg.dp_replicate} * dp_shard={cfg.dp_shard} * cp={cfg.cp_size} != "
-                f"world_size={world_size}. Call ParallelismConfig.resolve(world_size) first "
-                "(make_accelerator does this)."
-            )
-        return cls(
-            dp_replicate=cfg.dp_replicate,
-            dp_shard=cfg.dp_shard,
-            ring=cfg.context_parallel.ring_degree,
-            ulysses=cfg.context_parallel.ulysses_degree,
-            world_size=world_size,
-            device_type=device_type,
-        )
-
-    @property
-    def cp_size(self) -> int:
-        """Total context-parallel degree (`ring * ulysses`)."""
-        return self.ring * self.ulysses
-
-    @property
-    def is_sharded(self) -> bool:
-        """Whether parameters are sharded (`dp_shard > 1` or any context parallelism)."""
-        return self.dp_shard > 1 or self.cp_size > 1
-
-    @property
-    def dp_world_size(self) -> int:
-        """Number of distinct data-parallel workers — the divisor for all sample accounting."""
-        return self.dp_replicate * self.dp_shard
-
-    @property
-    def dp_rank(self) -> int:
-        """This process's data-parallel coordinate (CP peers share one dp_rank).
-
-        With the canonical row-major layout and (ring, ulysses) innermost, CP peers are
-        contiguous global ranks, so the dp coordinate is the integer quotient by cp_size —
-        the same arithmetic accelerate's mesh-aware dataloader applies.
-        """
-        global_rank = dist.get_rank() if dist.is_initialized() else 0
-        return global_rank // self.cp_size
-
-    def cp_mesh(self) -> None:
-        """Private (ring, ulysses) mesh for the CP engine — reserved for the CP round.
-
-        Raises:
-            NotImplementedError: Always — context parallelism is not implemented yet.
-        """
-        raise NotImplementedError(
-            "Context parallelism is not implemented yet; ParallelDims.cp_mesh is reserved for "
-            "the CP engine round (a private mesh aligned with accelerate's cp block)."
-        )

src/lerobot/distributed/utils.py

@@ -1,94 +0,0 @@
-#!/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.
-"""Rank utilities and post-`prepare()` sharding finalization."""
-
-import logging
-from typing import TYPE_CHECKING
-
-import torch.distributed as dist
-from torch import nn
-
-if TYPE_CHECKING:
-    from lerobot.distributed.parallel_dims import ParallelDims
-
-
-def is_main_process() -> bool:
-    """True on the process that owns rank-0-only side effects (file writes, uploads, logging).
-
-    Torch-native on purpose: persistence code must not depend on an `Accelerator` handle —
-    `_save_pretrained` and the hub publishers run in contexts that have none. Outside
-    distributed runs every process is the main process.
-
-    Returns:
-        bool: True when this process is rank 0 or no process group is initialized.
-    """
-    return not dist.is_initialized() or dist.get_rank() == 0
-
-
-def strip_accelerate_cp_hooks(model: nn.Module) -> int:
-    """Remove accelerate's context-parallel forward-pre-hooks from every module.
-
-    When `cp_size > 1` is declared, `accelerator.prepare()` unconditionally attaches hooks that
-    silently replace any `attention_mask` kwarg of `*self_attn` modules with `is_causal=True`
-    (`accelerate.big_modeling._attach_context_parallel_hooks`) — mask corruption for policies
-    with non-causal attention. LeRobot implements CP itself and never enters accelerate's CP
-    context, so these hooks are pure hazard. Deterministically identified by their defining
-    module; a version canary pins that identity.
-
-    Args:
-        model (nn.Module): The prepared model to strip the hooks from (all submodules are
-            visited).
-
-    Returns:
-        int: The number of hooks removed.
-    """
-    removed = 0
-    for module in model.modules():
-        for hook_id, hook in list(module._forward_pre_hooks.items()):
-            if getattr(hook, "__module__", None) == "accelerate.big_modeling":
-                del module._forward_pre_hooks[hook_id]
-                module._forward_pre_hooks_with_kwargs.pop(hook_id, None)
-                removed += 1
-    return removed
-
-
-def finalize_sharded_policy(policy: nn.Module, parallel_dims: "ParallelDims") -> None:
-    """Sharding correctness protocol, applied once, immediately after `accelerator.prepare()`.
-
-    1. Strip accelerate's CP mask hooks (only attached when cp > 1 was declared).
-    2. Register the policy's non-`forward` entry points (`_fsdp_forward_methods`) so FSDP2
-       unshards parameters around `select_action` & co. — without this, any inference-style
-       call on a sharded policy crashes on mixed Tensor/DTensor.
-
-    No-op for DDP/single-process runs.
-
-    Args:
-        policy (nn.Module): The policy as returned by `accelerator.prepare()`.
-        parallel_dims (ParallelDims): The run's resolved topology; decides whether the protocol
-            applies.
-    """
-    if not parallel_dims.is_sharded:
-        return
-    if parallel_dims.cp_size > 1:
-        removed = strip_accelerate_cp_hooks(policy)
-        logging.info("Stripped %d accelerate context-parallel attention-mask hooks.", removed)
-
-    from torch.distributed.fsdp import FSDPModule, register_fsdp_forward_method
-
-    if isinstance(policy, FSDPModule):
-        for method_name in getattr(type(policy), "_fsdp_forward_methods", ()):
-            if callable(getattr(policy, method_name, None)):
-                register_fsdp_forward_method(policy, method_name)

src/lerobot/jobs/hf.py

@@ -432,7 +432,7 @@ def submit_to_hf(cfg: TrainPipelineConfig) -> None:
 
     # Finish as soon as the model is pushed, rather than waiting out the platform's
     # post-run finalization before the job stage flips to COMPLETED. This matches the
-    # exact log line emitted by lerobot.common.train_utils.publish_trained_model — the two must stay
+    # exact log line emitted by PreTrainedPolicy.push_model_to_hub — the two must stay
     # in sync. If it ever stops matching we just fall back to stage-based completion
     # (~30s slower), so the contract is an optimization, not a correctness requirement.
     success_marker = f"Model pushed to https://huggingface.co/{repo_id}"

src/lerobot/motors/motors_bus.py

@@ -314,16 +314,11 @@ class SerialMotorsBus(MotorsBusBase):
     To find the port, you can run our utility script:
     ```bash
     lerobot-find-port.py
-    ```
-
-    which prints:
-
-    ```
-    Finding all available ports for the MotorsBus.
-    ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
-    Remove the usb cable from your MotorsBus and press Enter when done.
-    The port of this MotorsBus is /dev/tty.usbmodem575E0031751.
-    Reconnect the usb cable.
+    >>> Finding all available ports for the MotorsBus.
+    >>> ["/dev/tty.usbmodem575E0032081", "/dev/tty.usbmodem575E0031751"]
+    >>> Remove the usb cable from your MotorsBus and press Enter when done.
+    >>> The port of this MotorsBus is /dev/tty.usbmodem575E0031751.
+    >>> Reconnect the usb cable.
     ```
 
     Example of usage for 1 Feetech sts3215 motor connected to the bus:
@@ -600,7 +595,7 @@ class SerialMotorsBus(MotorsBusBase):
         ID, and finally programs the bus' default baud-rate.
 
         Args:
-            motor (str): Key of the motor in `motors`.
+            motor (str): Key of the motor in :pyattr:`motors`.
             initial_baudrate (int | None, optional): Current baud-rate (skips scanning when provided).
                 Defaults to None.
             initial_id (int | None, optional): Current ID (skips scanning when provided). Defaults to None.
@@ -671,7 +666,7 @@ class SerialMotorsBus(MotorsBusBase):
         """Enable torque on selected motors.
 
         Args:
-            motors (int | str | list[str] | None, optional): Same semantics as [`~motors.motors_bus.MotorsBus.disable_torque`].
+            motors (int | str | list[str] | None, optional): Same semantics as :pymeth:`disable_torque`.
                 Defaults to `None`.
             num_retry (int, optional): Number of additional retry attempts on communication failure.
                 Defaults to 0.
@@ -684,12 +679,10 @@ class SerialMotorsBus(MotorsBusBase):
 
         This helper is useful to temporarily disable torque when configuring motors.
 
-        Example:
-            ```python
-            >>> with bus.torque_disabled():  # doctest: +SKIP
+        Examples:
+            >>> with bus.torque_disabled():
             ...     # Safe operations here
             ...     pass
-            ```
         """
         self.disable_torque(motors)
         try:
@@ -702,7 +695,7 @@ class SerialMotorsBus(MotorsBusBase):
 
         Args:
             timeout_ms (int | None, optional): Timeout in *milliseconds*. If `None` (default) the method falls
-                back to `default_timeout`.
+                back to :pyattr:`default_timeout`.
         """
         timeout_ms = timeout_ms if timeout_ms is not None else self.default_timeout
         self.port_handler.setPacketTimeoutMillis(timeout_ms)
@@ -753,8 +746,8 @@ class SerialMotorsBus(MotorsBusBase):
 
         Args:
             calibration_dict (dict[str, MotorCalibration]): Calibration obtained from
-                [`~motors.motors_bus.MotorsBus.read_calibration`] or crafted by the user.
-            cache (bool, optional): Save the calibration to `calibration`. Defaults to True.
+                :pymeth:`read_calibration` or crafted by the user.
+            cache (bool, optional): Save the calibration to :pyattr:`calibration`. Defaults to True.
         """
         pass
 
@@ -762,7 +755,7 @@ class SerialMotorsBus(MotorsBusBase):
         """Restore factory calibration for the selected motors.
 
         Homing offset is set to ``0`` and min/max position limits are set to the full usable range.
-        The in-memory `calibration` is cleared.
+        The in-memory :pyattr:`calibration` is cleared.
 
         Args:
             motors (NameOrID | Sequence[NameOrID] | None, optional): Selection of motors. `None` (default)
@@ -1076,9 +1069,9 @@ class SerialMotorsBus(MotorsBusBase):
     ) -> None:
         """Write a value to a single motor's register.
 
-        Contrary to [`~motors.motors_bus.MotorsBus.sync_write`], this expects a response status packet emitted by the motor, which
+        Contrary to :pymeth:`sync_write`, this expects a response status packet emitted by the motor, which
         provides a guarantee that the value was written to the register successfully. In consequence, it is
-        slower than [`~motors.motors_bus.MotorsBus.sync_write`] but it is more reliable. It should typically be used when configuring
+        slower than :pymeth:`sync_write` but it is more reliable. It should typically be used when configuring
         motors.
 
         Args:
@@ -1235,8 +1228,8 @@ class SerialMotorsBus(MotorsBusBase):
     ) -> None:
         """Write the same register on multiple motors.
 
-        Contrary to [`~motors.motors_bus.MotorsBus.write`], this *does not* expects a response status packet emitted by the motor, which
-        can allow for lost packets. It is faster than [`~motors.motors_bus.MotorsBus.write`] and should typically be used when
+        Contrary to :pymeth:`write`, this *does not* expects a response status packet emitted by the motor, which
+        can allow for lost packets. It is faster than :pymeth:`write` and should typically be used when
         frequency matters and losing some packets is acceptable (e.g. teleoperation loops).
 
         Args:

src/lerobot/optim/__init__.py

@@ -20,6 +20,7 @@ from .optimizers import (
     SGDConfig as SGDConfig,
     XVLAAdamWConfig as XVLAAdamWConfig,
     load_optimizer_state,
+    load_optimizer_state_dict,
     save_optimizer_state,
 )
 from .schedulers import (
@@ -50,6 +51,7 @@ __all__ = [
     "VQBeTSchedulerConfig",
     # State management
     "load_optimizer_state",
+    "load_optimizer_state_dict",
     "load_scheduler_state",
     "save_optimizer_state",
     "save_scheduler_state",

src/lerobot/optim/optimizers.py

@@ -27,7 +27,7 @@ from lerobot.utils.constants import (
     OPTIMIZER_PARAM_GROUPS,
     OPTIMIZER_STATE,
 )
-from lerobot.utils.io_utils import deserialize_json_into_object, write_json
+from lerobot.utils.io_utils import deserialize_json_into_object, load_json, write_json
 from lerobot.utils.utils import flatten_dict, unflatten_dict
 
 # Type alias for parameters accepted by optimizer build() methods.
@@ -52,11 +52,6 @@ class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
     def type(self) -> str:
         return self.get_choice_name(self.__class__)
 
-    @property
-    def builds_multiple_optimizers(self) -> bool:
-        """True when build() returns a dict of optimizers (unsupported under sharded training)."""
-        return False
-
     @classmethod
     def default_choice_name(cls) -> str | None:
         return "adam"
@@ -250,10 +245,6 @@ class MultiAdamConfig(OptimizerConfig):
     grad_clip_norm: float = 10.0
     optimizer_groups: dict[str, dict[str, Any]] = field(default_factory=dict)
 
-    @property
-    def builds_multiple_optimizers(self) -> bool:
-        return True
-
     def build(self, params: OptimizerParams) -> dict[str, torch.optim.Optimizer]:
         """Build multiple Adam optimizers.
 
@@ -292,27 +283,35 @@ class MultiAdamConfig(OptimizerConfig):
 def save_optimizer_state(
     optimizer: torch.optim.Optimizer | dict[str, torch.optim.Optimizer],
     save_dir: Path,
+    optim_state_dict: dict | None = None,
 ) -> None:
-    """Save optimizer state to disk (non-sharded runs; sharded runs use the DCP channel).
+    """Save optimizer state to disk.
 
     Args:
         optimizer: Either a single optimizer or a dictionary of optimizers.
         save_dir: Directory to save the optimizer state.
+        optim_state_dict: Pre-gathered optimizer state dict (for FSDP, where the sharded state must
+            be gathered across ranks first). If provided, it is saved directly instead of calling
+            ``optimizer.state_dict()``. Only supported for a single optimizer. Defaults to None.
     """
     if isinstance(optimizer, dict):
         # Handle dictionary of optimizers
+        if optim_state_dict is not None:
+            raise ValueError("optim_state_dict is not supported for a dict of optimizers")
         for name, opt in optimizer.items():
             optimizer_dir = save_dir / name
             optimizer_dir.mkdir(exist_ok=True, parents=True)
             _save_single_optimizer_state(opt, optimizer_dir)
     else:
         # Handle single optimizer
-        _save_single_optimizer_state(optimizer, save_dir)
+        _save_single_optimizer_state(optimizer, save_dir, optim_state_dict=optim_state_dict)
 
 
-def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
+def _save_single_optimizer_state(
+    optimizer: torch.optim.Optimizer, save_dir: Path, optim_state_dict: dict | None = None
+) -> None:
     """Save a single optimizer's state to disk."""
-    state = optimizer.state_dict()
+    state = dict(optim_state_dict) if optim_state_dict is not None else optimizer.state_dict()
     param_groups = state.pop("param_groups")
     flat_state = flatten_dict(state)
     save_file(flat_state, save_dir / OPTIMIZER_STATE)
@@ -366,3 +365,19 @@ def _load_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Pat
 
     optimizer.load_state_dict(loaded_state_dict)
     return optimizer
+
+
+def load_optimizer_state_dict(save_dir: Path) -> dict:
+    """Read a saved optimizer state dict (safetensors + json) back into a plain dict.
+
+    Unlike `load_optimizer_state`, this does not load into an optimizer and preserves the original
+    ``state`` keys verbatim (e.g. FSDP parameter FQNs, which are not integer-castable). It is used by
+    the FSDP resume path, where the full state must be resharded via `FSDP.optim_state_dict_to_load`
+    before being loaded into the (sharded) optimizer.
+    """
+    flat_state = load_file(save_dir / OPTIMIZER_STATE)
+    state = unflatten_dict(flat_state)
+    return {
+        "state": state.get("state", {}),
+        "param_groups": load_json(save_dir / OPTIMIZER_PARAM_GROUPS),
+    }

src/lerobot/policies/act/modeling_act.py

@@ -47,8 +47,6 @@ class ACTPolicy(PreTrainedPolicy):
 
     config_class = ACTConfig
     name = "act"
-    # FSDP2 wrap units: one unit per transformer layer of both stacks.
-    _fsdp_wrap_modules = ["ACTEncoderLayer", "ACTDecoderLayer"]
 
     def __init__(
         self,

src/lerobot/policies/factory.py

@@ -131,16 +131,12 @@ class ProcessorConfigKwargs(TypedDict, total=False):
     This provides type hints for the optional arguments passed to `make_pre_post_processors`,
     improving code clarity and enabling static analysis.
 
-    **Attributes**:
-        - **preprocessor_config_filename** (`str | None`) -- The filename for the preprocessor configuration.
-        - **postprocessor_config_filename** (`str | None`) -- The filename for the postprocessor
-          configuration.
-        - **preprocessor_overrides** (`dict[str, Any] | None`) -- A dictionary of overrides for the
-          preprocessor configuration.
-        - **postprocessor_overrides** (`dict[str, Any] | None`) -- A dictionary of overrides for the
-          postprocessor configuration.
-        - **dataset_stats** (`dict[str, dict[str, torch.Tensor]] | None`) -- Dataset statistics for
-          normalization.
+    Attributes:
+        preprocessor_config_filename: The filename for the preprocessor configuration.
+        postprocessor_config_filename: The filename for the postprocessor configuration.
+        preprocessor_overrides: A dictionary of overrides for the preprocessor configuration.
+        postprocessor_overrides: A dictionary of overrides for the postprocessor configuration.
+        dataset_stats: Dataset statistics for normalization.
     """
 
     preprocessor_config_filename: str | None
@@ -246,7 +242,6 @@ def make_policy(
     ds_meta: LeRobotDatasetMetadata | None = None,
     env_cfg: EnvConfig | None = None,
     rename_map: dict[str, str] | None = None,
-    defer_weight_load: bool = False,
 ) -> PreTrainedPolicy:
     """
     Instantiate a policy model.
@@ -257,27 +252,22 @@ def make_policy(
     can either initialize a new policy from scratch or load a pretrained one.
 
     Args:
-        cfg (PreTrainedConfig): The configuration for the policy to be created. If
-            `cfg.pretrained_path` is set, the policy will be loaded with weights from that path.
-        ds_meta (LeRobotDatasetMetadata | None): Dataset metadata used to infer feature shapes and
-            types. Also provides statistics for normalization layers.
-        env_cfg (EnvConfig | None): Environment configuration used to infer feature shapes and
-            types. One of `ds_meta` or `env_cfg` must be provided.
-        rename_map (dict[str, str] | None): Optional mapping of dataset or environment feature
-            keys to match expected policy feature names (e.g., `"left"` → `"camera1"`).
-        defer_weight_load (bool): Build the exact policy `from_pretrained` would build — same
-            config resolution, same stats-derived buffers, same device placement and eval mode —
-            but skip the safetensors weight load. Used when resuming from a DCP checkpoint, whose
-            sharded weights stream in after `accelerator.prepare()` (the distributed checkpoint
-            engine overwrites the random init).
+        cfg: The configuration for the policy to be created. If `cfg.pretrained_path` is
+             set, the policy will be loaded with weights from that path.
+        ds_meta: Dataset metadata used to infer feature shapes and types. Also provides
+                 statistics for normalization layers.
+        env_cfg: Environment configuration used to infer feature shapes and types.
+                 One of `ds_meta` or `env_cfg` must be provided.
+        rename_map: Optional mapping of dataset or environment feature keys to match
+                 expected policy feature names (e.g., `"left"` → `"camera1"`).
 
     Returns:
-        PreTrainedPolicy: An instantiated and device-placed policy model.
+        An instantiated and device-placed policy model.
 
     Raises:
         ValueError: If both or neither of `ds_meta` and `env_cfg` are provided.
-        NotImplementedError: If attempting to use an unsupported policy-backend combination
-            (e.g., VQBeT with 'mps').
+        NotImplementedError: If attempting to use an unsupported policy-backend
+                             combination (e.g., VQBeT with 'mps').
     """
     if bool(ds_meta) == bool(env_cfg):
         raise ValueError("Either one of a dataset metadata or a sim env must be provided.")
@@ -342,18 +332,11 @@ def make_policy(
         )
 
     if cfg.pretrained_path and not cfg.use_peft:
-        if defer_weight_load:
-            # Same construction path as from_pretrained (config already resolved from the
-            # checkpoint by the caller; dataset_stats/dataset_meta kwargs identical), minus the
-            # weight load — parity by construction.
-            policy = policy_cls(**kwargs)
-            policy.eval()
-        else:
-            # Load a pretrained policy and override the config if needed (for example, if there
-            # are inference-time hyperparameters that we want to vary).
-            kwargs["pretrained_name_or_path"] = cfg.pretrained_path
-            kwargs["revision"] = cfg.pretrained_revision
-            policy = policy_cls.from_pretrained(**kwargs)
+        # Load a pretrained policy and override the config if needed (for example, if there are inference-time
+        # hyperparameters that we want to vary).
+        kwargs["pretrained_name_or_path"] = cfg.pretrained_path
+        kwargs["revision"] = cfg.pretrained_revision
+        policy = policy_cls.from_pretrained(**kwargs)
     elif cfg.pretrained_path and cfg.use_peft:
         # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
         # of the adapter and the adapter's config contains the path to the base policy. So we need the

src/lerobot/policies/fastwam/modeling_fastwam.py

@@ -54,9 +54,6 @@ class FastWAMPolicy(PreTrainedPolicy):
 
     config_class = FastWAMConfig
     name = "fastwam"
-    # FSDP2 wrap units: MoTLayer is the single FSDP owner of each layer's expert blocks
-    # (the blocks are re-parented onto it precisely so sharding has one boundary to hook).
-    _fsdp_wrap_modules = ["MoTLayer"]
 
     def __init__(
         self,

src/lerobot/policies/groot/processor_groot.py

@@ -56,6 +56,7 @@ from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
     PolicyAction,
+    PolicyActionProcessorStep,
     PolicyProcessorPipeline,
     ProcessorStep,
     ProcessorStepRegistry,
@@ -2297,7 +2298,7 @@ def _apply_n1_7_action_decode_transform(
 
 @dataclass
 @ProcessorStepRegistry.register(name="groot_n1_7_action_decode_v1")
-class GrootN17ActionDecodeStep(ProcessorStep):
+class GrootN17ActionDecodeStep(PolicyActionProcessorStep):
     """Decode the full 132-D N1.7 model action back to environment actions.
 
     N1.7 predicts checkpoint-order action groups. This step unnormalizes each
@@ -2318,6 +2319,8 @@ class GrootN17ActionDecodeStep(ProcessorStep):
     and chunk index alongside each queued action through the postprocessor.
     """
 
+    skip_if_missing = True
+
     env_action_dim: int = 0
     raw_stats: dict[str, Any] | None = None
     modality_config: dict[str, Any] | None = None
@@ -2326,20 +2329,17 @@ class GrootN17ActionDecodeStep(ProcessorStep):
     action_decode_transform: str | None = None
     pack_step: GrootN17PackInputsStep | None = field(default=None, repr=False)
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition.get(TransitionKey.ACTION)
-        if not isinstance(action, torch.Tensor):
-            return transition
+    def action(self, action: PolicyAction) -> PolicyAction:
         if self.raw_stats is None or self.modality_config is None:
-            return transition
+            return action
 
         action_config = self.modality_config.get("action", {})
         if not isinstance(action_config, dict):
-            return transition
+            return action
         action_keys = action_config.get("modality_keys", [])
         action_configs = action_config.get("action_configs", [])
         if not isinstance(action_keys, list) or not isinstance(action_configs, list):
-            return transition
+            return action
 
         action_np = action.detach().cpu().float().numpy()
         if self.use_relative_action and action_np.ndim != 3:
@@ -2420,7 +2420,7 @@ class GrootN17ActionDecodeStep(ProcessorStep):
                     raise ValueError(f"Unsupported relative N1.7 action config for '{key}': {cfg}")
 
         if not decoded_groups:
-            return transition
+            return action
 
         decoded = np.concatenate(
             [decoded_groups[key] for key in action_keys if isinstance(key, str) and key in decoded_groups],
@@ -2436,11 +2436,7 @@ class GrootN17ActionDecodeStep(ProcessorStep):
         )
         if squeeze_horizon:
             decoded = decoded[:, 0]
-        new_transition = transition.copy()
-        new_transition[TransitionKey.ACTION] = torch.as_tensor(
-            decoded, dtype=action.dtype, device=action.device
-        )
-        return new_transition
+        return torch.as_tensor(decoded, dtype=action.dtype, device=action.device)
 
     def transform_features(self, features):
         return features
@@ -2461,7 +2457,9 @@ class GrootN17ActionDecodeStep(ProcessorStep):
 # silently load into it (v1 is stubbed below with the removal guidance).
 @dataclass
 @ProcessorStepRegistry.register(name="groot_action_unpack_unnormalize_v2")
-class GrootActionUnpackUnnormalizeStep(ProcessorStep):
+class GrootActionUnpackUnnormalizeStep(PolicyActionProcessorStep):
+    skip_if_missing = True
+
     env_action_dim: int = 0
     # Apply inverse of min-max normalization if it was used in preprocessor
     normalize_min_max: bool = True
@@ -2470,12 +2468,8 @@ class GrootActionUnpackUnnormalizeStep(ProcessorStep):
     libero_gripper_action: bool = False
     libero_gripper_binarize: bool = True
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        # Expect model outputs to be in TransitionKey.ACTION as (B, T, D_model)
-        action = transition.get(TransitionKey.ACTION)
-        if not isinstance(action, torch.Tensor):
-            return transition
-
+    def action(self, action: PolicyAction) -> PolicyAction:
+        # Model outputs arrive as (B, T, D_model).
         # Slice to env dimension while preserving an optional action horizon.
         # Sync rollout postprocesses selected actions as (B, D); RTC postprocesses
         # chunks as (B, T, D), matching Isaac-GR00T's decode_action contract.
@@ -2517,8 +2511,7 @@ class GrootActionUnpackUnnormalizeStep(ProcessorStep):
             action = action.clone()
             action[..., -1] = gripper
 
-        transition[TransitionKey.ACTION] = action
-        return transition
+        return action
 
     def transform_features(self, features):
         return features

src/lerobot/policies/molmoact2/processor_molmoact2.py

@@ -41,7 +41,9 @@ from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
+    ObservationProcessorStep,
     PolicyAction,
+    PolicyActionProcessorStep,
     PolicyProcessorPipeline,
     ProcessorStep,
     ProcessorStepRegistry,
@@ -1007,7 +1009,7 @@ class MolmoAct2PackInputsProcessorStep(ProcessorStep):
 
 @ProcessorStepRegistry.register(name="molmoact2_state_frame_transform")
 @dataclass
-class MolmoAct2StateFrameTransformStep(ProcessorStep):
+class MolmoAct2StateFrameTransformStep(ObservationProcessorStep):
     """Convert robot state from arm frame to model frame before normalization.
 
     Required for zero-shot deployment of MolmoAct2-SO100_101 on SO-100/101
@@ -1023,25 +1025,21 @@ class MolmoAct2StateFrameTransformStep(ProcessorStep):
     See: https://huggingface.co/docs/lerobot/backwardcomp
     """
 
+    skip_if_missing = True
+
     joint_signs: list[float] | None = None
     joint_offsets: list[float] | None = None
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        observation = transition.get(TransitionKey.OBSERVATION)
-        if not isinstance(observation, dict) or OBS_STATE not in observation:
-            return transition
-        transition = transition.copy()
-        observation = observation.copy()
+    def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
+        if self.joint_signs is None or self.joint_offsets is None or OBS_STATE not in observation:
+            return observation
         state = torch.as_tensor(observation[OBS_STATE], dtype=torch.float32).clone()
         n = len(self.joint_signs)
         signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=state.device)
         offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=state.device)
         state[..., :n] = signs * state[..., :n] + offsets
         observation[OBS_STATE] = state
-        transition[TransitionKey.OBSERVATION] = observation
-        return transition
+        return observation
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
@@ -1054,7 +1052,7 @@ class MolmoAct2StateFrameTransformStep(ProcessorStep):
 
 @ProcessorStepRegistry.register(name="molmoact2_action_frame_transform")
 @dataclass
-class MolmoAct2ActionFrameTransformStep(ProcessorStep):
+class MolmoAct2ActionFrameTransformStep(PolicyActionProcessorStep):
     """Convert model action from model frame back to arm frame after unnormalization.
 
     Inverse of MolmoAct2StateFrameTransformStep. Required for zero-shot
@@ -1065,23 +1063,20 @@ class MolmoAct2ActionFrameTransformStep(ProcessorStep):
     See: https://huggingface.co/docs/lerobot/backwardcomp
     """
 
+    skip_if_missing = True
+
     joint_signs: list[float] | None = None
     joint_offsets: list[float] | None = None
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    def action(self, action: PolicyAction) -> PolicyAction:
         if self.joint_signs is None or self.joint_offsets is None:
-            return transition
-        action = transition.get(TransitionKey.ACTION)
-        if action is None:
-            return transition
-        transition = transition.copy()
+            return action
         action = torch.as_tensor(action, dtype=torch.float32).clone()
         n = len(self.joint_signs)
         signs = torch.tensor(self.joint_signs, dtype=torch.float32, device=action.device)
         offsets = torch.tensor(self.joint_offsets, dtype=torch.float32, device=action.device)
         action[..., :n] = signs * (action[..., :n] - offsets)
-        transition[TransitionKey.ACTION] = action
-        return transition
+        return action
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
@@ -1094,13 +1089,11 @@ class MolmoAct2ActionFrameTransformStep(ProcessorStep):
 
 @ProcessorStepRegistry.register(name="molmoact2_clamp_action")
 @dataclass
-class MolmoAct2ClampActionProcessorStep(ProcessorStep):
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-        action = transition.get(TransitionKey.ACTION)
-        if action is not None:
-            transition[TransitionKey.ACTION] = torch.as_tensor(action).clamp(-1.0, 1.0)
-        return transition
+class MolmoAct2ClampActionProcessorStep(PolicyActionProcessorStep):
+    skip_if_missing = True
+
+    def action(self, action: PolicyAction) -> PolicyAction:
+        return action.clamp(-1.0, 1.0)
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/policies/pi05/processor_pi05.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any
 
@@ -22,9 +21,10 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
+    ComplementaryDataProcessorStep,
     PolicyAction,
     PolicyProcessorPipeline,
     ProcessorStep,
@@ -41,7 +41,7 @@ from .configuration_pi05 import PI05Config
 
 @ProcessorStepRegistry.register(name="pi05_prepare_state_tokenizer_processor_step")
 @dataclass
-class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
+class Pi05PrepareStateTokenizerProcessorStep(ComplementaryDataProcessorStep):
     """
     Processor step to prepare the state and tokenize the language input.
     """
@@ -49,19 +49,14 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
     max_state_dim: int = 32
     task_key: str = "task"
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-
-        state = transition.get(TransitionKey.OBSERVATION, {}).get(OBS_STATE)
+    def complementary_data(self, complementary_data: dict[str, Any]) -> dict[str, Any]:
+        state = (self.transition.get(TransitionKey.OBSERVATION) or {}).get(OBS_STATE)
         if state is None:
             raise ValueError("State is required for PI05")
-        tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key)
+        tasks = complementary_data.get(self.task_key)
         if tasks is None:
             raise ValueError("No task found in complementary data")
 
-        # TODO: check if this necessary
-        state = deepcopy(state)
-
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()
@@ -74,10 +69,11 @@ class Pi05PrepareStateTokenizerProcessorStep(ProcessorStep):
             full_prompt = f"Task: {cleaned_text}, State: {state_str};\nAction: "
             full_prompts.append(full_prompt)
 
-        transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts
-        # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!)
-        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
-        return transition
+        complementary_data[self.task_key] = full_prompts
+        return complementary_data
+
+    def get_config(self) -> dict[str, Any]:
+        return {"task_key": self.task_key, "max_state_dim": self.max_state_dim}
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any
 
@@ -22,10 +21,11 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
     AbsoluteActionsProcessorStep,
     ActionTokenizerProcessorStep,
+    ComplementaryDataProcessorStep,
     PolicyAction,
     PolicyProcessorPipeline,
     ProcessorStep,
@@ -42,7 +42,7 @@ from .configuration_pi0_fast import PI0FastConfig
 
 @ProcessorStepRegistry.register(name="pi0_fast_prepare_state_tokenizer_processor_step")
 @dataclass
-class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
+class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ComplementaryDataProcessorStep):
     """
     Processor step to prepare the state and tokenize the language input.
     """
@@ -50,19 +50,14 @@ class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
     max_state_dim: int = 32
     task_key: str = "task"
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        transition = transition.copy()
-
-        state = transition.get(TransitionKey.OBSERVATION, {}).get(OBS_STATE)
+    def complementary_data(self, complementary_data: dict[str, Any]) -> dict[str, Any]:
+        state = (self.transition.get(TransitionKey.OBSERVATION) or {}).get(OBS_STATE)
         if state is None:
             raise ValueError("State is required for PI0Fast")
-        tasks = transition.get(TransitionKey.COMPLEMENTARY_DATA, {}).get(self.task_key)
+        tasks = complementary_data.get(self.task_key)
         if tasks is None:
             raise ValueError("No task found in complementary data")
 
-        # TODO: check if this necessary
-        state = deepcopy(state)
-
         # State should already be normalized to [-1, 1] by the NormalizerProcessorStep that runs before this step
         # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
         state_np = state.cpu().numpy()
@@ -75,10 +70,11 @@ class Pi0FastPrepareStateAndLanguageTokenizerProcessorStep(ProcessorStep):
             full_prompt = f"Task: {cleaned_text}, State: {state_str};\n"
             full_prompts.append(full_prompt)
 
-        transition[TransitionKey.COMPLEMENTARY_DATA][self.task_key] = full_prompts
-        # Normalize state to [-1, 1] range if needed (assuming it's already normalized by normalizer processor step!!)
-        # Discretize into 256 bins (see openpi `PaligemmaTokenizer.tokenize()`)
-        return transition
+        complementary_data[self.task_key] = full_prompts
+        return complementary_data
+
+    def get_config(self) -> dict[str, Any]:
+        return {"task_key": self.task_key, "max_state_dim": self.max_state_dim}
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/policies/pretrained.py

@@ -18,17 +18,20 @@ import builtins
 import dataclasses
 import logging
 import os
-import warnings
+from importlib.resources import files
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, ClassVar, TypedDict, TypeVar, Unpack
+from tempfile import TemporaryDirectory
+from typing import TYPE_CHECKING, TypedDict, TypeVar, Unpack
 
-from huggingface_hub import hf_hub_download, save_torch_state_dict
+from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download, save_torch_state_dict
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
-from safetensors.torch import load_model as load_model_as_safetensor
+from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
 from torch import Tensor, nn
 
+from lerobot.__version__ import __version__
 from lerobot.configs import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
 from lerobot.utils.device_utils import resolve_safetensors_device
 from lerobot.utils.hub import HubMixin
 from lerobot.utils.import_utils import _peft_available, require_package
@@ -43,14 +46,56 @@ else:
     get_peft_model = None
 
 if TYPE_CHECKING:
-    from lerobot.configs.train import TrainPipelineConfig
     from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
 
 T = TypeVar("T", bound="PreTrainedPolicy")
 
-# Pinned far above any policy's total size so save_torch_state_dict always emits exactly one
-# `model.safetensors` (no shards, no index) — a constant, not a computed byte count.
-_SINGLE_FILE_SHARD_SIZE = "1TB"
+
+def _build_card_context(
+    cfg: TrainPipelineConfig | None,
+    dataset_meta: LeRobotDatasetMetadata | None,
+    input_features: dict | None,
+    output_features: dict | None,
+) -> dict:
+    """Collect optional data for the model-card template.
+
+    Returns plain values only (no Markdown) — the template in
+    ``lerobot/templates/lerobot_modelcard_template.md`` decides how and whether to show
+    each one. Everything is best-effort: anything unavailable is left empty/None and the
+    template simply skips that section, so this never breaks a Hub push.
+    """
+    context = {
+        "training": None,
+        "input_features": input_features or {},
+        "output_features": output_features or {},
+        "dataset": None,
+        "robot_type": None,
+        "cameras": [],
+    }
+
+    if cfg is not None:
+        optimizer = getattr(cfg, "optimizer", None)
+        context["training"] = {
+            "steps": cfg.steps,
+            "batch_size": cfg.batch_size,
+            "seed": cfg.seed,
+            "optimizer": getattr(optimizer, "type", None) if optimizer else None,
+            "lr": getattr(optimizer, "lr", None) if optimizer else None,
+            "lerobot_version": __version__,
+        }
+
+    if dataset_meta is not None:
+        context["dataset"] = {
+            "repo_id": dataset_meta.repo_id,
+            "episodes": dataset_meta.total_episodes,
+            "frames": dataset_meta.total_frames,
+            "fps": dataset_meta.fps,
+            "tasks": [str(task) for task in dataset_meta.tasks.index],
+        }
+        context["robot_type"] = dataset_meta.robot_type
+        context["cameras"] = [key.split(".")[-1] for key in dataset_meta.camera_keys]
+
+    return context
 
 
 class ActionSelectKwargs(TypedDict, total=False):
@@ -65,22 +110,6 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
     config_class: None
     name: None
 
-    # --- declarative parallelism/acceleration surface ----------------------------------------
-    # Module CLASS names forming the FSDP2 wrap units (and, once wired, the activation-
-    # checkpointing units). Resolved onto the accelerate plugin right before
-    # `accelerator.prepare()` by `lerobot.distributed.set_fsdp_wrap_modules`; sharded training
-    # with no wrap source anywhere fails loudly instead of silently wrapping only the root.
-    _fsdp_wrap_modules: ClassVar[list[str] | None] = None
-    # Non-`forward` entry points that must trigger FSDP2 unshard/reshard hooks when called on a
-    # sharded policy (registered post-prepare via `torch.distributed.fsdp
-    # .register_fsdp_forward_method`); calling them unregistered crashes on mixed Tensor/DTensor.
-    _fsdp_forward_methods: ClassVar[tuple[str, ...]] = ("select_action", "predict_action_chunk")
-    # Capability gate for the (future) activation-checkpointing wiring.
-    supports_gradient_checkpointing: ClassVar[bool] = False
-    # Declarative context-parallel plan (diffusers `ContextParallelModelPlan` semantics:
-    # module FQN -> sequence split/gather spec). Reserved for the CP engine round.
-    _cp_plan: ClassVar[dict[str, Any] | None] = None
-
     def __init__(self, config: PreTrainedConfig, *inputs, **kwargs):
         super().__init__()
         if not isinstance(config, PreTrainedConfig):
@@ -98,33 +127,43 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         if not getattr(cls, "name", None):
             raise TypeError(f"Class {cls.__name__} must define 'name'")
 
-    def _save_pretrained(self, save_directory: Path) -> None:
-        """Serialize this policy's parameters (and config) into `save_directory`.
+    def save_pretrained(
+        self,
+        save_directory: str | Path,
+        *,
+        state_dict: dict[str, Tensor] | None = None,
+        repo_id: str | None = None,
+        push_to_hub: bool = False,
+        card_kwargs: dict | None = None,
+        **push_to_hub_kwargs,
+    ) -> str | None:
+        """Save the policy to a directory (and optionally push to the Hub).
 
-        Sharding is handled internally: under FSDP2 the full state dict is gathered through a
-        COLLECTIVE, so when the policy is sharded this method (via `save_pretrained`) must be
-        called on EVERY rank — a rank-0-gated call deadlocks. File writes happen on the main
-        process only, in all layouts (single, DDP, sharded).
-
-        Args:
-            save_directory (Path): Target directory for the policy config (`config.json`) and the
-                safetensors weight file(s).
+        Overrides `HubMixin.save_pretrained` to add a `state_dict` argument (mirroring
+        `transformers.PreTrainedModel.save_pretrained`). Under FSDP, `self.state_dict()` would
+        return sharded tensors, so the caller gathers the full state dict via a cross-rank
+        collective and passes it here for `_save_pretrained` to write directly.
         """
-        # Lazy imports: the persistence layer pulls in lerobot.distributed only when saving.
-        from lerobot.distributed.checkpoint import full_model_state_dict, is_sharded_module
-        from lerobot.distributed.utils import is_main_process
+        save_directory = Path(save_directory)
+        save_directory.mkdir(parents=True, exist_ok=True)
+        self._save_pretrained(save_directory, state_dict=state_dict)
+        if push_to_hub:
+            if repo_id is None:
+                repo_id = save_directory.name
+            return self.push_to_hub(repo_id=repo_id, card_kwargs=card_kwargs, **push_to_hub_kwargs)
+        return None
 
-        model_to_save = self.module if hasattr(self, "module") else self
-        if is_sharded_module(model_to_save):
-            logging.info("Gathering the full state dict from all ranks (sharded policy).")
-        state_dict = full_model_state_dict(model_to_save)  # collective when sharded; {} off-main
-        if not state_dict or not is_main_process():
-            # Sharded: the gather materializes on the main rank only (emptiness check).
-            # Non-sharded multi-rank (DDP): every rank holds a full dict — the explicit rank
-            # gate prevents N ranks racing on the same files. Single process: never taken.
-            return
+    def _save_pretrained(self, save_directory: Path, state_dict: dict[str, Tensor] | None = None) -> None:
         self.config._save_pretrained(save_directory)
-        save_torch_state_dict(state_dict, str(save_directory), max_shard_size=_SINGLE_FILE_SHARD_SIZE)
+        model_to_save = self.module if hasattr(self, "module") else self
+        if state_dict is None:
+            save_model_as_safetensor(model_to_save, str(save_directory / SAFETENSORS_SINGLE_FILE))
+            return
+        # A pre-gathered (e.g. FSDP full) state dict was supplied: write it directly.
+        # `save_torch_state_dict` discards shared-tensor duplicates just like `save_model` does;
+        # pin `max_shard_size` above the total size so the output stays a single `model.safetensors`
+        total_bytes = sum(t.numel() * t.element_size() for t in state_dict.values())
+        save_torch_state_dict(state_dict, str(save_directory), max_shard_size=max(total_bytes, 1))
 
     @classmethod
     def from_pretrained(
@@ -252,39 +291,92 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
         peft_model=None,
         state_dict: dict[str, Tensor] | None = None,
         dataset_meta: LeRobotDatasetMetadata | None = None,
-    ) -> None:
-        """Publish this policy to the Hub.
+    ):
+        api = HfApi()
+        repo_id = api.create_repo(
+            repo_id=self.config.repo_id, private=self.config.private, exist_ok=True
+        ).repo_id
 
-        Deprecated: use :func:`lerobot.common.train_utils.publish_trained_model` instead, which
-        also publishes the pre/post-processors alongside the model.
+        # Push the files to the repo in a single commit
+        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
+            saved_path = Path(tmp) / repo_id
 
-        Args:
-            cfg (TrainPipelineConfig): The training config; saved as `train_config.json` and
-                used to render the model card.
-            peft_model: The PEFT wrapper when training adapters, whose weights replace the full
-                model weights in the published repo. Defaults to None.
-            state_dict (dict[str, Tensor] | None): Ignored; weights are now gathered internally
-                when the policy is sharded. Defaults to None.
-            dataset_meta (LeRobotDatasetMetadata | None): Dataset metadata for the model card,
-                if available. Defaults to None.
-        """
-        from lerobot.common.train_utils import publish_trained_model
+            if peft_model is not None:
+                # Since PEFT just forwards calls to `push_model_to_hub`, `self` is not the PeftModel wrapper
+                # but the actual policy which is why we need the PEFT model passed to us to save the adapter.
+                # That also means that we need to store the policy config ourselves since PEFT can't.
+                peft_model.save_pretrained(saved_path)
+                self.config.save_pretrained(saved_path)
+            else:
+                # Calls _save_pretrained and stores model tensors
+                self.save_pretrained(saved_path, state_dict=state_dict)
 
-        warnings.warn(
-            "PreTrainedPolicy.push_model_to_hub is deprecated and will be removed in a future "
-            "version. Use lerobot.common.train_utils.publish_trained_model(cfg, model, "
-            "preprocessor, postprocessor, dataset_meta) instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        if state_dict is not None:
-            warnings.warn(
-                "The `state_dict` argument is ignored: sharded weights are gathered internally "
-                "when the policy is saved.",
-                FutureWarning,
-                stacklevel=2,
+            card = self.generate_model_card(
+                cfg.dataset.repo_id,
+                self.config.type,
+                self.config.license,
+                self.config.tags,
+                cfg=cfg,
+                dataset_meta=dataset_meta,
             )
-        publish_trained_model(cfg, self, None, None, dataset_meta, peft_model=peft_model)
+            card.save(str(saved_path / "README.md"))
+
+            cfg.save_pretrained(saved_path)  # Calls _save_pretrained and stores train config
+
+            commit_info = api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message="Upload policy weights, train config and readme",
+                allow_patterns=["*.safetensors", "*.json", "*.yaml", "*.md"],
+                ignore_patterns=["*.tmp", "*.log"],
+            )
+
+            # Contract: lerobot.jobs.hf.submit_to_hf watches for this exact
+            # "Model pushed to <url>" line to end a remote run early. Keep the wording
+            # and URL format in sync (it falls back to status polling if they drift).
+            logging.info(f"Model pushed to {commit_info.repo_url.url}")
+
+    def generate_model_card(
+        self,
+        dataset_repo_id: str,
+        model_type: str,
+        license: str | None,
+        tags: list[str] | None,
+        cfg: TrainPipelineConfig | None = None,
+        dataset_meta: LeRobotDatasetMetadata | None = None,
+    ) -> ModelCard:
+        base_model_mapping = {
+            "smolvla": "lerobot/smolvla_base",
+            "pi0": "lerobot/pi0_base",
+            "pi05": "lerobot/pi05_base",
+            "pi0_fast": "lerobot/pi0fast-base",
+            "xvla": "lerobot/xvla-base",
+        }
+
+        card_data = ModelCardData(
+            license=license or "apache-2.0",
+            library_name="lerobot",
+            pipeline_tag="robotics",
+            tags=list(set(tags or []).union({"robotics", "lerobot", model_type})),
+            model_name=model_type,
+            datasets=dataset_repo_id,
+            base_model=base_model_mapping.get(model_type),
+        )
+
+        context = _build_card_context(
+            cfg, dataset_meta, self.config.input_features, self.config.output_features
+        )
+        # Used by the template to pre-fill commands and the "Fine-tuned from" line.
+        context["policy_repo_id"] = getattr(self.config, "repo_id", None)
+        context["base_model"] = base_model_mapping.get(model_type)
+
+        template_card = (
+            files("lerobot.templates").joinpath("lerobot_modelcard_template.md").read_text(encoding="utf-8")
+        )
+        card = ModelCard.from_template(card_data, template_str=template_card, **context)
+        card.validate()
+        return card
 
     def wrap_with_peft(
         self,

src/lerobot/policies/rtc/action_queue.py

@@ -46,11 +46,10 @@ class ActionQueue:
     Args:
         cfg (RTCConfig): Configuration for Real-Time Chunking behavior.
 
-    **Attributes**:
-        - **queue** (`Tensor | None`) -- Processed actions for robot rollout (time_steps, action_dim).
-        - **original_queue** (`Tensor | None`) -- Original actions for RTC computation (time_steps,
-          action_dim).
-        - **last_index** (`int`) -- Current consumption index in the queue.
+    Attributes:
+        queue (Tensor | None): Processed actions for robot rollout (time_steps, action_dim).
+        original_queue (Tensor | None): Original actions for RTC computation (time_steps, action_dim).
+        last_index (int): Current consumption index in the queue.
     """
 
     def __init__(self, cfg: RTCConfig):

src/lerobot/policies/rtc/debug_tracker.py

@@ -27,19 +27,19 @@ from torch import Tensor
 class DebugStep:
     """Container for debug information from a single denoising step.
 
-    **Attributes**:
-        - **step_idx** (`int`) -- Step index/counter.
-        - **x_t** (`Tensor | None`) -- Current latent/state tensor.
-        - **v_t** (`Tensor | None`) -- Velocity from denoiser.
-        - **x1_t** (`Tensor | None`) -- Denoised prediction (x_t - time * v_t).
-        - **correction** (`Tensor | None`) -- Correction gradient tensor.
-        - **err** (`Tensor | None`) -- Weighted error term.
-        - **weights** (`Tensor | None`) -- Prefix attention weights.
-        - **guidance_weight** (`float | Tensor | None`) -- Applied guidance weight.
-        - **time** (`float | Tensor | None`) -- Time parameter.
-        - **inference_delay** (`int | None`) -- Inference delay parameter.
-        - **execution_horizon** (`int | None`) -- Execution horizon parameter.
-        - **metadata** (`dict[str, Any]`) -- Additional metadata.
+    Attributes:
+        step_idx (int): Step index/counter.
+        x_t (Tensor | None): Current latent/state tensor.
+        v_t (Tensor | None): Velocity from denoiser.
+        x1_t (Tensor | None): Denoised prediction (x_t - time * v_t).
+        correction (Tensor | None): Correction gradient tensor.
+        err (Tensor | None): Weighted error term.
+        weights (Tensor | None): Prefix attention weights.
+        guidance_weight (float | Tensor | None): Applied guidance weight.
+        time (float | Tensor | None): Time parameter.
+        inference_delay (int | None): Inference delay parameter.
+        execution_horizon (int | None): Execution horizon parameter.
+        metadata (dict[str, Any]): Additional metadata.
     """
 
     step_idx: int = 0

src/lerobot/policies/vla_jepa/processor_vla_jepa.py

@@ -20,12 +20,11 @@ import torch
 
 from lerobot.policies.vla_jepa.configuration_vla_jepa import VLAJEPAConfig
 from lerobot.processor import (
-    EnvTransition,
     PolicyAction,
+    PolicyActionProcessorStep,
     PolicyProcessorPipeline,
     ProcessorStep,
     ProcessorStepRegistry,
-    TransitionKey,
     UnnormalizerProcessorStep,
     make_default_policy_processor_steps,
     make_policy_processor_pipelines,
@@ -33,22 +32,20 @@ from lerobot.processor import (
 
 
 @ProcessorStepRegistry.register(name="vla_jepa_clip_actions")
-class ClipActionsProcessorStep(ProcessorStep):
+class ClipActionsProcessorStep(PolicyActionProcessorStep):
     """Clips action tensor to [-1, 1] before unnormalization."""
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition.get(TransitionKey.ACTION)
-        if action is not None:
-            transition = dict(transition)
-            transition[TransitionKey.ACTION] = action.clamp(-1.0, 1.0)
-        return transition
+    skip_if_missing = True
+
+    def action(self, action: PolicyAction) -> PolicyAction:
+        return action.clamp(-1.0, 1.0)
 
     def transform_features(self, features):
         return features
 
 
 @ProcessorStepRegistry.register(name="vla_jepa_pre_snap_gripper")
-class PreSnapGripperProcessorStep(ProcessorStep):
+class PreSnapGripperProcessorStep(PolicyActionProcessorStep):
     """Snaps a gripper dimension to {0, 1} BEFORE unnormalization.
 
     Mirrors the original starVLA LIBERO eval:
@@ -58,43 +55,49 @@ class PreSnapGripperProcessorStep(ProcessorStep):
     space where 0=open and 1=close.
     """
 
+    skip_if_missing = True
+
     def __init__(self, gripper_dim: int = 6, threshold: float = 0.5):
         self.gripper_dim = gripper_dim
         self.threshold = threshold
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition.get(TransitionKey.ACTION)
-        if action is not None and action.shape[-1] > self.gripper_dim:
-            transition = dict(transition)
-            a = action.clone()
-            a[..., self.gripper_dim] = (a[..., self.gripper_dim] >= self.threshold).float()
-            transition[TransitionKey.ACTION] = a
-        return transition
+    def action(self, action: PolicyAction) -> PolicyAction:
+        if action.shape[-1] <= self.gripper_dim:
+            return action
+        a = action.clone()
+        a[..., self.gripper_dim] = (a[..., self.gripper_dim] >= self.threshold).float()
+        return a
+
+    def get_config(self) -> dict[str, Any]:
+        return {"gripper_dim": self.gripper_dim, "threshold": self.threshold}
 
     def transform_features(self, features):
         return features
 
 
 @ProcessorStepRegistry.register(name="vla_jepa_binarize_gripper")
-class BinarizeGripperProcessorStep(ProcessorStep):
+class BinarizeGripperProcessorStep(PolicyActionProcessorStep):
     """Binarizes a gripper dimension after unnormalization.
 
     Maps continuous value to {-1, 1}: > threshold → -1, <= threshold → 1 (matches starVLA convention).
     Only applied when action has more dimensions than gripper_dim.
     """
 
+    skip_if_missing = True
+
     def __init__(self, gripper_dim: int = 6, threshold: float = 0.5):
         self.gripper_dim = gripper_dim
         self.threshold = threshold
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        action = transition.get(TransitionKey.ACTION)
-        if action is not None and action.shape[-1] > self.gripper_dim:
-            transition = dict(transition)
-            a = action.clone()
-            a[..., self.gripper_dim] = 1.0 - 2.0 * (a[..., self.gripper_dim] > self.threshold).float()
-            transition[TransitionKey.ACTION] = a
-        return transition
+    def action(self, action: PolicyAction) -> PolicyAction:
+        if action.shape[-1] <= self.gripper_dim:
+            return action
+        a = action.clone()
+        a[..., self.gripper_dim] = 1.0 - 2.0 * (a[..., self.gripper_dim] > self.threshold).float()
+        return a
+
+    def get_config(self) -> dict[str, Any]:
+        return {"gripper_dim": self.gripper_dim, "threshold": self.threshold}
 
     def transform_features(self, features):
         return features

src/lerobot/policies/xvla/processor_xvla.py

@@ -21,10 +21,12 @@ import numpy as np
 import torch
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
+    ComplementaryDataProcessorStep,
     ObservationProcessorStep,
     PolicyAction,
+    PolicyActionProcessorStep,
     PolicyProcessorPipeline,
     ProcessorStep,
     ProcessorStepRegistry,
@@ -201,7 +203,7 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="xvla_image_scale")
-class XVLAImageScaleProcessorStep(ProcessorStep):
+class XVLAImageScaleProcessorStep(ObservationProcessorStep):
     """Scale image observations by 255 to convert from [0, 1] to [0, 255] range.
 
     This processor step multiplies all image observations by 255, which is required
@@ -214,29 +216,22 @@ class XVLAImageScaleProcessorStep(ProcessorStep):
 
     image_keys: list[str] | None = None
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    skip_if_missing = True
+
+    def observation(self, observation):
         """Scale image observations by 255."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
         # Determine which keys to scale
         keys_to_scale = self.image_keys
         if keys_to_scale is None:
             # Auto-detect image keys
-            keys_to_scale = [k for k in obs if k.startswith(OBS_IMAGES)]
+            keys_to_scale = [k for k in observation if k.startswith(OBS_IMAGES)]
 
         # Scale each image
         for key in keys_to_scale:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                obs[key] = obs[key] * 255
+            if key in observation and isinstance(observation[key], torch.Tensor):
+                observation[key] = observation[key] * 255
 
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
+        return observation
 
     def transform_features(self, features):
         """Image scaling doesn't change feature structure."""
@@ -251,7 +246,7 @@ class XVLAImageScaleProcessorStep(ProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="xvla_image_to_float")
-class XVLAImageToFloatProcessorStep(ProcessorStep):
+class XVLAImageToFloatProcessorStep(ObservationProcessorStep):
     """Convert image observations from [0, 255] to [0, 1] range.
 
     This processor step divides image observations by 255 to convert from uint8-like
@@ -270,32 +265,26 @@ class XVLAImageToFloatProcessorStep(ProcessorStep):
     image_keys: list[str] | None = None
     validate_range: bool = True
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    skip_if_missing = True
+
+    def observation(self, observation):
         """Convert image observations from [0, 255] to [0, 1]."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
         # Determine which keys to convert
         keys_to_convert = self.image_keys
         if keys_to_convert is None:
             # Auto-detect image keys
-            keys_to_convert = [k for k in obs if k.startswith(OBS_IMAGES)]
+            keys_to_convert = [k for k in observation if k.startswith(OBS_IMAGES)]
 
         # Convert each image
         for key in keys_to_convert:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                tensor = obs[key]
+            if key in observation and isinstance(observation[key], torch.Tensor):
+                tensor = observation[key]
 
                 min_val = tensor.min().item()
                 max_val = tensor.max().item()
 
                 if max_val <= 1.0:
-                    obs[key] = tensor.float()  # ensure float dtype, but no division
+                    observation[key] = tensor.float()  # ensure float dtype, but no division
                     continue
                 # Validate that values are in [0, 255] range if requested
                 if self.validate_range and (min_val < 0.0 or max_val > 255.0):
@@ -306,10 +295,9 @@ class XVLAImageToFloatProcessorStep(ProcessorStep):
                     )
 
                 # Convert to float and divide by 255
-                obs[key] = tensor.float() / 255.0
+                observation[key] = tensor.float() / 255.0
 
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
+        return observation
 
     def transform_features(self, features):
         """Image conversion doesn't change feature structure."""
@@ -325,7 +313,7 @@ class XVLAImageToFloatProcessorStep(ProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="xvla_imagenet_normalize")
-class XVLAImageNetNormalizeProcessorStep(ProcessorStep):
+class XVLAImageNetNormalizeProcessorStep(ObservationProcessorStep):
     """Normalize image observations using ImageNet statistics.
 
     This processor step applies ImageNet normalization (mean and std) to image observations.
@@ -343,26 +331,20 @@ class XVLAImageNetNormalizeProcessorStep(ProcessorStep):
 
     image_keys: list[str] | None = None
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    skip_if_missing = True
+
+    def observation(self, observation):
         """Normalize image observations using ImageNet statistics."""
-        new_transition = transition.copy()
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
-        if obs is None:
-            return new_transition
-
-        # Make a copy of observations to avoid modifying the original
-        obs = obs.copy()
-
         # Determine which keys to normalize
         keys_to_normalize = self.image_keys
         if keys_to_normalize is None:
             # Auto-detect image keys
-            keys_to_normalize = [k for k in obs if k.startswith(OBS_IMAGES)]
+            keys_to_normalize = [k for k in observation if k.startswith(OBS_IMAGES)]
 
         # Normalize each image
         for key in keys_to_normalize:
-            if key in obs and isinstance(obs[key], torch.Tensor):
-                tensor = obs[key]
+            if key in observation and isinstance(observation[key], torch.Tensor):
+                tensor = observation[key]
 
                 # Validate that values are in [0, 1] range
                 min_val = tensor.min().item()
@@ -384,10 +366,9 @@ class XVLAImageNetNormalizeProcessorStep(ProcessorStep):
                     std = std.unsqueeze(0)
 
                 # Normalize: (image - mean) / std
-                obs[key] = (tensor - mean) / std
+                observation[key] = (tensor - mean) / std
 
-        new_transition[TransitionKey.OBSERVATION] = obs
-        return new_transition
+        return observation
 
     def transform_features(self, features):
         """ImageNet normalization doesn't change feature structure."""
@@ -402,38 +383,32 @@ class XVLAImageNetNormalizeProcessorStep(ProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="xvla_add_domain_id")
-class XVLAAddDomainIdProcessorStep(ProcessorStep):
+class XVLAAddDomainIdProcessorStep(ComplementaryDataProcessorStep):
     """Add domain_id to complementary data.
 
     This processor step adds a domain_id tensor to the complementary data,
     which is used by XVLA to identify different robot embodiments or task domains.
 
     Args:
-        domain_id: The domain ID to add (default: 3)
+        domain_id: The domain ID to add (default: 0)
     """
 
     domain_id: int = 0
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    def complementary_data(self, complementary_data):
         """Add domain_id to complementary data."""
-        new_transition = transition.copy()
-        comp = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        comp = {} if comp is None else comp.copy()
-
         # Infer batch size from observation tensors
-        obs = new_transition.get(TransitionKey.OBSERVATION, {})
+        obs = self.transition.get(TransitionKey.OBSERVATION) or {}
         batch_size = 1
-        if obs:
-            for v in obs.values():
-                if isinstance(v, torch.Tensor):
-                    batch_size = v.shape[0]
-                    break
+        for v in obs.values():
+            if isinstance(v, torch.Tensor):
+                batch_size = v.shape[0]
+                break
 
         # Add domain_id tensor
-        comp["domain_id"] = torch.tensor([int(self.domain_id)] * batch_size, dtype=torch.long)
+        complementary_data["domain_id"] = torch.tensor([int(self.domain_id)] * batch_size, dtype=torch.long)
 
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp
-        return new_transition
+        return complementary_data
 
     def transform_features(self, features):
         """Domain ID addition doesn't change feature structure."""
@@ -448,7 +423,7 @@ class XVLAAddDomainIdProcessorStep(ProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="xvla_rotation_6d_to_axis_angle")
-class XVLARotation6DToAxisAngleProcessorStep(ProcessorStep):
+class XVLARotation6DToAxisAngleProcessorStep(PolicyActionProcessorStep):
     """Convert 6D rotation representation to axis-angle and reorganize action dimensions.
 
     This processor step takes actions with 6D rotation representation and converts them to
@@ -465,14 +440,10 @@ class XVLARotation6DToAxisAngleProcessorStep(ProcessorStep):
 
     expected_action_dim: int = 10
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    skip_if_missing = True
+
+    def action(self, action: PolicyAction) -> PolicyAction:
         """Convert 6D rotation to axis-angle in action."""
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-
-        if action is None or not isinstance(action, torch.Tensor):
-            return new_transition
-
         # Convert to numpy for processing
         device = action.device
         dtype = action.dtype
@@ -494,10 +465,7 @@ class XVLARotation6DToAxisAngleProcessorStep(ProcessorStep):
         action_np[:, -1] = np.where(action_np[:, -1] > 0.5, 1.0, -1.0)
 
         # Convert back to tensor
-        action = torch.from_numpy(action_np).to(device=device, dtype=dtype)
-
-        new_transition[TransitionKey.ACTION] = action
-        return new_transition
+        return torch.from_numpy(action_np).to(device=device, dtype=dtype)
 
     def transform_features(self, features):
         """Rotation conversion changes action dimension from 10 to 7."""

src/lerobot/processor/__init__.py

@@ -53,13 +53,15 @@ from .factory import (
 )
 from .gym_action_processor import (
     Numpy2TorchActionProcessorStep,
+    Numpy2TorchTeleopActionProcessorStep,
     Torch2NumpyActionProcessorStep,
 )
 from .hil_processor import (
     AddTeleopActionAsComplimentaryDataStep,
     AddTeleopEventsAsInfoStep,
     GripperPenaltyProcessorStep,
-    GymHILAdapterProcessorStep,
+    GymHILInfoAdapterStep,
+    GymHILTeleopDataAdapterStep,
     ImageCropResizeProcessorStep,
     InterventionActionProcessorStep,
     RewardClassifierProcessorStep,
@@ -126,7 +128,8 @@ __all__ = [
     "DoneProcessorStep",
     "EnvAction",
     "EnvTransition",
-    "GymHILAdapterProcessorStep",
+    "GymHILInfoAdapterStep",
+    "GymHILTeleopDataAdapterStep",
     "GripperPenaltyProcessorStep",
     "hotswap_stats",
     "IdentityProcessorStep",
@@ -148,6 +151,7 @@ __all__ = [
     "NewLineTaskProcessorStep",
     "NormalizerProcessorStep",
     "Numpy2TorchActionProcessorStep",
+    "Numpy2TorchTeleopActionProcessorStep",
     "ObservationProcessorStep",
     "PolicyAction",
     "PolicyActionProcessorStep",

src/lerobot/processor/batch_processor.py

@@ -175,6 +175,9 @@ class AddBatchDimensionComplementaryDataStep(ComplementaryDataProcessorStep):
             if isinstance(task_index_value, Tensor) and task_index_value.dim() == 0:
                 complementary_data["task_index"] = task_index_value.unsqueeze(0)
 
+        complementary_data.pop("language_persistent", None)
+        complementary_data.pop("language_events", None)
+
         if "messages" in complementary_data:
             messages = complementary_data["messages"]
             if isinstance(messages, list) and (not messages or isinstance(messages[0], dict)):
@@ -217,12 +220,10 @@ class AddBatchDimensionProcessorStep(ProcessorStep):
     This step combines individual processors for actions, observations, and complementary data
     to create a batched transition (batch size 1) from a single-instance transition.
 
-    **Attributes**:
-        - **to_batch_action_processor** (`AddBatchDimensionActionStep`) -- Processor for the action component.
-        - **to_batch_observation_processor** (`AddBatchDimensionObservationStep`) -- Processor for the
-          observation component.
-        - **to_batch_complementary_data_processor** (`AddBatchDimensionComplementaryDataStep`) -- Processor
-          for the complementary data component.
+    Attributes:
+        to_batch_action_processor: Processor for the action component.
+        to_batch_observation_processor: Processor for the observation component.
+        to_batch_complementary_data_processor: Processor for the complementary data component.
     """
 
     to_batch_action_processor: AddBatchDimensionActionStep = field(

src/lerobot/processor/delta_action_processor.py

@@ -32,8 +32,9 @@ class MapTensorToDeltaActionDictStep(ActionProcessorStep):
     It decomposes the vector into named components for delta movements of the
     end-effector (x, y, z) and optionally the gripper.
 
-    **Attributes**:
-        - **use_gripper** (`bool`) -- If True, assumes the 4th element of the tensor is the gripper action.
+    Attributes:
+        use_gripper: If True, assumes the 4th element of the tensor is the
+                     gripper action.
     """
 
     use_gripper: bool = True
@@ -80,10 +81,10 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
     into a target action format that includes an "enabled" flag and target
     end-effector positions. It also handles scaling and noise filtering.
 
-    **Attributes**:
-        - **position_scale** (`float`) -- A factor to scale the delta position inputs.
-        - **noise_threshold** (`float`) -- The magnitude below which delta inputs are considered noise and do
-          not trigger an "enabled" state.
+    Attributes:
+        position_scale: A factor to scale the delta position inputs.
+        noise_threshold: The magnitude below which delta inputs are considered noise
+                         and do not trigger an "enabled" state.
     """
 
     # Scale factors for delta movements

src/lerobot/processor/device_processor.py

@@ -40,10 +40,10 @@ class DeviceProcessorStep(ProcessorStep):
 
     This is crucial for preparing data for model training or inference on hardware like GPUs.
 
-    **Attributes**:
-        - **device** (`str`) -- The target device for tensors (e.g., "cpu", "cuda", "cuda:0").
-        - **float_dtype** (`str | None`) -- The target floating-point dtype as a string (e.g., "float32",
-          "float16", "bfloat16"). If None, the dtype is not changed.
+    Attributes:
+        device: The target device for tensors (e.g., "cpu", "cuda", "cuda:0").
+        float_dtype: The target floating-point dtype as a string (e.g., "float32", "float16", "bfloat16").
+                     If None, the dtype is not changed.
     """
 
     device: str = "cpu"

src/lerobot/processor/gym_action_processor.py

@@ -17,11 +17,11 @@
 from dataclasses import dataclass
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvAction, EnvTransition, PolicyAction, TransitionKey
+from lerobot.lerobot_types import EnvAction, PolicyAction
 
 from .converters import to_tensor
 from .hil_processor import TELEOP_ACTION_KEY
-from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry
+from .pipeline import ActionProcessorStep, ComplementaryDataProcessorStep, ProcessorStepRegistry
 
 
 @ProcessorStepRegistry.register("torch2numpy_action_processor")
@@ -33,9 +33,10 @@ class Torch2NumpyActionProcessorStep(ActionProcessorStep):
     This step is useful when the output of a policy (typically a torch.Tensor)
     needs to be passed to an environment or component that expects a NumPy array.
 
-    **Attributes**:
-        - **squeeze_batch_dim** (`bool`) -- If True, removes the first dimension of the array if it is of size
-          1. This is useful for converting a batched action of size (1, D) to a single action of size (D,).
+    Attributes:
+        squeeze_batch_dim: If True, removes the first dimension of the array
+                           if it is of size 1. This is useful for converting a
+                           batched action of size (1, D) to a single action of size (D,).
     """
 
     squeeze_batch_dim: bool = True
@@ -69,32 +70,36 @@ class Torch2NumpyActionProcessorStep(ActionProcessorStep):
 
 @ProcessorStepRegistry.register("numpy2torch_action_processor")
 @dataclass
-class Numpy2TorchActionProcessorStep(ProcessorStep):
+class Numpy2TorchActionProcessorStep(ActionProcessorStep):
     """Converts a NumPy array action to a PyTorch tensor when action is present."""
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        """Converts numpy action to torch tensor if action exists, otherwise passes through."""
-        self._current_transition = transition.copy()
-        new_transition = self._current_transition
+    skip_if_missing = True
 
-        action = new_transition.get(TransitionKey.ACTION)
-        if action is not None:
-            if not isinstance(action, EnvAction):
-                raise TypeError(
-                    f"Expected np.ndarray or None, got {type(action).__name__}. "
-                    "Use appropriate processor for non-tensor actions."
-                )
-            torch_action = to_tensor(action, dtype=None)  # Preserve original dtype
-            new_transition[TransitionKey.ACTION] = torch_action
-
-        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        if TELEOP_ACTION_KEY in complementary_data:
-            teleop_action = complementary_data[TELEOP_ACTION_KEY]
-            if isinstance(teleop_action, EnvAction):
-                complementary_data[TELEOP_ACTION_KEY] = to_tensor(teleop_action)
-            new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
-
-        return new_transition
+    def action(self, action: EnvAction) -> PolicyAction:
+        if not isinstance(action, EnvAction):
+            raise TypeError(
+                f"Expected np.ndarray or None, got {type(action).__name__}. "
+                "Use appropriate processor for non-tensor actions."
+            )
+        return to_tensor(action, dtype=None)  # Preserve original dtype
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+
+@ProcessorStepRegistry.register("numpy2torch_teleop_action_processor")
+@dataclass
+class Numpy2TorchTeleopActionProcessorStep(ComplementaryDataProcessorStep):
+    """Converts a NumPy teleop action in the complementary data to a PyTorch tensor."""
+
+    def complementary_data(self, complementary_data: dict) -> dict:
+        if TELEOP_ACTION_KEY in complementary_data:
+            teleop_action = complementary_data[TELEOP_ACTION_KEY]
+            if isinstance(teleop_action, EnvAction):
+                complementary_data[TELEOP_ACTION_KEY] = to_tensor(teleop_action)
+        return complementary_data
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]

src/lerobot/processor/hil_processor.py

@@ -101,8 +101,8 @@ class AddTeleopActionAsComplimentaryDataStep(ComplementaryDataProcessorStep):
     be available to downstream processors, for example, to override a policy's action
     during an intervention.
 
-    **Attributes**:
-        - **teleop_device** (`Teleoperator`) -- The teleoperator instance to get the action from.
+    Attributes:
+        teleop_device: The teleoperator instance to get the action from.
     """
 
     teleop_device: "Teleoperator"
@@ -137,9 +137,9 @@ class AddTeleopEventsAsInfoStep(InfoProcessorStep):
     This step extracts control events from teleoperators that support event-based
     interaction, making these signals available to other parts of the system.
 
-    **Attributes**:
-        - **teleop_device** (`TeleopWithEvents`) -- An instance of a teleoperator that implements the
-          `HasTeleopEvents` protocol.
+    Attributes:
+        teleop_device: An instance of a teleoperator that implements the
+                       `HasTeleopEvents` protocol.
     """
 
     teleop_device: TeleopWithEvents
@@ -180,10 +180,10 @@ class ImageCropResizeProcessorStep(ObservationProcessorStep):
     the specified transformations. It handles device placement, moving tensors to the
     CPU if necessary for operations not supported on certain accelerators like MPS.
 
-    **Attributes**:
-        - **crop_params_dict** (`dict[str, tuple[int, int, int, int]] | None`) -- A dictionary mapping image
-          keys to cropping parameters (top, left, height, width).
-        - **resize_size** (`tuple[int, int] | None`) -- A tuple (height, width) to resize all images to.
+    Attributes:
+        crop_params_dict: A dictionary mapping image keys to cropping parameters
+                          (top, left, height, width).
+        resize_size: A tuple (height, width) to resize all images to.
     """
 
     crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
@@ -267,9 +267,9 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
     """
     Tracks episode steps and enforces a time limit by truncating the episode.
 
-    **Attributes**:
-        - **max_episode_steps** (`int`) -- The maximum number of steps allowed per episode.
-        - **current_step** (`int`) -- The current step count for the active episode.
+    Attributes:
+        max_episode_steps: The maximum number of steps allowed per episode.
+        current_step: The current step count for the active episode.
     """
 
     max_episode_steps: int
@@ -312,20 +312,39 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
         return features
 
 
-@ProcessorStepRegistry.register("gym_hil_adapter_processor")
-class GymHILAdapterProcessorStep(ProcessorStep):
+@ProcessorStepRegistry.register("gym_hil_info_adapter")
+class GymHILInfoAdapterStep(InfoProcessorStep):
     """
-    Adapts the output of the `gym-hil` environment to the format expected by `lerobot` processors.
+    Adapts the `info` dictionary of the `gym-hil` environment to the format expected by
+    `lerobot` processors.
 
-    This step normalizes the `transition` object by:
-    1. Copying `teleop_action` from `info` to `complementary_data`.
-    2. Copying `is_intervention` from `info` (using the string key) to `info` (using the enum key).
-    3. Copying `discrete_penalty` from `info` to `complementary_data`.
+    Mirrors `is_intervention` from the string key to the `TeleopEvents.IS_INTERVENTION`
+    enum key when present.
     """
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        info = transition.get(TransitionKey.INFO, {})
-        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+    def info(self, info: dict) -> dict:
+        if "is_intervention" in info:
+            info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
+        return info
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+
+@ProcessorStepRegistry.register("gym_hil_teleop_data_adapter")
+class GymHILTeleopDataAdapterStep(ComplementaryDataProcessorStep):
+    """
+    Copies teleoperation data emitted by the `gym-hil` environment from `info` into the
+    transition's complementary data.
+
+    Copies `teleop_action` and `discrete_penalty` from `info` to `complementary_data`
+    when present.
+    """
+
+    def complementary_data(self, complementary_data: dict) -> dict:
+        info = self.transition.get(TransitionKey.INFO) or {}
 
         if TELEOP_ACTION_KEY in info:
             complementary_data[TELEOP_ACTION_KEY] = info[TELEOP_ACTION_KEY]
@@ -333,13 +352,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
         if DISCRETE_PENALTY_KEY in info:
             complementary_data[DISCRETE_PENALTY_KEY] = info[DISCRETE_PENALTY_KEY]
 
-        if "is_intervention" in info:
-            info[TeleopEvents.IS_INTERVENTION] = info["is_intervention"]
-
-        transition[TransitionKey.INFO] = info
-        transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
-
-        return transition
+        return complementary_data
 
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
@@ -349,7 +362,7 @@ class GymHILAdapterProcessorStep(ProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register("gripper_penalty_processor")
-class GripperPenaltyProcessorStep(ProcessorStep):
+class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep):
     """
     Applies a small per-transition cost on the discrete gripper action.
 
@@ -358,11 +371,11 @@ class GripperPenaltyProcessorStep(ProcessorStep):
     This discourages gripper oscillation while leaving "stay" and saturating-further
     commands unpenalized.
 
-    **Attributes**:
-        - **penalty** (`float`) -- The negative reward value to apply.
-        - **max_gripper_pos** (`float`) -- The maximum position value for the gripper, used for normalization.
-        - **open_threshold** (`float`) -- Normalized state below which the gripper is considered "open".
-        - **closed_threshold** (`float`) -- Normalized state above which the gripper is considered "closed".
+    Attributes:
+        penalty: The negative reward value to apply.
+        max_gripper_pos: The maximum position value for the gripper, used for normalization.
+        open_threshold: Normalized state below which the gripper is considered "open".
+        closed_threshold: Normalized state above which the gripper is considered "closed".
     """
 
     penalty: float = -0.02
@@ -370,31 +383,30 @@ class GripperPenaltyProcessorStep(ProcessorStep):
     open_threshold: float = 0.1
     closed_threshold: float = 0.9
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    def complementary_data(self, complementary_data: dict) -> dict:
         """
         Calculates the gripper penalty and adds it to the complementary data.
 
         Args:
-            transition: The incoming environment transition.
+            complementary_data: The incoming complementary data dictionary.
 
         Returns:
-            The modified transition with the penalty added to complementary data.
+            The complementary data with the penalty added under the
+            `discrete_penalty` key.
         """
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+        action = self.transition.get(TransitionKey.ACTION)
 
         raw_joint_positions = complementary_data.get("raw_joint_positions")
         if raw_joint_positions is None:
-            return new_transition
+            return complementary_data
 
         current_gripper_pos = raw_joint_positions.get(f"{GRIPPER_KEY}.pos", None)
         if current_gripper_pos is None:
-            return new_transition
+            return complementary_data
 
         # During reset, the transition may not carry any action yet.
         if action is None:
-            return new_transition
+            return complementary_data
 
         # Gripper action is expected as the last action dimension.
         gripper_action = action[-1].item()
@@ -414,12 +426,8 @@ class GripperPenaltyProcessorStep(ProcessorStep):
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
-        # Update complementary data with penalty info
-        new_complementary_data = dict(complementary_data)
-        new_complementary_data[DISCRETE_PENALTY_KEY] = gripper_penalty
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
-
-        return new_transition
+        complementary_data[DISCRETE_PENALTY_KEY] = gripper_penalty
+        return complementary_data
 
     def get_config(self) -> dict[str, Any]:
         """
@@ -436,10 +444,6 @@ class GripperPenaltyProcessorStep(ProcessorStep):
             "closed_threshold": self.closed_threshold,
         }
 
-    def reset(self) -> None:
-        """Resets the processor's internal state."""
-        pass
-
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
@@ -456,10 +460,10 @@ class InterventionActionProcessorStep(ProcessorStep):
     this step replaces the policy's action with the human's teleoperated action.
     It also processes signals to terminate the episode or flag success.
 
-    **Attributes**:
-        - **use_gripper** (`bool`) -- Whether to include the gripper in the teleoperated action.
-        - **terminate_on_success** (`bool`) -- If True, automatically sets the `done` flag when a `success`
-          event is received.
+    Attributes:
+        use_gripper: Whether to include the gripper in the teleoperated action.
+        terminate_on_success: If True, automatically sets the `done` flag when a
+                              `success` event is received.
     """
 
     use_gripper: bool = False
@@ -557,13 +561,13 @@ class RewardClassifierProcessorStep(ProcessorStep):
     This step uses a model to determine if the current state is successful, updating
     the reward and potentially terminating the episode.
 
-    **Attributes**:
-        - **pretrained_path** (`str | None`) -- Path to the pretrained reward classifier model.
-        - **device** (`str`) -- The device to run the classifier on.
-        - **success_threshold** (`float`) -- The probability threshold to consider a prediction as successful.
-        - **success_reward** (`float`) -- The reward value to assign on success.
-        - **terminate_on_success** (`bool`) -- If True, terminates the episode upon successful classification.
-        - **reward_classifier** (`Any`) -- The loaded classifier model instance.
+    Attributes:
+        pretrained_path: Path to the pretrained reward classifier model.
+        device: The device to run the classifier on.
+        success_threshold: The probability threshold to consider a prediction as successful.
+        success_reward: The reward value to assign on success.
+        terminate_on_success: If True, terminates the episode upon successful classification.
+        reward_classifier: The loaded classifier model instance.
     """
 
     pretrained_path: str | None = None

src/lerobot/processor/migrate_policy_normalization.py

@@ -647,15 +647,10 @@ def main():
     tags = set(tags).union({"robotics", "lerobot", policy_type})
     tags = list(tags)
 
-    # Generate model card through the free helper (PreTrainedPolicy.generate_model_card was
-    # removed with the publisher redesign), then apply the metadata recovered above — the
-    # migrated policy config does not carry the original repo's card fields.
-    from lerobot.common.train_utils import generate_model_card
-
-    card = generate_model_card(policy.config)
-    card.data.datasets = dataset_repo_id
-    card.data.license = license
-    card.data.tags = sorted(tags)
+    # Generate model card
+    card = policy.generate_model_card(
+        dataset_repo_id=dataset_repo_id, model_type=policy_type, license=license, tags=tags
+    )
 
     # Save model card locally
     card.save(str(output_dir / "README.md"))

src/lerobot/processor/normalize_processor.py

@@ -71,23 +71,22 @@ class _NormalizationMixin:
         )
         ```
 
-    **Attributes**:
-        - **features** (`dict[str, PolicyFeature]`) -- A dictionary mapping feature names to `PolicyFeature`
-          objects, defining the data structure to be processed.
-        - **norm_map** (`dict[FeatureType, NormalizationMode]`) -- A dictionary mapping `FeatureType` to
-          `NormalizationMode`, specifying which normalization method to use for each type of feature.
-        - **stats** (`dict[str, dict[str, Any]] | None`) -- A dictionary containing the normalization
-          statistics (e.g., mean, std, min, max) for each feature.
-        - **device** (`torch.device | str | None`) -- The PyTorch device on which to store and perform tensor
-          operations.
-        - **eps** (`float`) -- A small epsilon value to prevent division by zero in normalization
-          calculations.
-        - **normalize_observation_keys** (`set[str] | None`) -- An optional set of keys to selectively apply
-          normalization to specific observation features.
-        - **_tensor_stats** (`dict[str, dict[str, Tensor]]`) -- An internal dictionary holding the
-          normalization statistics as PyTorch tensors.
-        - **_stats_explicitly_provided** (`bool`) -- Internal flag tracking whether stats were explicitly
-          provided during construction (used for override preservation).
+    Attributes:
+        features: A dictionary mapping feature names to `PolicyFeature` objects, defining
+            the data structure to be processed.
+        norm_map: A dictionary mapping `FeatureType` to `NormalizationMode`, specifying
+            which normalization method to use for each type of feature.
+        stats: A dictionary containing the normalization statistics (e.g., mean, std,
+            min, max) for each feature.
+        device: The PyTorch device on which to store and perform tensor operations.
+        eps: A small epsilon value to prevent division by zero in normalization
+            calculations.
+        normalize_observation_keys: An optional set of keys to selectively apply
+            normalization to specific observation features.
+        _tensor_stats: An internal dictionary holding the normalization statistics as
+            PyTorch tensors.
+        _stats_explicitly_provided: Internal flag tracking whether stats were explicitly
+            provided during construction (used for override preservation).
     """
 
     features: dict[str, PolicyFeature]

src/lerobot/processor/pipeline.py

@@ -38,10 +38,10 @@ from collections.abc import Callable, Iterable, Sequence
 from copy import deepcopy
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, TypedDict, TypeVar, cast
+from typing import Any, ClassVar, TypedDict, TypeVar, cast
 
 import torch
-from huggingface_hub import hf_hub_download, snapshot_download
+from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file, save_file
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
@@ -159,6 +159,14 @@ class ProcessorStep(ABC):
 
     _current_transition: EnvTransition | None = None
 
+    # Consulted by the specialized single-field bases (ObservationProcessorStep, ActionProcessorStep,
+    # etc.): when True, the step is skipped (the transition is returned unchanged) if its target field
+    # is None, instead of raising a ValueError. Set it as a plain class attribute in subclasses
+    # (`skip_if_missing = True`) so that dataclass steps don't pick it up as a field. Use it for steps
+    # that must tolerate partial transitions, e.g. action steps in a preprocessor that also runs at
+    # inference time (where the action is None) or steps in RL pipelines that run on reset transitions.
+    skip_if_missing: ClassVar[bool] = False
+
     @property
     def transition(self) -> EnvTransition:
         """Provides access to the most recent transition being processed.
@@ -212,10 +220,6 @@ class ProcessorStep(ABC):
         """
         return None
 
-    def save_artifacts(self, save_directory: Path) -> dict[str, str]:
-        """Save non-tensor assets and map constructor arguments to relative paths."""
-        return {}
-
     def reset(self) -> None:
         """Resets the internal state of the processor step, if any."""
         return None
@@ -269,18 +273,13 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
     data processing workflow. It's generic, allowing for custom input and output types,
     which are handled by the `to_transition` and `to_output` converters.
 
-    **Attributes**:
-        - **steps** (`Sequence[ProcessorStep]`) -- A sequence of `ProcessorStep` objects that make up the
-          pipeline.
-        - **name** (`str`) -- A descriptive name for the pipeline.
-        - **to_transition** (`Callable[[TInput], EnvTransition]`) -- A function to convert raw input data into
-          the standardized `EnvTransition` format.
-        - **to_output** (`Callable[[EnvTransition], TOutput]`) -- A function to convert the final
-          `EnvTransition` into the desired output format.
-        - **before_step_hooks** (`list[Callable[[int, EnvTransition], None]]`) -- A list of functions to be
-          called before each step is executed.
-        - **after_step_hooks** (`list[Callable[[int, EnvTransition], None]]`) -- A list of functions to be
-          called after each step is executed.
+    Attributes:
+        steps: A sequence of `ProcessorStep` objects that make up the pipeline.
+        name: A descriptive name for the pipeline.
+        to_transition: A function to convert raw input data into the standardized `EnvTransition` format.
+        to_output: A function to convert the final `EnvTransition` into the desired output format.
+        before_step_hooks: A list of functions to be called before each step is executed.
+        after_step_hooks: A list of functions to be called after each step is executed.
     """
 
     steps: Sequence[ProcessorStep] = field(default_factory=list)
@@ -565,22 +564,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         pipeline_config = self.get_config()
         pipeline_state_dict = self.state_dict()
 
-        for processor_step, step_entry in zip(self.steps, pipeline_config["steps"], strict=True):
-            artifacts = processor_step.save_artifacts(save_directory)
-            if artifacts:
-                for config_key, relative_path in artifacts.items():
-                    artifact_path = Path(relative_path)
-                    if artifact_path.is_absolute() or ".." in artifact_path.parts:
-                        raise ValueError(
-                            f"Processor artifact path must be relative to the checkpoint: {relative_path!r}"
-                        )
-                    if not (save_directory / artifact_path).exists():
-                        raise FileNotFoundError(
-                            f"Processor step did not save declared artifact '{relative_path}'"
-                        )
-                    step_entry["config"][config_key] = artifact_path.as_posix()
-                step_entry["artifacts"] = artifacts
-
         for state_key, step_state_dict in pipeline_state_dict.items():
             state_filename = f"{state_key}.safetensors"
             save_file(step_state_dict, save_directory / state_filename)
@@ -765,13 +748,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 
         # 3. Build steps with overrides
         steps, validated_overrides = cls._build_steps_with_overrides(
-            loaded_config,
-            overrides or {},
-            model_id,
-            base_path,
-            config_filename,
-            hub_download_kwargs,
-            is_local_source,
+            loaded_config, overrides or {}, model_id, base_path, hub_download_kwargs, is_local_source
         )
 
         # 4. Validate that all overrides were used
@@ -967,7 +944,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         overrides: dict[str, Any],
         model_id: str,
         base_path: Path | None,
-        config_filename: str,
         hub_download_kwargs: dict[str, Any],
         is_local_source: bool = False,
     ) -> tuple[list[ProcessorStep], set[str]]:
@@ -977,11 +953,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
 
         **For each step in loaded_config["steps"]**:
 
-        0. **Artifact Resolution** (via _resolve_artifact_paths):
-           - Resolve declared relative artifact paths against a local checkpoint
-           - Download declared artifacts when loading the pipeline from the Hub
-           - Reject absolute paths and path traversal before step construction
-
         1. **Class Resolution** (via _resolve_step_class):
            - **If "registry_name" exists**: Look up in ProcessorStepRegistry
              Example: {"registry_name": "normalize_step"} -> Get registered class
@@ -1015,8 +986,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             overrides: User-provided parameter overrides (keyed by class/registry name)
             model_id: The model identifier (needed for Hub state file downloads)
             base_path: Local directory path for finding state files
-            config_filename: Processor config path, used as the repository-relative
-                base for state files and declared artifacts.
             hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
             is_local_source: Whether model_id resolved to a local directory or config file.
 
@@ -1029,80 +998,15 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             ImportError: If a step class cannot be imported or found in registry
             ValueError: If a step cannot be instantiated with its configuration
         """
-        loaded_config = deepcopy(loaded_config)
-        cls._resolve_artifact_paths(
-            loaded_config,
-            model_id,
-            base_path,
-            config_filename,
-            hub_download_kwargs,
-        )
         steps, remaining_override_keys = cls._build_steps_from_config(loaded_config, overrides)
 
         for step_instance, step_entry in zip(steps, loaded_config["steps"], strict=True):
             cls._load_step_state(
-                step_instance,
-                step_entry,
-                model_id,
-                base_path,
-                config_filename,
-                hub_download_kwargs,
-                is_local_source,
+                step_instance, step_entry, model_id, base_path, hub_download_kwargs, is_local_source
             )
 
         return steps, remaining_override_keys
 
-    @classmethod
-    def _resolve_artifact_paths(
-        cls,
-        loaded_config: dict[str, Any],
-        model_id: str,
-        base_path: Path | None,
-        config_filename: str,
-        hub_download_kwargs: dict[str, Any],
-    ) -> None:
-        """Resolve declared relative processor artifacts before step construction.
-
-        Args:
-            loaded_config: Mutable processor configuration containing step artifact declarations.
-            model_id: Local checkpoint path or Hub model identifier.
-            base_path: Local directory containing the resolved processor configuration.
-            config_filename: Processor config path, whose parent is the artifact root on the Hub.
-            hub_download_kwargs: Authentication, revision, and cache arguments for Hub downloads.
-
-        Raises:
-            ValueError: If a declared artifact path is absolute or escapes the checkpoint.
-            FileNotFoundError: If a declared artifact cannot be found locally or downloaded.
-        """
-        is_local = Path(model_id).is_dir() or Path(model_id).is_file()
-
-        for step_entry in loaded_config["steps"]:
-            artifacts = step_entry.get("artifacts", {})
-            for config_key, relative_path in artifacts.items():
-                artifact_path = Path(relative_path)
-                if artifact_path.is_absolute() or ".." in artifact_path.parts:
-                    raise ValueError(
-                        f"Processor artifact path must be relative to the checkpoint: {relative_path!r}"
-                    )
-
-                resolved_path = base_path / artifact_path if base_path is not None else artifact_path
-                if not resolved_path.exists() and not is_local:
-                    repository_path = Path(config_filename).parent / artifact_path
-                    snapshot_download(
-                        repo_id=model_id,
-                        repo_type="model",
-                        allow_patterns=f"{repository_path.as_posix()}/**",
-                        **hub_download_kwargs,
-                    )
-
-                if not resolved_path.exists():
-                    step_name = step_entry.get("registry_name", step_entry.get("class", "unknown"))
-                    raise FileNotFoundError(
-                        f"Missing processor artifact '{relative_path}' for step '{step_name}' "
-                        f"next to '{config_filename}'. Checkpoint artifacts are incomplete."
-                    )
-                step_entry["config"][config_key] = str(resolved_path)
-
     @classmethod
     def _build_steps_from_config(
         cls,
@@ -1262,7 +1166,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
         step_entry: dict[str, Any],
         model_id: str,
         base_path: Path | None,
-        config_filename: str,
         hub_download_kwargs: dict[str, Any],
         is_local_source: bool = False,
     ) -> None:
@@ -1303,8 +1206,6 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             step_entry: The step configuration dictionary (may contain "state_file")
             model_id: The model identifier (used for Hub downloads if needed)
             base_path: Local directory path for finding state files (None for Hub-only)
-            config_filename: Processor config path, whose parent is used to resolve
-                repository-relative state files on the Hub.
             hub_download_kwargs: Parameters for hf_hub_download (tokens, cache, etc.)
             is_local_source: Whether model_id resolved to a local directory or config file.
 
@@ -1330,7 +1231,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
             # Download from Hub
             state_path = hf_hub_download(
                 repo_id=model_id,
-                filename=(Path(config_filename).parent / state_filename).as_posix(),
+                filename=state_filename,
                 repo_type="model",
                 **hub_download_kwargs,
             )
@@ -1860,7 +1761,12 @@ PolicyProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
 
 
 class ObservationProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the observation in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the observation in a transition.
+
+    The `observation` hook may read other parts of the transition via `self.transition`, but only the
+    observation may be written. Set `skip_if_missing = True` on a subclass to skip the step (instead of
+    raising) when the transition carries no observation.
+    """
 
     @abstractmethod
     def observation(self, observation: RobotObservation) -> RobotObservation:
@@ -1880,6 +1786,8 @@ class ObservationProcessorStep(ProcessorStep, ABC):
         new_transition = self._current_transition
 
         observation = new_transition.get(TransitionKey.OBSERVATION)
+        if observation is None and self.skip_if_missing:
+            return new_transition
         if observation is None or not isinstance(observation, dict):
             raise ValueError("ObservationProcessorStep requires an observation in the transition.")
 
@@ -1889,7 +1797,12 @@ class ObservationProcessorStep(ProcessorStep, ABC):
 
 
 class ActionProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the action in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the action in a transition.
+
+    The `action` hook may read other parts of the transition via `self.transition`, but only the action
+    may be written. Set `skip_if_missing = True` on a subclass to skip the step (instead of raising)
+    when the transition carries no action, e.g. for steps in pipelines that also run at inference time.
+    """
 
     @abstractmethod
     def action(
@@ -1912,6 +1825,8 @@ class ActionProcessorStep(ProcessorStep, ABC):
 
         action = new_transition.get(TransitionKey.ACTION)
         if action is None:
+            if self.skip_if_missing:
+                return new_transition
             raise ValueError("ActionProcessorStep requires an action in the transition.")
 
         processed_action = self.action(action)
@@ -1920,7 +1835,12 @@ class ActionProcessorStep(ProcessorStep, ABC):
 
 
 class RobotActionProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` for processing a `RobotAction` (a dictionary)."""
+    """An abstract `ProcessorStep` for processing a `RobotAction` (a dictionary).
+
+    The `action` hook may read other parts of the transition via `self.transition`, but only the action
+    may be written. Set `skip_if_missing = True` on a subclass to skip the step (instead of raising)
+    when the transition carries no action.
+    """
 
     @abstractmethod
     def action(self, action: RobotAction) -> RobotAction:
@@ -1940,6 +1860,8 @@ class RobotActionProcessorStep(ProcessorStep, ABC):
         new_transition = self._current_transition
 
         action = new_transition.get(TransitionKey.ACTION)
+        if action is None and self.skip_if_missing:
+            return new_transition
         if action is None or not isinstance(action, dict):
             raise ValueError(f"Action should be a RobotAction type (dict), but got {type(action)}")
 
@@ -1949,7 +1871,12 @@ class RobotActionProcessorStep(ProcessorStep, ABC):
 
 
 class PolicyActionProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` for processing a `PolicyAction` (a tensor or dict of tensors)."""
+    """An abstract `ProcessorStep` for processing a `PolicyAction` (a tensor).
+
+    The `action` hook may read other parts of the transition via `self.transition`, but only the action
+    may be written. Set `skip_if_missing = True` on a subclass to skip the step (instead of raising)
+    when the transition carries no action, e.g. for steps in pipelines that also run at inference time.
+    """
 
     @abstractmethod
     def action(self, action: PolicyAction) -> PolicyAction:
@@ -1969,6 +1896,8 @@ class PolicyActionProcessorStep(ProcessorStep, ABC):
         new_transition = self._current_transition
 
         action = new_transition.get(TransitionKey.ACTION)
+        if action is None and self.skip_if_missing:
+            return new_transition
         if not isinstance(action, PolicyAction):
             raise ValueError(f"Action should be a PolicyAction type (tensor), but got {type(action)}")
 
@@ -1978,7 +1907,11 @@ class PolicyActionProcessorStep(ProcessorStep, ABC):
 
 
 class RewardProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the reward in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the reward in a transition.
+
+    Set `skip_if_missing = True` on a subclass to skip the step (instead of raising) when the
+    transition carries no reward.
+    """
 
     @abstractmethod
     def reward(self, reward) -> float | torch.Tensor:
@@ -1999,6 +1932,8 @@ class RewardProcessorStep(ProcessorStep, ABC):
 
         reward = new_transition.get(TransitionKey.REWARD)
         if reward is None:
+            if self.skip_if_missing:
+                return new_transition
             raise ValueError("RewardProcessorStep requires a reward in the transition.")
 
         processed_reward = self.reward(reward)
@@ -2007,7 +1942,11 @@ class RewardProcessorStep(ProcessorStep, ABC):
 
 
 class DoneProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the 'done' flag in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the 'done' flag in a transition.
+
+    Set `skip_if_missing = True` on a subclass to skip the step (instead of raising) when the
+    transition carries no 'done' flag.
+    """
 
     @abstractmethod
     def done(self, done) -> bool | torch.Tensor:
@@ -2028,6 +1967,8 @@ class DoneProcessorStep(ProcessorStep, ABC):
 
         done = new_transition.get(TransitionKey.DONE)
         if done is None:
+            if self.skip_if_missing:
+                return new_transition
             raise ValueError("DoneProcessorStep requires a done flag in the transition.")
 
         processed_done = self.done(done)
@@ -2036,7 +1977,11 @@ class DoneProcessorStep(ProcessorStep, ABC):
 
 
 class TruncatedProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the 'truncated' flag in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the 'truncated' flag in a transition.
+
+    Set `skip_if_missing = True` on a subclass to skip the step (instead of raising) when the
+    transition carries no 'truncated' flag.
+    """
 
     @abstractmethod
     def truncated(self, truncated) -> bool | torch.Tensor:
@@ -2057,6 +2002,8 @@ class TruncatedProcessorStep(ProcessorStep, ABC):
 
         truncated = new_transition.get(TransitionKey.TRUNCATED)
         if truncated is None:
+            if self.skip_if_missing:
+                return new_transition
             raise ValueError("TruncatedProcessorStep requires a truncated flag in the transition.")
 
         processed_truncated = self.truncated(truncated)
@@ -2065,7 +2012,11 @@ class TruncatedProcessorStep(ProcessorStep, ABC):
 
 
 class InfoProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that specifically targets the 'info' dictionary in a transition."""
+    """An abstract `ProcessorStep` that specifically targets the 'info' dictionary in a transition.
+
+    The `info` hook may read other parts of the transition via `self.transition`, but only the info
+    dictionary may be written.
+    """
 
     @abstractmethod
     def info(self, info) -> dict[str, Any]:
@@ -2085,6 +2036,8 @@ class InfoProcessorStep(ProcessorStep, ABC):
         new_transition = self._current_transition
 
         info = new_transition.get(TransitionKey.INFO)
+        if info is None and self.skip_if_missing:
+            return new_transition
         if info is None or not isinstance(info, dict):
             raise ValueError("InfoProcessorStep requires an info dictionary in the transition.")
 
@@ -2094,7 +2047,11 @@ class InfoProcessorStep(ProcessorStep, ABC):
 
 
 class ComplementaryDataProcessorStep(ProcessorStep, ABC):
-    """An abstract `ProcessorStep` that targets the 'complementary_data' in a transition."""
+    """An abstract `ProcessorStep` that targets the 'complementary_data' in a transition.
+
+    The `complementary_data` hook may read other parts of the transition via `self.transition` (e.g. an
+    action or observation the step derives data from), but only the complementary data may be written.
+    """
 
     @abstractmethod
     def complementary_data(self, complementary_data) -> dict[str, Any]:
@@ -2114,6 +2071,8 @@ class ComplementaryDataProcessorStep(ProcessorStep, ABC):
         new_transition = self._current_transition
 
         complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA)
+        if complementary_data is None and self.skip_if_missing:
+            return new_transition
         if complementary_data is None or not isinstance(complementary_data, dict):
             raise ValueError("ComplementaryDataProcessorStep requires complementary data in the transition.")
 

src/lerobot/processor/relative_action_processor.py

@@ -20,11 +20,11 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
 from lerobot.utils.constants import OBS_STATE
 
 from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
-from .pipeline import ProcessorStep, ProcessorStepRegistry
+from .pipeline import PolicyActionProcessorStep, ProcessorStep, ProcessorStepRegistry
 
 # Re-export for backward compatibility
 __all__ = [
@@ -91,11 +91,11 @@ class RelativeActionsProcessorStep(ProcessorStep):
     Caches the last seen state so a paired AbsoluteActionsProcessorStep can reverse
     the conversion during postprocessing.
 
-    **Attributes**:
-        - **enabled** (`bool`) -- Whether to apply the relative conversion.
-        - **exclude_joints** (`list[str]`) -- Joint names to keep absolute (not converted to relative).
-        - **action_names** (`list[str] | None`) -- Action dimension names from dataset metadata, used to build
-          the mask from exclude_joints. If None, all dims are converted.
+    Attributes:
+        enabled: Whether to apply the relative conversion.
+        exclude_joints: Joint names to keep absolute (not converted to relative).
+        action_names: Action dimension names from dataset metadata, used to build
+            the mask from exclude_joints. If None, all dims are converted.
     """
 
     enabled: bool = False
@@ -161,25 +161,26 @@ class RelativeActionsProcessorStep(ProcessorStep):
 
 @ProcessorStepRegistry.register("absolute_actions_processor")
 @dataclass
-class AbsoluteActionsProcessorStep(ProcessorStep):
+class AbsoluteActionsProcessorStep(PolicyActionProcessorStep):
     """Converts relative actions back to absolute actions (action += state) for all dimensions.
 
     Mirrors OpenPI's AbsoluteActions transform. Applied during postprocessing so
     predicted relative offsets are converted back to absolute positions for execution.
     Reads the cached state from its paired RelativeActionsProcessorStep.
 
-    **Attributes**:
-        - **enabled** (`bool`) -- Whether to apply the absolute conversion.
-        - **relative_step** (`RelativeActionsProcessorStep | None`) -- Reference to the paired
-          RelativeActionsProcessorStep that caches state.
+    Attributes:
+        enabled: Whether to apply the absolute conversion.
+        relative_step: Reference to the paired RelativeActionsProcessorStep that caches state.
     """
 
     enabled: bool = False
     relative_step: RelativeActionsProcessorStep | None = field(default=None, repr=False)
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    skip_if_missing = True
+
+    def action(self, action: PolicyAction) -> PolicyAction:
         if not self.enabled:
-            return transition
+            return action
 
         if self.relative_step is None:
             raise RuntimeError(
@@ -194,14 +195,8 @@ class AbsoluteActionsProcessorStep(ProcessorStep):
                 "but no state has been cached. Ensure the preprocessor runs before the postprocessor."
             )
 
-        new_transition = transition.copy()
-        action = new_transition.get(TransitionKey.ACTION)
-        if action is None:
-            return new_transition
-
         mask = self.relative_step._build_mask(action.shape[-1])
-        new_transition[TransitionKey.ACTION] = to_absolute_actions(action, cached_state, mask)
-        return new_transition
+        return to_absolute_actions(action, cached_state, mask)
 
     def get_config(self) -> dict[str, Any]:
         return {"enabled": self.enabled}

src/lerobot/processor/rename_processor.py

@@ -32,9 +32,10 @@ class RenameObservationsProcessorStep(ObservationProcessorStep):
     from an environment's format to the format expected by a LeRobot policy or
     other downstream components.
 
-    **Attributes**:
-        - **rename_map** (`dict[str, str]`) -- A dictionary mapping from old key names to new key names. Keys
-          present in an observation that are not in this map will be kept with their original names.
+    Attributes:
+        rename_map: A dictionary mapping from old key names to new key names.
+                    Keys present in an observation that are not in this map will
+                    be kept with their original names.
     """
 
     rename_map: dict[str, str] = field(default_factory=dict)

src/lerobot/processor/render_messages_processor.py

@@ -16,11 +16,9 @@
 
 from __future__ import annotations
 
-from dataclasses import asdict, dataclass
+from dataclasses import dataclass
 from typing import Any
 
-import numpy as np
-
 from lerobot.configs import PipelineFeatureType, PolicyFeature
 from lerobot.configs.recipe import TrainingRecipe
 from lerobot.datasets.language import LANGUAGE_EVENTS, LANGUAGE_PERSISTENT
@@ -34,46 +32,25 @@ from .pipeline import ProcessorStep, ProcessorStepRegistry
 @dataclass
 @ProcessorStepRegistry.register(name="render_messages_processor")
 class RenderMessagesStep(ProcessorStep):
-    """Turn raw language columns into recipe-defined messages and supervision.
+    """Processor step that turns raw language columns into rendered chat messages.
 
-    Reads ``language_persistent`` and ``language_events`` from complementary
-    data, renders them at each sample timestamp, and replaces the raw columns
-    with ``messages``, ``message_streams``, and ``target_message_indices``.
-    Batched inputs are filtered to samples with applicable supervision; samples
-    without language annotations use their task string as low-level supervision
-    when one is available.
+    Reads ``language_persistent`` and ``language_events`` from the transition's
+    complementary data, renders them through ``recipe`` at the sample timestamp,
+    and replaces the raw columns with the resulting ``messages`` /
+    ``message_streams`` / ``target_message_indices`` keys.
     """
 
     recipe: TrainingRecipe
     dataset_ctx: Any | None = None
 
-    def __post_init__(self) -> None:
-        if isinstance(self.recipe, dict):
-            self.recipe = TrainingRecipe.from_dict(self.recipe)
-
-    def get_config(self) -> dict[str, Any]:
-        return {"recipe": asdict(self.recipe)}
-
     def __call__(self, transition: EnvTransition) -> EnvTransition | None:
-        """Render messages, preserving unannotated samples and dropping unmatched annotated ones."""
+        """Render messages for a single transition; return ``None`` to drop it."""
         complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
         persistent = complementary_data.get(LANGUAGE_PERSISTENT) or []
         events = complementary_data.get(LANGUAGE_EVENTS) or []
 
         if not persistent and not events:
-            # A dataset without language annotations remains usable: render its
-            # task as low-level supervision, or pass it through when no task exists.
-            rendered = _fallback_low_level_render(complementary_data.get("task"))
-            if rendered is None:
-                return transition
-            new_transition = transition.copy()
-            new_complementary_data = dict(new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {})
-            new_complementary_data.update(rendered)
-            new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
-            return new_transition
-
-        if _is_batched_language(persistent) or _is_batched_language(events):
-            return self._call_batch(transition, complementary_data, persistent, events)
+            return transition
 
         timestamp = complementary_data.get("timestamp")
         if timestamp is None:
@@ -90,171 +67,18 @@ class RenderMessagesStep(ProcessorStep):
             dataset_ctx=self.dataset_ctx,
         )
         if rendered is None:
-            # Language is present but this sparse frame has no applicable recipe
-            # branch. Keep it only when task-level action supervision is possible.
-            rendered = _fallback_low_level_render(complementary_data.get("task"))
-            if rendered is None:
-                return None
+            return None
 
         new_transition = transition.copy()
-        new_complementary_data = dict(new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {})
+        new_complementary_data = dict(complementary_data)
         new_complementary_data.pop(LANGUAGE_PERSISTENT, None)
         new_complementary_data.pop(LANGUAGE_EVENTS, None)
         new_complementary_data.update(rendered)
         new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
         return new_transition
 
-    def _call_batch(
-        self,
-        transition: EnvTransition,
-        complementary_data: dict[str, Any],
-        persistent_batch: list,
-        events_batch: list,
-    ) -> EnvTransition | None:
-        """Render a language batch.
-
-        Non-empty persistent and event batches must have the same size. Either
-        list may be empty when that language column is absent from the batch.
-        """
-        timestamp = complementary_data.get("timestamp")
-        if timestamp is None:
-            raise KeyError("RenderMessagesStep requires sample timestamp in complementary data.")
-
-        non_empty_batch_sizes = {len(batch) for batch in (persistent_batch, events_batch) if batch}
-        if len(non_empty_batch_sizes) > 1:
-            raise ValueError(
-                "Batched language columns must have equal lengths when both are non-empty, "
-                f"got persistent={len(persistent_batch)} and events={len(events_batch)}."
-            )
-        batch_size = next(iter(non_empty_batch_sizes), 0)
-        messages: list[list[dict[str, Any]]] = []
-        message_streams: list[list[str | None]] = []
-        target_message_indices: list[list[int]] = []
-        keep_indices: list[int] = []
-
-        for i in range(batch_size):
-            rendered = render_sample(
-                recipe=self.recipe,
-                persistent=persistent_batch[i] if i < len(persistent_batch) else [],
-                events=events_batch[i] if i < len(events_batch) else [],
-                t=_batch_value(timestamp, i),
-                sample_idx=int(_batch_value(complementary_data.get("index", 0), i)),
-                task=_batch_value(complementary_data.get("task"), i),
-                dataset_ctx=self.dataset_ctx,
-            )
-            if rendered is None:
-                rendered = _fallback_low_level_render(_batch_value(complementary_data.get("task"), i))
-                if rendered is None:
-                    continue
-            keep_indices.append(i)
-            messages.append(rendered["messages"])
-            message_streams.append(rendered["message_streams"])
-            target_message_indices.append(rendered["target_message_indices"])
-
-        if not messages:
-            return None
-
-        new_transition = (
-            _select_batch_indices(transition, keep_indices, batch_size)
-            if len(keep_indices) != batch_size
-            else transition.copy()
-        )
-        new_complementary_data = dict(new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {})
-        new_complementary_data.pop(LANGUAGE_PERSISTENT, None)
-        new_complementary_data.pop(LANGUAGE_EVENTS, None)
-        new_complementary_data["messages"] = messages
-        new_complementary_data["message_streams"] = message_streams
-        new_complementary_data["target_message_indices"] = target_message_indices
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
-        return new_transition
-
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
         """Pass features through unchanged; rendering only touches complementary data."""
         return features
-
-
-def _is_batched_language(value: Any) -> bool:
-    return isinstance(value, list) and bool(value) and isinstance(value[0], list)
-
-
-def _batch_value(value: Any, index: int) -> Any:
-    if value is None:
-        return None
-    if isinstance(value, list):
-        return value[index]
-    if hasattr(value, "ndim") and value.ndim > 0:
-        return unwrap_scalar(value[index])
-    return unwrap_scalar(value)
-
-
-def _select_batch_indices(transition: EnvTransition, indices: list[int], batch_size: int) -> EnvTransition:
-    selected = transition.copy()
-    for key in (TransitionKey.OBSERVATION, TransitionKey.COMPLEMENTARY_DATA):
-        data = selected.get(key)
-        if isinstance(data, dict):
-            selected[key] = {
-                name: _select_value(value, indices, batch_size, f"{key}.{name}")
-                for name, value in data.items()
-            }
-    action = selected.get(TransitionKey.ACTION)
-    if action is not None:
-        selected[TransitionKey.ACTION] = _select_value(action, indices, batch_size, str(TransitionKey.ACTION))
-    return selected
-
-
-def _select_value(value: Any, indices: list[int], batch_size: int, path: str) -> Any:
-    if isinstance(value, dict):
-        return {key: _select_value(item, indices, batch_size, f"{path}.{key}") for key, item in value.items()}
-    if isinstance(value, list):
-        if len(value) != batch_size:
-            raise ValueError(
-                f"Cannot filter batched field {path!r}: expected {batch_size} values, got {len(value)}."
-            )
-        return [value[i] for i in indices]
-    if isinstance(value, np.ndarray) and value.ndim > 0:
-        return value[indices]
-    if hasattr(value, "index_select") and hasattr(value, "new_tensor") and getattr(value, "ndim", 0) > 0:
-        return value.index_select(0, value.new_tensor(indices).long())
-    return value
-
-
-def _fallback_low_level_render(task: Any) -> dict[str, Any] | None:
-    """Keep action-only samples trainable when no recipe branch matches."""
-    if hasattr(task, "item"):
-        task = task.item()
-    if isinstance(task, list):
-        if not task:
-            return None
-        messages = []
-        message_streams = []
-        target_message_indices = []
-        missing_indices = []
-        for index, t in enumerate(task):
-            rendered = _fallback_low_level_render(t)
-            if rendered is None:
-                missing_indices.append(index)
-                continue
-            messages.append(rendered["messages"])
-            message_streams.append(rendered["message_streams"])
-            target_message_indices.append(rendered["target_message_indices"])
-        if missing_indices:
-            if len(missing_indices) == len(task):
-                return None
-            raise ValueError(
-                "Batched low-level fallback requires a non-empty task for every sample; "
-                f"missing task at indices {missing_indices}."
-            )
-        return {
-            "messages": messages,
-            "message_streams": message_streams,
-            "target_message_indices": target_message_indices,
-        }
-    if not isinstance(task, str) or not task:
-        return None
-    return {
-        "messages": [{"role": "user", "content": task}],
-        "message_streams": ["low_level"],
-        "target_message_indices": [],
-    }

src/lerobot/processor/tokenizer_processor.py

@@ -25,7 +25,6 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass, field
-from pathlib import Path
 from typing import TYPE_CHECKING, Any
 
 import torch
@@ -33,7 +32,6 @@ import torch
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
 from lerobot.lerobot_types import EnvTransition, RobotObservation, TransitionKey
 from lerobot.utils.constants import (
-    ACTION_CODE_TOKEN_MASK,
     ACTION_TOKEN_MASK,
     ACTION_TOKENS,
     OBS_LANGUAGE_ATTENTION_MASK,
@@ -43,7 +41,7 @@ from lerobot.utils.constants import (
 )
 from lerobot.utils.import_utils import _transformers_available
 
-from .pipeline import ActionProcessorStep, ObservationProcessorStep, ProcessorStepRegistry
+from .pipeline import ComplementaryDataProcessorStep, ObservationProcessorStep, ProcessorStepRegistry
 
 # Conditional import for type checking and lazy loading
 if TYPE_CHECKING or _transformers_available:
@@ -65,17 +63,15 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
     Requires the `transformers` library to be installed.
 
-    **Attributes**:
-        - **tokenizer_name** (`str | None`) -- The name of a pretrained tokenizer from the Hugging Face Hub
-          (e.g., "bert-base-uncased").
-        - **tokenizer** (`Any | None`) -- A pre-initialized tokenizer object. If provided, `tokenizer_name` is
-          ignored.
-        - **max_length** (`int`) -- The maximum length to pad or truncate sequences to.
-        - **task_key** (`str`) -- The key in `complementary_data` where the task string is stored.
-        - **padding_side** (`str`) -- The side to pad on ('left' or 'right').
-        - **padding** (`str`) -- The padding strategy ('max_length', 'longest', etc.).
-        - **truncation** (`bool`) -- Whether to truncate sequences longer than `max_length`.
-        - **input_tokenizer** (`Any`) -- The internal tokenizer instance, loaded during initialization.
+    Attributes:
+        tokenizer_name: The name of a pretrained tokenizer from the Hugging Face Hub (e.g., "bert-base-uncased").
+        tokenizer: A pre-initialized tokenizer object. If provided, `tokenizer_name` is ignored.
+        max_length: The maximum length to pad or truncate sequences to.
+        task_key: The key in `complementary_data` where the task string is stored.
+        padding_side: The side to pad on ('left' or 'right').
+        padding: The padding strategy ('max_length', 'longest', etc.).
+        truncation: Whether to truncate sequences longer than `max_length`.
+        input_tokenizer: The internal tokenizer instance, loaded during initialization.
     """
 
     tokenizer_name: str | None = None
@@ -140,7 +136,7 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         # Standardize to a list of strings for the tokenizer
         if isinstance(task, str):
             return [task]
-        elif isinstance(task, list | tuple) and all(isinstance(t, str) for t in task):
+        elif isinstance(task, (list, tuple)) and all(isinstance(t, str) for t in task):
             return list(task)
 
         return None
@@ -297,15 +293,6 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
         return config
 
-    def save_artifacts(self, save_directory: Path) -> dict[str, str]:
-        """Save the tokenizer so object-provided instances reload without overrides."""
-        artifact_path = Path("tokenizer")
-        save_pretrained = getattr(self.input_tokenizer, "save_pretrained", None)
-        if save_pretrained is None:
-            raise TypeError("Tokenizer must implement save_pretrained() to save a portable pipeline.")
-        save_pretrained(save_directory / artifact_path)
-        return {"tokenizer_name": artifact_path.as_posix()}
-
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
@@ -338,27 +325,22 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register(name="action_tokenizer_processor")
-class ActionTokenizerProcessorStep(ActionProcessorStep):
+class ActionTokenizerProcessorStep(ComplementaryDataProcessorStep):
     """
     Processor step to tokenize action data using a fast action tokenizer.
 
-    This step takes action tensors from an `EnvTransition`, tokenizes them using
+    This step reads the action tensor from the `EnvTransition`, tokenizes it using
     a Hugging Face `transformers` AutoProcessor (such as the Physical Intelligence "fast" tokenizer),
-    and returns the tokenized action.
+    and stores the resulting token IDs and mask in the transition's complementary data.
 
     Requires the `transformers` library to be installed.
 
-    **Attributes**:
-        - **tokenizer_name** -- The name of a pretrained processor from the Hugging Face Hub (e.g.,
-          "lerobot/fast-action-tokenizer").
-        - **tokenizer** -- A pre-initialized processor/tokenizer object. If provided, `tokenizer_name` is
-          ignored.
-        - **trust_remote_code** (`bool`) -- Whether to trust remote code when loading the tokenizer (required
-          for some tokenizers).
-        - **action_tokenizer** (`Any`) -- The internal tokenizer/processor instance, loaded during
-          initialization.
-        - **paligemma_tokenizer_name** (`str`) -- The name of a pretrained PaliGemma tokenizer from the
-          Hugging Face Hub (e.g., "google/paligemma-3b-pt-224").
+    Attributes:
+        action_tokenizer_name: The name of a pretrained processor from the Hugging Face Hub (e.g., "lerobot/fast-action-tokenizer").
+        action_tokenizer_input_object: A pre-initialized processor/tokenizer object. If provided, `action_tokenizer_name` is ignored.
+        trust_remote_code: Whether to trust remote code when loading the tokenizer (required for some tokenizers).
+        action_tokenizer: The internal tokenizer/processor instance, loaded during initialization.
+        paligemma_tokenizer_name: The name of a pretrained PaliGemma tokenizer from the Hugging Face Hub (e.g., "google/paligemma-3b-pt-224").
     """
 
     action_tokenizer_name: str | None = None
@@ -367,7 +349,6 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
     max_action_tokens: int = 256
     fast_skip_tokens: int = 128
     paligemma_tokenizer_name: str = "google/paligemma-3b-pt-224"
-    allow_truncation: bool = True
     # Internal tokenizer instance (not part of the config)
     action_tokenizer: Any = field(default=None, init=False, repr=False)
     _paligemma_tokenizer: Any = field(default=None, init=False, repr=False)
@@ -411,38 +392,27 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
             add_bos_token=False,
         )
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    def complementary_data(self, complementary_data: dict[str, Any]) -> dict[str, Any]:
         """
-        Applies action tokenization to the transition.
-
-        This overrides the base class to handle both tokens and mask.
+        Tokenizes the transition's action and adds the tokens and mask to the complementary data.
 
         Args:
-            transition: The input transition with action data.
+            complementary_data: The input complementary data dictionary.
 
         Returns:
-            The processed transition with tokenized actions and mask in complementary data.
+            The complementary data with tokenized actions and mask added.
         """
-        self._current_transition = transition.copy()
-        new_transition = self._current_transition
-
-        action = new_transition.get(TransitionKey.ACTION)
+        action = self.transition.get(TransitionKey.ACTION)
         if action is None:
             # During inference, no action is available, skip tokenization
-            return new_transition
+            return complementary_data
 
-        # Tokenize and get masks for the full formatted sequence and the discrete action codes.
-        tokens, mask, code_mask = self._tokenize_action(action)
+        # Tokenize and get both tokens and mask
+        tokens, mask = self._tokenize_action(action)
 
-        # Store mask in complementary data
-        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
-        if complementary_data is None:
-            complementary_data = {}
         complementary_data[ACTION_TOKEN_MASK] = mask
-        complementary_data[ACTION_CODE_TOKEN_MASK] = code_mask
         complementary_data[ACTION_TOKENS] = tokens
-        new_transition[TransitionKey.COMPLEMENTARY_DATA] = complementary_data
-        return new_transition
+        return complementary_data
 
     def _act_tokens_to_paligemma_tokens(self, tokens: torch.Tensor) -> torch.Tensor:
         """
@@ -450,7 +420,7 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
         """
         return self._paligemma_tokenizer.vocab_size - 1 - self.fast_skip_tokens - tokens
 
-    def _tokenize_action(self, action: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    def _tokenize_action(self, action: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Tokenizes the action tensor and creates a mask.
 
@@ -479,7 +449,6 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
         # The fast tokenizer expects action data and returns token IDs
         tokens_list = []
         masks_list = []
-        code_masks_list = []
 
         for i in range(batch_size):
             # Tokenize single action (move to CPU first as tokenizer uses scipy which requires numpy)
@@ -497,83 +466,58 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
             if tokens.dim() > 1:
                 tokens = tokens.flatten()
 
-            action_code_tokens = self._act_tokens_to_paligemma_tokens(tokens)
             bos_id = self._paligemma_tokenizer.bos_token_id
-            prompt_tokens = torch.tensor(
-                self._paligemma_tokenizer.encode("Action: ", add_special_tokens=False),
-                device=action.device,
-            )
-            end_tokens = torch.tensor(self._paligemma_tokenizer.encode("|"), device=action.device)
-
-            code_start = 1 + len(prompt_tokens)
-            code_end = code_start + len(action_code_tokens)
+            # add bos
             tokens = torch.cat(
                 [
                     torch.tensor([bos_id], device=action.device),
-                    prompt_tokens,
-                    action_code_tokens,
-                    end_tokens,
+                    torch.tensor(
+                        self._paligemma_tokenizer.encode("Action: ", add_special_tokens=False),
+                        device=action.device,
+                    ),
+                    self._act_tokens_to_paligemma_tokens(tokens),
+                    torch.tensor(self._paligemma_tokenizer.encode("|"), device=action.device),
                 ]
             )
-            code_mask = torch.zeros(len(tokens), dtype=torch.bool, device=action.device)
-            code_mask[code_start:code_end] = True
 
             # Truncate or pad to max_action_tokens
             if len(tokens) > self.max_action_tokens:
-                if not self.allow_truncation:
-                    raise ValueError(
-                        f"FAST action sequence has {len(tokens)} tokens, exceeding "
-                        f"max_action_tokens={self.max_action_tokens}."
-                    )
                 logging.warning(
                     f"Token length ({len(tokens)}) exceeds max length ({self.max_action_tokens}), truncating. "
                     "Consider increasing the `max_action_tokens` in your model config if this happens frequently."
                 )
                 tokens = tokens[: self.max_action_tokens]
-                code_mask = code_mask[: self.max_action_tokens]
                 mask = torch.ones(self.max_action_tokens, dtype=torch.bool, device=action.device)
             else:
-                pad_len = self.max_action_tokens - len(tokens)
                 mask = torch.cat(
                     [
                         torch.ones(len(tokens), dtype=torch.bool, device=action.device),
-                        torch.zeros(pad_len, dtype=torch.bool, device=action.device),
+                        torch.zeros(
+                            self.max_action_tokens - len(tokens), dtype=torch.bool, device=action.device
+                        ),
                     ]
                 )
-                code_mask = torch.nn.functional.pad(code_mask, (0, pad_len), value=False)
                 # Pad tokens with zeros
-                tokens = torch.nn.functional.pad(tokens, (0, pad_len), value=0)
+                tokens = torch.nn.functional.pad(tokens, (0, self.max_action_tokens - len(tokens)), value=0)
 
             tokens_list.append(tokens)
             masks_list.append(mask)
-            code_masks_list.append(code_mask)
 
         # Stack into batched tensors
         tokens_batch = torch.stack(tokens_list, dim=0)  # (B, max_action_tokens)
         masks_batch = torch.stack(masks_list, dim=0)  # (B, max_action_tokens)
-        code_masks_batch = torch.stack(code_masks_list, dim=0)  # (B, max_action_tokens)
 
         # Remove batch dimension if input was single sample
         if single_sample:
             tokens_batch = tokens_batch.squeeze(0)
             masks_batch = masks_batch.squeeze(0)
-            code_masks_batch = code_masks_batch.squeeze(0)
 
         # Move to the same device as the input
         if device is not None:
             tokens_batch = tokens_batch.to(device)
             masks_batch = masks_batch.to(device)
-            code_masks_batch = code_masks_batch.to(device)
 
-        return tokens_batch, masks_batch, code_masks_batch
-
-    def action(self, action: torch.Tensor) -> torch.Tensor:
-        """
-        This method is not used since we override __call__.
-        Required by ActionProcessorStep ABC.
-        """
-        tokens, _, _ = self._tokenize_action(action)
-        return tokens
+        return tokens_batch, masks_batch
 
     def get_config(self) -> dict[str, Any]:
         """
@@ -590,7 +534,6 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
             "max_action_tokens": self.max_action_tokens,
             "fast_skip_tokens": self.fast_skip_tokens,
             "paligemma_tokenizer_name": self.paligemma_tokenizer_name,
-            "allow_truncation": self.allow_truncation,
         }
 
         # Only save tokenizer_name if it was used to create the tokenizer
@@ -599,27 +542,19 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
 
         return config
 
-    def save_artifacts(self, save_directory: Path) -> dict[str, str]:
-        artifact_path = Path("action_tokenizer")
-        save_pretrained = getattr(self.action_tokenizer, "save_pretrained", None)
-        if save_pretrained is None:
-            raise TypeError("Action tokenizer must implement save_pretrained() to save a portable pipeline.")
-        save_pretrained(save_directory / artifact_path)
-        return {"action_tokenizer_name": artifact_path.as_posix()}
-
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
         """
-        Updates feature definitions to reflect tokenized actions.
+        Returns the policy features unchanged.
 
-        This updates the policy features dictionary to indicate that the action
-        has been tokenized into a sequence of token IDs with shape (max_action_tokens,).
+        The tokenized actions and mask are stored in complementary data, which is not
+        tracked in the policy features dictionary.
 
         Args:
             features: The dictionary of existing policy features.
 
         Returns:
-            The updated dictionary of policy features.
+            The dictionary of policy features, unchanged.
         """
         return features

src/lerobot/rewards/pretrained.py

@@ -16,11 +16,12 @@ import abc
 import builtins
 import logging
 import os
-import warnings
+from importlib.resources import files
 from pathlib import Path
+from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any, TypeVar
 
-from huggingface_hub import hf_hub_download
+from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
@@ -60,22 +61,6 @@ class PreTrainedRewardModel(nn.Module, HubMixin, abc.ABC):
             raise TypeError(f"Class {cls.__name__} must define 'name'")
 
     def _save_pretrained(self, save_directory: Path) -> None:
-        """Serialize this reward model's parameters (and config) into `save_directory`.
-
-        Safe to call on every rank: replicas carry identical weights, so only the main process
-        writes (sharded reward models are rejected at config validation — no collective gather).
-
-        Args:
-            save_directory (Path): Target directory for the reward model config (`config.json`)
-                and `model.safetensors`.
-        """
-        from lerobot.distributed.utils import is_main_process
-
-        # save_checkpoint calls this on every rank; replicas carry identical
-        # weights, so the main process is the only writer. Sharded reward models are rejected
-        # at config validation, so no collective gather is needed here.
-        if not is_main_process():
-            return
         self.config._save_pretrained(save_directory)
         model_to_save = self.module if hasattr(self, "module") else self
         save_model_as_safetensor(model_to_save, str(save_directory / SAFETENSORS_SINGLE_FILE))
@@ -190,22 +175,53 @@ class PreTrainedRewardModel(nn.Module, HubMixin, abc.ABC):
         """
         return type(self).forward is not PreTrainedRewardModel.forward
 
-    def push_model_to_hub(self, cfg: "TrainPipelineConfig") -> None:
-        """Publish this reward model to the Hub.
+    def push_model_to_hub(self, cfg: "TrainPipelineConfig"):
+        api = HfApi()
+        repo_id = api.create_repo(
+            repo_id=self.config.repo_id, private=self.config.private, exist_ok=True
+        ).repo_id
 
-        Deprecated: use :func:`lerobot.common.train_utils.publish_trained_model` instead.
+        # Push the files to the repo in a single commit
+        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
+            saved_path = Path(tmp) / repo_id
 
-        Args:
-            cfg (TrainPipelineConfig): The training config; saved as `train_config.json` and
-                used to render the model card.
-        """
-        from lerobot.common.train_utils import publish_trained_model
+            self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
 
-        warnings.warn(
-            "PreTrainedRewardModel.push_model_to_hub is deprecated and will be removed in a "
-            "future version. Use lerobot.common.train_utils.publish_trained_model(cfg, model, "
-            "preprocessor, postprocessor, dataset_meta) instead.",
-            FutureWarning,
-            stacklevel=2,
+            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)  # Calls _save_pretrained and stores train config
+
+            commit_info = api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message="Upload reward model weights, train config and readme",
+                allow_patterns=["*.safetensors", "*.json", "*.yaml", "*.md"],
+                ignore_patterns=["*.tmp", "*.log"],
+            )
+
+            logging.info(f"Model pushed to {commit_info.repo_url.url}")
+
+    def generate_model_card(
+        self, dataset_repo_id: str, model_type: str, license: str | None, tags: list[str] | None
+    ) -> ModelCard:
+        card_data = ModelCardData(
+            license=license or "apache-2.0",
+            library_name="lerobot",
+            pipeline_tag="robotics",
+            tags=list(set(tags or []).union({"robotics", "lerobot", "reward-model", model_type})),
+            model_name=model_type,
+            datasets=dataset_repo_id,
         )
-        publish_trained_model(cfg, self, None, None, None)
+
+        template_card = (
+            files("lerobot.templates")
+            .joinpath("lerobot_rewardmodel_modelcard_template.md")
+            .read_text(encoding="utf-8")
+        )
+        card = ModelCard.from_template(card_data, template_str=template_card)
+        card.validate()
+        return card

src/lerobot/rewards/robometer/processor_robometer.py

@@ -25,13 +25,13 @@ from PIL import Image
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
+    ObservationProcessorStep,
     PolicyAction,
     PolicyProcessorPipeline,
-    ProcessorStep,
     ProcessorStepRegistry,
     policy_action_to_transition,
 )
@@ -105,7 +105,7 @@ def _expand_tasks(task: Any, *, batch_size: int, default: str | None) -> list[st
 
 @dataclass
 @ProcessorStepRegistry.register(name="robometer_encoder")
-class RobometerEncoderProcessorStep(ProcessorStep):
+class RobometerEncoderProcessorStep(ObservationProcessorStep):
     """Encode raw frames + task into Qwen-VL tensors for the Robometer model.
 
     Loads a :class:`~transformers.AutoProcessor` matching ``base_model_id`` and
@@ -160,11 +160,8 @@ class RobometerEncoderProcessorStep(ProcessorStep):
             if token not in tokenizer.get_vocab():
                 tokenizer.add_special_tokens({"additional_special_tokens": [token]})
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = transition.get(TransitionKey.OBSERVATION)
-        complementary = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
-        if not isinstance(observation, dict):
-            raise ValueError("RobometerEncoderProcessorStep requires an observation dict")
+    def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
+        complementary = self.transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
 
         if self.image_key not in observation:
             raise KeyError(f"Robometer expected image key {self.image_key!r} in observation")
@@ -190,13 +187,9 @@ class RobometerEncoderProcessorStep(ProcessorStep):
         ]
         encoded = self.encode_samples(samples)
 
-        new_observation = dict(observation)
         for key, value in encoded.items():
-            new_observation[f"{ROBOMETER_FEATURE_PREFIX}{key}"] = value
-
-        new_transition = transition.copy()
-        new_transition[TransitionKey.OBSERVATION] = new_observation
-        return new_transition
+            observation[f"{ROBOMETER_FEATURE_PREFIX}{key}"] = value
+        return observation
 
     def encode_samples(self, samples: list[tuple[np.ndarray, str]]) -> dict[str, Tensor]:
         """Run the Qwen-VL processor on a list of ``(frames, task)`` samples."""

src/lerobot/rewards/sarm/processor_sarm.py

@@ -48,13 +48,13 @@ else:
     Faker = None  # type: ignore[assignment, misc]
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, PolicyAction, TransitionKey
+from lerobot.lerobot_types import PolicyAction, TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
+    ObservationProcessorStep,
     PolicyProcessorPipeline,
-    ProcessorStep,
     RenameObservationsProcessorStep,
     from_tensor_to_numpy,
     policy_action_to_transition,
@@ -73,7 +73,7 @@ from .sarm_utils import (
 logger = logging.getLogger(__name__)
 
 
-class SARMEncodingProcessorStep(ProcessorStep):
+class SARMEncodingProcessorStep(ObservationProcessorStep):
     """ProcessorStep that encodes images and text with CLIP and generates stage and progress labels for SARM."""
 
     def __init__(
@@ -257,9 +257,9 @@ class SARMEncodingProcessorStep(ProcessorStep):
 
         return annotations
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
+    def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
         """
-        Encode images, text, and normalize states in the transition.
+        Encode images, text, and normalize states in the observation.
 
         Implements SARM training data preparation:
         - Applies language perturbation (20% probability)
@@ -267,9 +267,7 @@ class SARMEncodingProcessorStep(ProcessorStep):
         - Generates stage+tau targets for all frames
         - Outputs lengths tensor for valid sequence masking
         """
-        new_transition = transition.copy() if hasattr(transition, "copy") else dict(transition)
-        observation = new_transition.get(TransitionKey.OBSERVATION)
-        comp_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+        comp_data = self.transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
 
         frame_index = comp_data.get("index")
         episode_index = comp_data.get("episode_index")
@@ -392,8 +390,7 @@ class SARMEncodingProcessorStep(ProcessorStep):
                     )
                 observation["dense_targets"] = dense_targets
 
-        new_transition[TransitionKey.OBSERVATION] = observation
-        return new_transition
+        return observation
 
     def _compute_batch_targets(
         self,

src/lerobot/rewards/topreward/modeling_topreward.py

@@ -58,11 +58,12 @@ import builtins
 import logging
 import os
 from pathlib import Path
+from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any, TypeVar
 
 import numpy as np
 import torch
-from huggingface_hub import hf_hub_download
+from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError
 from torch import Tensor
@@ -74,6 +75,9 @@ from lerobot.rewards.topreward.configuration_topreward import TOPRewardConfig
 from lerobot.rewards.topreward.processor_topreward import TOPREWARD_FEATURE_PREFIX, TOPREWARD_INPUT_KEYS
 from lerobot.utils.import_utils import _transformers_available, require_package
 
+if TYPE_CHECKING:
+    from lerobot.configs.train import TrainPipelineConfig
+
 if TYPE_CHECKING or _transformers_available:
     from transformers import Qwen3VLForConditionalGeneration
 else:
@@ -201,3 +205,34 @@ class TOPRewardModel(PreTrainedRewardModel):
         instance.to(config.device)
         instance.eval()
         return instance
+
+    def push_model_to_hub(self, cfg: TrainPipelineConfig):
+        """Push the TOPReward ``config.json`` + model card to the Hub."""
+        api = HfApi()
+        repo_id = api.create_repo(
+            repo_id=self.config.repo_id, private=self.config.private, exist_ok=True
+        ).repo_id
+
+        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
+            saved_path = Path(tmp) / repo_id
+            saved_path.mkdir(parents=True, exist_ok=True)
+
+            self.config._save_pretrained(saved_path)
+
+            card = self.generate_model_card(
+                cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags
+            )
+            card.save(str(saved_path / "README.md"))
+
+            cfg.save_pretrained(saved_path)
+
+            commit_info = api.upload_folder(
+                repo_id=repo_id,
+                repo_type="model",
+                folder_path=saved_path,
+                commit_message="Upload TOPReward config and readme",
+                allow_patterns=["*.json", "*.yaml", "*.md"],
+                ignore_patterns=["*.tmp", "*.log", "*.safetensors"],
+            )
+
+            logger.info(f"Model pushed to {commit_info.repo_url.url}")

src/lerobot/rewards/topreward/processor_topreward.py

@@ -23,13 +23,13 @@ import torch
 from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
-from lerobot.lerobot_types import EnvTransition, TransitionKey
+from lerobot.lerobot_types import TransitionKey
 from lerobot.processor import (
     AddBatchDimensionProcessorStep,
     DeviceProcessorStep,
+    ObservationProcessorStep,
     PolicyAction,
     PolicyProcessorPipeline,
-    ProcessorStep,
     ProcessorStepRegistry,
     policy_action_to_transition,
 )
@@ -107,7 +107,7 @@ def _expand_tasks(task: Any, *, batch_size: int, default: str | None) -> list[st
 
 @dataclass
 @ProcessorStepRegistry.register(name="topreward_encoder")
-class TOPRewardEncoderProcessorStep(ProcessorStep):
+class TOPRewardEncoderProcessorStep(ObservationProcessorStep):
     """Encode raw frames + task into Qwen-VL tensors for the TOPReward model.
 
     Loads a :class:`~transformers.AutoProcessor` matching ``vlm_name`` and
@@ -142,9 +142,8 @@ class TOPRewardEncoderProcessorStep(ProcessorStep):
         require_package("transformers", extra="topreward")
         self._processor = AutoProcessor.from_pretrained(self.vlm_name, trust_remote_code=True)
 
-    def __call__(self, transition: EnvTransition) -> EnvTransition:
-        observation = transition.get(TransitionKey.OBSERVATION)
-        complementary = transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
+    def observation(self, observation: dict[str, Any]) -> dict[str, Any]:
+        complementary = self.transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
         if self.image_key not in observation:
             raise KeyError(f"TOPReward expected image key {self.image_key!r} in observation")
 
@@ -161,13 +160,9 @@ class TOPRewardEncoderProcessorStep(ProcessorStep):
 
         encoded = self._encode_batch(videos, tasks, batch_size)
 
-        new_observation = dict(observation)
         for key, value in encoded.items():
-            new_observation[f"{TOPREWARD_FEATURE_PREFIX}{key}"] = value
-
-        new_transition = transition.copy()
-        new_transition[TransitionKey.OBSERVATION] = new_observation
-        return new_transition
+            observation[f"{TOPREWARD_FEATURE_PREFIX}{key}"] = value
+        return observation
 
     def _encode_batch(self, videos: Tensor, tasks: list[str], batch_size) -> dict[str, Any]:
         """Tokenise a batch of (frames, task) pairs into Qwen-VL tensors.

src/lerobot/rl/actor.py

@@ -13,7 +13,8 @@
 # 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.
-"""Actor server runner for distributed HILSerl robot policy training.
+"""
+Actor server runner for distributed HILSerl robot policy training.
 
 This script implements the actor component of the distributed HILSerl architecture.
 It executes the policy in the robot environment, collects experience,
@@ -118,16 +119,6 @@ from .train_rl import TrainRLServerPipelineConfig
 
 @parser.wrap()
 def actor_cli(cfg: TrainRLServerPipelineConfig):
-    """CLI entry point for the HILSerl actor server.
-
-    Connects to the learner server over gRPC, then launches (as threads or processes, depending on
-    `cfg.policy.concurrency.multiprocessing_context`) the background workers that receive updated
-    policy parameters and stream transitions/interactions back to the learner, while running the
-    policy-environment interaction loop (`act_with_policy`) on the main thread/process.
-
-    Args:
-        cfg (`TrainRLServerPipelineConfig`): Parsed from the CLI.
-    """
     # Fail fast with a friendly error if the optional ``hilserl`` extra is missing.
     require_package("grpcio", extra="hilserl", import_name="grpc")
     cfg.validate()
@@ -243,19 +234,18 @@ def act_with_policy(
     transitions_queue: Queue,
     interactions_queue: Queue,
 ):
-    """Executes policy interaction within the environment.
+    """
+    Executes policy interaction within the environment.
 
     This function rolls out the policy in the environment, collecting interaction data and pushing it to a queue for streaming to the learner.
     Once an episode is completed, updated network parameters received from the learner are retrieved from a queue and loaded into the network.
 
     Args:
-        cfg (`TrainRLServerPipelineConfig`): Training configuration.
-        shutdown_event (`Event`): Set to stop the policy loop.
-        parameters_queue (`Queue`): Queue of serialized learner weights, drained via
-            `update_policy_parameters`.
-        transitions_queue (`Queue`): Queue transitions are pushed to for streaming to the learner.
-        interactions_queue (`Queue`): Queue interaction messages are pushed to for streaming to the
-            learner.
+        cfg: Configuration settings for the interaction process.
+        shutdown_event: Event to check if the process should shutdown.
+        parameters_queue: Queue to receive updated network parameters from the learner.
+        transitions_queue: Queue to send transitions to the learner.
+        interactions_queue: Queue to send interactions to the learner.
     """
     # Initialize logging for multiprocessing
     if not use_threads(cfg):
@@ -450,8 +440,7 @@ def establish_learner_connection(
     Args:
         stub (services_pb2_grpc.LearnerServiceStub): The stub to use for the connection.
         shutdown_event (Event): The event to check if the connection should be established.
-        attempts (int, *optional*, defaults to 30): The number of attempts to establish the connection.
-
+        attempts (int): The number of attempts to establish the connection.
     Returns:
         bool: True if the connection is established, False otherwise.
     """
@@ -484,6 +473,7 @@ def learner_service_client(
     Returns:
         tuple[services_pb2_grpc.LearnerServiceStub, grpc.Channel]: The stub and the channel.
     """
+
     channel = grpc.insecure_channel(
         f"{host}:{port}",
         grpc_channel_options(),
@@ -506,8 +496,8 @@ def receive_policy(
         cfg (TrainRLServerPipelineConfig): The configuration for the actor.
         parameters_queue (Queue): The queue to receive the parameters.
         shutdown_event (Event): The event to check if the process should shutdown.
-        learner_client (services_pb2_grpc.LearnerServiceStub | None, *optional*): Optional pre-created stub.
-        grpc_channel (grpc.Channel | None, *optional*): Optional pre-created channel.
+        learner_client (services_pb2_grpc.LearnerServiceStub | None): Optional pre-created stub.
+        grpc_channel (grpc.Channel | None): Optional pre-created channel.
     """
     logging.info("[ACTOR] Start receiving parameters from the Learner")
     if not use_threads(cfg):
@@ -567,9 +557,10 @@ def send_transitions(
         cfg (TrainRLServerPipelineConfig): The configuration for the actor.
         transitions_queue (Queue): The queue to receive the transitions.
         shutdown_event (Event): The event to check if the process should shutdown.
-        learner_client (services_pb2_grpc.LearnerServiceStub | None, *optional*): Optional pre-created stub.
-        grpc_channel (grpc.Channel | None, *optional*): Optional pre-created channel.
+        learner_client (services_pb2_grpc.LearnerServiceStub | None): Optional pre-created stub.
+        grpc_channel (grpc.Channel | None): Optional pre-created channel.
     """
+
     if not use_threads(cfg):
         # Create a process-specific log file
         log_dir = os.path.join(cfg.output_dir, "logs")
@@ -621,9 +612,10 @@ def send_interactions(
         cfg (TrainRLServerPipelineConfig): The configuration for the actor.
         interactions_queue (Queue): The queue to receive the interactions.
         shutdown_event (Event): The event to check if the process should shutdown.
-        learner_client (services_pb2_grpc.LearnerServiceStub | None, *optional*): Optional pre-created stub.
-        grpc_channel (grpc.Channel | None, *optional*): Optional pre-created channel.
+        learner_client (services_pb2_grpc.LearnerServiceStub | None): Optional pre-created stub.
+        grpc_channel (grpc.Channel | None): Optional pre-created channel.
     """
+
     if not use_threads(cfg):
         # Create a process-specific log file
         log_dir = os.path.join(cfg.output_dir, "logs")
@@ -665,17 +657,6 @@ def transitions_stream(
     transitions_queue: Queue,
     timeout: float,
 ) -> "Generator[Any, None, services_pb2.Empty]":
-    """GRPC client-streaming generator that forwards queued transitions to the learner.
-
-    Args:
-        shutdown_event (`Event`): Set to stop streaming and return.
-        transitions_queue (`Queue`): Queue of serialized transition batches, filled by
-            `push_transitions_to_transport_queue`.
-        timeout (`float`): Seconds to wait for a queue item before checking `shutdown_event` again.
-
-    Yields:
-        Chunks of a `services_pb2.Transition` message, produced by `send_bytes_in_chunks`.
-    """
     while not shutdown_event.is_set():
         try:
             message = transitions_queue.get(block=True, timeout=timeout)
@@ -695,16 +676,6 @@ def interactions_stream(
     interactions_queue: Queue,
     timeout: float,
 ) -> "Generator[Any, None, services_pb2.Empty]":
-    """GRPC client-streaming generator that forwards queued interaction messages to the learner.
-
-    Args:
-        shutdown_event (`Event`): Set to stop streaming and return.
-        interactions_queue (`Queue`): Queue of serialized interaction messages.
-        timeout (`float`): Seconds to wait for a queue item before checking `shutdown_event` again.
-
-    Yields:
-        Chunks of a `services_pb2.InteractionMessage`, produced by `send_bytes_in_chunks`.
-    """
     while not shutdown_event.is_set():
         try:
             message = interactions_queue.get(block=True, timeout=timeout)
@@ -747,11 +718,12 @@ def update_policy_parameters(algorithm: RLAlgorithm, parameters_queue: Queue, de
 
 
 def push_transitions_to_transport_queue(transitions: list, transitions_queue):
-    """Move `transitions` to CPU, check for NaNs, and enqueue them for the learner.
+    """Send transitions to learner in smaller chunks to avoid network issues.
 
     Args:
-        transitions (`list`): Transitions to send, as produced by the actor's rollout loop.
-        transitions_queue (`Queue`): Queue drained by `transitions_stream`.
+        transitions: List of transitions to send
+        message_queue: Queue to send messages to learner
+        chunk_size: Size of each chunk to send
     """
     transition_to_send_to_learner = []
     for transition in transitions:
@@ -788,13 +760,6 @@ def get_frequency_stats(timer: TimerManager) -> dict[str, float]:
 
 
 def log_policy_frequency_issue(policy_fps: float, cfg: TrainRLServerPipelineConfig, interaction_step: int):
-    """Log a warning if `policy_fps` is below the environment's target `cfg.env.fps`.
-
-    Args:
-        policy_fps (`float`): Measured policy loop frequency.
-        cfg (`TrainRLServerPipelineConfig`): Provides the target `cfg.env.fps` to compare against.
-        interaction_step (`int`): Current interaction step, included in the warning message.
-    """
     if policy_fps < cfg.env.fps:
         logging.warning(
             f"[ACTOR] Policy FPS {policy_fps:.1f} below required {cfg.env.fps} at step {interaction_step}"
@@ -802,7 +767,6 @@ def log_policy_frequency_issue(policy_fps: float, cfg: TrainRLServerPipelineConf
 
 
 def use_threads(cfg: TrainRLServerPipelineConfig) -> bool:
-    """Whether the actor's background workers should run as threads instead of processes."""
     return cfg.policy.concurrency.actor == "threads"
 
 

src/lerobot/rl/algorithms/base.py

@@ -101,7 +101,6 @@ class RLAlgorithm(HubMixin, abc.ABC):
 
     @optimization_step.setter
     def optimization_step(self, value: int) -> None:
-        """Set the current learner optimization step."""
         self._optimization_step = int(value)
 
     def get_weights(self) -> dict[str, Any]:

src/lerobot/rl/algorithms/configs.py

@@ -45,6 +45,7 @@ class TrainingStats:
 
     def to_log_dict(self) -> dict[str, float]:
         """Flatten all stats into a single dict for logging."""
+
         d: dict[str, float] = {}
         for name, val in self.losses.items():
             d[name] = val
@@ -97,35 +98,6 @@ class RLAlgorithmConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
         revision: str | None = None,
         **algo_kwargs: Any,
     ) -> T:
-        """Load an algorithm config from a local directory or the Hugging Face Hub.
-
-        Args:
-            pretrained_name_or_path (`str | Path`):
-                Local directory containing `config.json`, or a Hub repo id.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether to force re-download the config even if it's cached.
-            resume_download (`bool | None`, *optional*):
-                Whether to resume an interrupted download.
-            proxies (`dict[Any, Any] | None`, *optional*):
-                Proxies to use for the download request.
-            token (`str | bool | None`, *optional*):
-                Hugging Face Hub authentication token.
-            cache_dir (`str | Path | None`, *optional*):
-                Directory to cache the downloaded config in.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only look for files locally, without querying the Hub.
-            revision (`str | None`, *optional*):
-                Hub revision (branch, tag, or commit hash) to load from.
-            **algo_kwargs: Attribute overrides applied to the loaded config instance.
-
-        Returns:
-            RLAlgorithmConfig: The loaded config, as the concrete registered subclass.
-
-        Raises:
-            FileNotFoundError: If no `config.json` is found locally or on the Hub.
-            TypeError: If loaded via a specific subclass but the config's registered type doesn't
-                match it.
-        """
         model_id = str(pretrained_name_or_path)
         config_file: str | None = None
         if Path(model_id).is_dir():

src/lerobot/rl/algorithms/factory.py

@@ -24,8 +24,8 @@ def make_algorithm_config(algorithm_type: str, **kwargs) -> RLAlgorithmConfig:
     """Instantiate an `RLAlgorithmConfig` from its registered type name.
 
     Args:
-        algorithm_type (`str`): Registry key of the algorithm (e.g. `"sac"`).
-        kwargs (`Any`, *optional*): Keyword arguments forwarded to the config class constructor.
+        algorithm_type: Registry key of the algorithm (e.g. ``"sac"``).
+        **kwargs: Keyword arguments forwarded to the config class constructor.
 
     Returns:
         An instance of the matching ``RLAlgorithmConfig`` subclass.
@@ -44,13 +44,14 @@ def make_algorithm_config(algorithm_type: str, **kwargs) -> RLAlgorithmConfig:
 
 
 def get_algorithm_class(name: str) -> type[RLAlgorithm]:
-    """Retrieves an RL algorithm class by its registered name.
+    """
+    Retrieves an RL algorithm class by its registered name.
 
     This function uses dynamic imports to avoid loading all algorithm classes into
     memory at once, improving startup time and reducing dependencies.
 
     Args:
-        name (`str`): The name of the algorithm. Supported names are "sac".
+        name: The name of the algorithm. Supported names are "sac".
 
     Returns:
         The algorithm class corresponding to the given name.
@@ -69,7 +70,8 @@ def get_algorithm_class(name: str) -> type[RLAlgorithm]:
 
 
 def make_algorithm(cfg: RLAlgorithmConfig, policy: torch.nn.Module) -> RLAlgorithm:
-    """Instantiate an RL algorithm.
+    """
+    Instantiate an RL algorithm.
 
     This factory function looks up the :class:`RLAlgorithm` subclass that matches
     ``cfg.type`` and instantiates it with the provided policy. It also enforces
@@ -77,8 +79,8 @@ def make_algorithm(cfg: RLAlgorithmConfig, policy: torch.nn.Module) -> RLAlgorit
     normally handled by :meth:`TrainRLServerPipelineConfig.validate`).
 
     Args:
-        cfg (`RLAlgorithmConfig`): The algorithm configuration. Must have `policy_config` set.
-        policy (`torch.nn.Module`): The policy module the algorithm will train.
+        cfg: The algorithm configuration. Must have ``policy_config`` set.
+        policy: The policy module the algorithm will train.
 
     Returns:
         An instantiated :class:`RLAlgorithm`.

src/lerobot/rl/algorithms/sac/configuration_sac.py

@@ -39,73 +39,52 @@ class SACAlgorithmConfig(RLAlgorithmConfig):
     update loop. The policy-side (actor + observation encoder) lives in
     :class:`~lerobot.policies.gaussian_actor.GaussianActorConfig` and is
     referenced via :attr:`policy_config`.
-
-    Args:
-        actor_lr (`float`, *optional*, defaults to 0.0003):
-            Learning rate for the actor network.
-        critic_lr (`float`, *optional*, defaults to 0.0003):
-            Learning rate for the critic network.
-        temperature_lr (`float`, *optional*, defaults to 0.0003):
-            Learning rate for the temperature parameter.
-        discount (`float`, *optional*, defaults to 0.99):
-            Discount factor for the Bellman update.
-        use_backup_entropy (`bool`, *optional*, defaults to `True`):
-            Whether to use backup entropy in the Bellman target.
-        critic_target_update_weight (`float`, *optional*, defaults to 0.005):
-            Polyak-averaging weight for the critic target update.
-        num_critics (`int`, *optional*, defaults to 2):
-            Number of critics in the ensemble.
-        num_subsample_critics (`int | None`, *optional*):
-            Number of critics to subsample from the ensemble for each Bellman target computation.
-            `None` uses the full ensemble.
-        critic_network_kwargs (`CriticNetworkConfig`, *optional*):
-            Configuration for the (continuous-action) critic network architecture.
-        discrete_critic_network_kwargs (`CriticNetworkConfig`, *optional*):
-            Configuration for the discrete-action critic network architecture.
-        temperature_init (`float`, *optional*, defaults to 1.0):
-            Initial value of the entropy temperature.
-        target_entropy (`float | None`, *optional*):
-            Target entropy for automatic temperature tuning. If `None`, defaults to `-|A|/2` where
-            `|A|` is the total action dimension (continuous + 1 if there is a discrete action head).
-        utd_ratio (`int`, *optional*, defaults to 1):
-            Update-to-data ratio. Set to `>1` to enable extra critic updates per env step.
-        policy_update_freq (`int`, *optional*, defaults to 1):
-            Frequency of policy updates, in units of critic updates.
-        grad_clip_norm (`float`, *optional*, defaults to 40.0):
-            Gradient-clipping norm applied during optimization.
-        use_torch_compile (`bool`, *optional*, defaults to `False`):
-            Whether to `torch.compile` the algorithm's forward passes. Currently disabled by default.
-        policy_config (`PreTrainedConfig | None`, *optional*):
-            The policy (actor) config this algorithm trains. Populated via `from_policy_config` or by
-            `TrainRLServerPipelineConfig.validate` before the algorithm is constructed.
     """
 
     # Optimizer learning rates
+    # Learning rate for the actor network
     actor_lr: float = 3e-4
+    # Learning rate for the critic network
     critic_lr: float = 3e-4
+    # Learning rate for the temperature parameter
     temperature_lr: float = 3e-4
 
     # Bellman update
+    # Discount factor for the SAC algorithm
     discount: float = 0.99
+    # Whether to use backup entropy for the SAC algorithm
     use_backup_entropy: bool = True
+    # Weight for the critic target update
     critic_target_update_weight: float = 0.005
 
     # Critic ensemble
+    # Number of critics in the ensemble
     num_critics: int = 2
+    # Number of subsampled critics for training
     num_subsample_critics: int | None = None
+    # Configuration for the critic network architecture
     critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+    # Configuration for the discrete critic network
     discrete_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
 
     # Temperature / entropy
+    # Initial temperature value
     temperature_init: float = 1.0
+    # Target entropy for automatic temperature tuning. If ``None``, defaults to
+    # ``-|A|/2`` where ``|A|`` is the total action dimension (continuous + 1 if
+    # there is a discrete action head).
     target_entropy: float | None = None
 
     # Update loop
+    # Update-to-data ratio. Set to >1 to enable extra critic updates per env step.
     utd_ratio: int = 1
+    # Frequency of policy updates
     policy_update_freq: int = 1
+    # Gradient clipping norm for the SAC algorithm
     grad_clip_norm: float = 40.0
 
     # Optimizations
+    # torch.compile is currently disabled by default
     use_torch_compile: bool = False
 
     # Policy config