chore(deps): bump the actions group across 1 directory with 6 updates

Bumps the actions group with 6 updates in the / directory: | Package | From | To | | --- | --- | --- | | [docker/login-action](https://github.com/docker/login-action) | `4.4.0` | `4.6.0` | | [anthropics/claude-code-action](https://github.com/anthropics/claude-code-action) | `1.0.179` | `1.0.183` | | [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) | `8.3.2` | `9.0.0` | | [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) | `1.14.1` | `1.14.2` | | [trufflesecurity/trufflehog](https://github.com/trufflesecurity/trufflehog) | `3.95.9` | `3.96.0` | | [actions/stale](https://github.com/actions/stale) | `10` | `11` | Updates `docker/login-action` from 4.4.0 to 4.6.0 - [Release notes](https://github.com/docker/login-action/releases) - [Commits](https://github.com/docker/login-action/compare/v4.4.0...v4.6.0) Updates `anthropics/claude-code-action` from 1.0.179 to 1.0.183 - [Release notes](https://github.com/anthropics/claude-code-action/releases) - [Commits](https://github.com/anthropics/claude-code-action/compare/b76a0776ae74036e77cd11018083743453d7ad35...be7b93b1907a4abad570368f3c74b6fe3807510b) Updates `astral-sh/setup-uv` from 8.3.2 to 9.0.0 - [Release notes](https://github.com/astral-sh/setup-uv/releases) - [Commits](https://github.com/astral-sh/setup-uv/compare/v8.3.2...v9.0.0) Updates `pypa/gh-action-pypi-publish` from 1.14.1 to 1.14.2 - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/ba38be9e461d3875417946c167d0b5f3d385a247...dc37677b2e1c63e2034f94d8a5b11f265b73ba33) Updates `trufflesecurity/trufflehog` from 3.95.9 to 3.96.0 - [Release notes](https://github.com/trufflesecurity/trufflehog/releases) - [Commits](https://github.com/trufflesecurity/trufflehog/compare/27b0417c16317ca9a472a9a8092acce143b49c55...6f3c981e7b77f235fd2702dd74af25fc4b72bf11) Updates `actions/stale` from 10 to 11 - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v10...v11) --- updated-dependencies: - dependency-name: docker/login-action dependency-version: 4.6.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions - dependency-name: anthropics/claude-code-action dependency-version: 1.0.183 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: actions - dependency-name: astral-sh/setup-uv dependency-version: 9.0.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: pypa/gh-action-pypi-publish dependency-version: 1.14.2 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: actions - dependency-name: trufflesecurity/trufflehog dependency-version: 3.96.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions - dependency-name: actions/stale dependency-version: '11' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions ... Signed-off-by: dependabot[bot] <support@github.com>
docs: add API documentation infrastructure (#4348 )
2026-08-08 17:39:44 +00:00 · 2026-08-07 14:00:00 +00:00 · 2026-08-07 15:55:25 +02:00 · 2026-08-07 20:54:14 +08:00 · 2026-08-07 14:22:41 +02:00 · 2026-08-07 13:52:23 +02:00
129 changed files with 9380 additions and 1643 deletions
@@ -84,7 +84,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -242,7 +242,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -344,7 +344,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -451,7 +451,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -552,7 +552,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -660,7 +660,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -766,7 +766,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -870,7 +870,7 @@ jobs:

      - name: Login to Docker Hub
        if: ${{ env.DOCKERHUB_USERNAME != '' }}
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -53,7 +53,7 @@ jobs:

      - name: Run Claude Code
        id: claude
-        uses: anthropics/claude-code-action@b76a0776ae74036e77cd11018083743453d7ad35  # v1.0.179
+        uses: anthropics/claude-code-action@be7b93b1907a4abad570368f3c74b6fe3807510b  # v1.0.183
        with:
          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
          additional_permissions: |
@@ -61,7 +61,7 @@ jobs:
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -96,7 +96,7 @@ jobs:
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -33,7 +33,7 @@ jobs:
      github.event.workflow_run.event == 'pull_request' &&
      github.event.workflow_run.conclusion == 'success' &&
      github.repository == 'huggingface/lerobot'
-    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@931031bf2b54aabb134ceb54980a6a2860a00f11  # main
    with:
      package_name: lerobot
    secrets:
@@ -24,19 +24,24 @@ on:
        required: false
        type: string

-  # Triggers the workflow on push events to main for the docs folder
+  # 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.
  push:
    branches:
      - main
    paths:
      - "docs/**"
+      - "src/**"

-  # Triggers the workflow on pull request events targeting main for the docs folder
+  # Same for pull requests, so a docstring change gets a preview build and a broken `[[autodoc]]` path
+  # fails the PR rather than main.
  pull_request:
    branches:
      - main
    paths:
      - "docs/**"
+      - "src/**"

  release:
    types: [published]
@@ -55,16 +60,29 @@ jobs:
      github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@931031bf2b54aabb134ceb54980a6a2860a00f11  # main
    with:
      commit_sha: ${{ github.sha }}
      package: lerobot
+      # The shared workflow builds its venv with the runner's system Python, which is 3.10 on
+      # ubuntu-22.04. lerobot requires >=3.12, so without this the install fails during setup —
+      # before `pre_command` below ever runs. Added upstream in huggingface/doc-builder#808.
+      python_version: "3.12"
+      # 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 }}
@@ -78,9 +96,12 @@ jobs:
    permissions:
      contents: read
      pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@6108e850ae1cf2f71bb0815a600bcd50c39abfa7  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@931031bf2b54aabb134ceb54980a6a2860a00f11  # main
    with:
      commit_sha: ${{ github.event.pull_request.head.sha }}
      pr_number: ${{ github.event.number }}
      package: lerobot
-      additional_args: --not_python_module
+      # See the comment on build_main_docs. The PR workflow passes its own `--version pr_<n>`, so no
+      # additional_args are needed here.
+      python_version: "3.12"
+      pre_command: uv pip install "./lerobot[dataset]"
@@ -87,7 +87,7 @@ jobs:
          libusb-1.0-0-dev speech-dispatcher libgeos-dev portaudio19-dev

      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9  # v9.0.0
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -80,7 +80,7 @@ jobs:
          speech-dispatcher libgeos-dev portaudio19-dev

      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9  # v9.0.0
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -146,7 +146,7 @@ jobs:
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@af1e73f918a031802d376d3c8bbc3fe56130a9b0  # v4.4.0
+        uses: docker/login-action@dbcb813823bdd20940b903addbd779551569679f  # v4.6.0
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -53,7 +53,7 @@ jobs:
          persist-credentials: false

      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v8.3.2 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v9.0.0 # zizmor: ignore[unpinned-uses]
        with:
          version: ${{ env.UV_VERSION }}
          python-version: ${{ env.PYTHON_VERSION }}
@@ -115,7 +115,7 @@ jobs:
          speech-dispatcher libgeos-dev portaudio19-dev

      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@v8.3.2 # zizmor: ignore[unpinned-uses]
+        uses: astral-sh/setup-uv@v9.0.0 # zizmor: ignore[unpinned-uses]
        with:
          enable-cache: true
          version: ${{ env.UV_VERSION }}
@@ -168,7 +168,7 @@ jobs:
        with:
          cache-binary: false
      - name: Login to Docker Hub
-        uses: docker/login-action@v4.4.0 # zizmor: ignore[unpinned-uses]
+        uses: docker/login-action@v4.6.0 # zizmor: ignore[unpinned-uses]
        with:
          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
@@ -56,3 +56,41 @@ 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@c771a70e6277c0a99b617c7a806ffedaca235ff9  # v9.0.0
+        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
@@ -104,7 +104,7 @@ jobs:
      - name: Publish to TestPyPI for pre-releases
        # True for tags like 'v0.2.0-rc1'
        if: startsWith(github.ref, 'refs/tags/v') && contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
+        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33  # v1.14.2
        with:
          repository-url: https://test.pypi.org/legacy/
          verbose: true
@@ -112,7 +112,7 @@ jobs:

      - name: Publish to PyPI
        if: startsWith(github.ref, 'refs/tags/v') && !contains(github.ref, '-')
-        uses: pypa/gh-action-pypi-publish@ba38be9e461d3875417946c167d0b5f3d385a247  # v1.14.1
+        uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33  # v1.14.2
        with:
          verbose: true
          print-hash: true
@@ -137,7 +137,7 @@ jobs:
          git curl libglib2.0-0 libegl1-mesa-dev ffmpeg libusb-1.0-0-dev \
          speech-dispatcher libgeos-dev portaudio19-dev
      - name: Setup uv and Python
-        uses: astral-sh/setup-uv@11f9893b081a58869d3b5fccaea48c9e9e46f990  # v8.3.2
+        uses: astral-sh/setup-uv@c771a70e6277c0a99b617c7a806ffedaca235ff9  # v9.0.0
        with:
          enable-cache: true # zizmor: ignore[cache-poisoning]
          version: ${{ env.UV_VERSION }}
@@ -49,6 +49,6 @@ jobs:
          persist-credentials: false

      - name: Secret Scanning
-        uses: trufflesecurity/trufflehog@27b0417c16317ca9a472a9a8092acce143b49c55  # v3.95.9
+        uses: trufflesecurity/trufflehog@6f3c981e7b77f235fd2702dd74af25fc4b72bf11  # v3.96.0
        with:
          extra_args: --only-verified
@@ -52,7 +52,7 @@ jobs:
      issues: write
      pull-requests: write
    steps:
-      - uses: actions/stale@v10
+      - uses: actions/stale@v11
        with:
          repo-token: ${{ secrets.GITHUB_TOKEN }}
          stale-issue-label: stale
@@ -67,7 +67,11 @@ repos:
        args: [--prose-wrap=preserve]
        # Jinja2 model-card templates use a .md extension but contain {% ... %} /
        # {{ ... }} tags that prettier's Markdown formatter mangles (e.g. table loops).
-        exclude: ^src/lerobot/templates/.*\.md$
+        #
+        # 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)$

  ##### Security #####
  - repo: https://github.com/gitleaks/gitleaks
@@ -104,8 +108,13 @@ 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: ["-vv", "--config=pyproject.toml"]
+  #       args: ["--config=pyproject.toml"]
+  #       pass_filenames: false
@@ -50,6 +50,10 @@ 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**:
@@ -184,3 +184,29 @@ 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
@@ -128,6 +128,23 @@ lerobot-eval \

 Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the [EnvHub Documentation](https://huggingface.co/docs/lerobot/envhub).

+### Third-Party Hardware
+
+Beyond the natively supported hardware, the community maintains a growing ecosystem of plugins for other robots, teleoperators, cameras, and sensors - UFACTORY xArm, Universal Robots UR5e, Franka, AgileX Piper, Trossen WidowX, ARX5, I2RT YAM, GELLO, SpaceMouse, Meta Quest, ROS 2 bridges, tactile and depth cameras, and more.
+
+Plugins are auto-discovered by package name: LeRobot imports any installed package prefixed with `lerobot_robot_`, `lerobot_teleoperator_`, or `lerobot_camera_`. Install one and use the `type` it registers straight from the CLI:
+
+```bash
+pip install lerobot_robot_<name> lerobot_teleoperator_<name>
+
+lerobot-record \
+  --robot.type=<robot_name> \
+  --teleop.type=<teleoperator_name> \
+  --dataset.repo_id=${HF_USER}/my-dataset
+```
+
+Browse the full list in the [Third-Party Robots & Teleoperators](https://huggingface.co/docs/lerobot/main/third_party_robots) and [Third-Party Cameras & Sensors](https://huggingface.co/docs/lerobot/main/third_party_sensors) documentation.
+
 ## Resources

 - **[Documentation](https://huggingface.co/docs/lerobot/index):** The complete guide to tutorials & API.
@@ -0,0 +1,60 @@
+# 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]
@@ -191,6 +191,28 @@
 - 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
+  title: "API Reference"
@@ -0,0 +1,24 @@
+# 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
@@ -0,0 +1,27 @@
+# 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
@@ -0,0 +1,23 @@
+# 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
@@ -0,0 +1,19 @@
+# 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
@@ -0,0 +1,23 @@
+# 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
@@ -0,0 +1,20 @@
+# 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
@@ -0,0 +1,20 @@
+# 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
@@ -0,0 +1,147 @@
+# 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
@@ -0,0 +1,30 @@
+# 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
@@ -161,6 +161,16 @@ 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).
@@ -300,7 +310,7 @@ 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 `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.
+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.

 Mirror an existing policy that's structurally similar to yours; the diff is small.

@@ -344,7 +354,7 @@ 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). Use `PreTrainedPolicy.push_model_to_hub` so the repo gets `config.json`, `model.safetensors`, and a model card.
+**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.

 **Report results in your policy's MDX**, with the exact `lerobot-eval` command and hardware so anyone can re-run:

@@ -62,7 +62,10 @@ Reference data points on a 4×H100 80 GB cluster (`accelerate launch --num_proce
 | `smolvla`   | 27m 49s    |      0.312 |           0.011 | ~80%     | `--policy.path=lerobot/smolvla_base`, `freeze_vision_encoder=false`, `train_expert_only=false`                                 |
 | `pi05`      | 3h 41m     |      2.548 |           0.014 | ~95%     | `--policy.pretrained_path=lerobot/pi05_base`, `gradient_checkpointing=true`, `dtype=bfloat16`, vision encoder + expert trained |

-The `dataloading_s` vs. `update_s` ratio is the diagnostic that matters: when `dataloading_s` approaches `update_s`, more GPUs stop helping — your dataloader is the bottleneck and you should look at `--num_workers`, image resolution, and disk speed before adding compute.
+Training logs separate the full iteration into `dataloading_s` (`next(dl_iter)`), `preprocessing_s`
+(image conversion and the policy pipeline), and `update_s` (the optimizer update). `step_s` covers all
+three and drives `samples_per_s`. The benchmark above predates this split, so its `dataloading_s` includes
+preprocessing.

 ### Schedule and checkpoints

@@ -108,6 +108,7 @@ 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)"
@@ -127,7 +128,9 @@ ask_vqa_top:
      }
 ```

-Add one such sub-recipe per camera the dataset records.
+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.

 ## Layer 3 — training format

@@ -141,7 +144,20 @@ 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` 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.
+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.
@@ -142,6 +142,22 @@ repo_id = "yaak-ai/L2D-v3"
 dataset = StreamingLeRobotDataset(repo_id)  # streams directly from the Hub
 ```

+Datasets stored in an [HF Storage Bucket](https://huggingface.co/docs/hub/storage-buckets) (`hf://buckets/`) can be streamed the same way by passing `repo_type="bucket"`:
+
+```python
+dataset = StreamingLeRobotDataset("my-org/my-bucket", repo_type="bucket")
+```
+
+Both options are available in `lerobot-train` through `--dataset.streaming=true`, and `--dataset.repo_type=bucket` to stream from a bucket instead of a Hub dataset repo:
+
+```bash
+lerobot-train \
+  --dataset.repo_id=my-org/my-bucket \
+  --dataset.repo_type=bucket \
+  --dataset.streaming=true \
+  ...
+```
+
 <div style="display:flex; justify-content:center; gap:12px; flex-wrap:wrap;">
  <figure style="margin:0; text-align:center;">
    <img
@@ -242,6 +242,17 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
  --repo-id=your_dataset
 ```

+Recording, resuming, and merging aggregate quantiles from per-episode summaries, so `meta/stats.json` ends up holding a conservative envelope (`min` for `q <= 50`, `max` for `q > 50`) rather than whole-dataset quantiles. To estimate the latter, scan every episode with a running histogram:
+
+```bash
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
+  --repo-id=your_dataset \
+  --overwrite \
+  --skip-images
+```
+
+`--skip-images` keeps the existing image statistics and avoids video decoding when only `STATE`/`ACTION` need recomputing, and `--root` reads a local dataset instead of the Hub. These values are histogram estimates, subject to discretization and rebinning error, so they can differ from the conservative ones — which changes MolmoAct2's normalized targets and therefore its loss scale. Statistics already saved inside an existing checkpoint are not affected.
+
 Alternatively, train MolmoAct2 with mean/std normalization:

 ```bash
@@ -1,28 +1,29 @@
 # Multi-GPU Training

-This guide shows you how to train policies on multiple GPUs using [Hugging Face Accelerate](https://huggingface.co/docs/accelerate).
+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` |

 ## Installation

-`accelerate` is included in the `training` extra. Install it with:
+`accelerate` is included in the `training` extra:

 ```bash
 pip install 'lerobot[training]'
 ```

-## Training with Multiple GPUs
+## Launching

-You can launch training in two ways:
+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.

-### Option 1: Without config (specify parameters directly)
-
-You can specify all parameters directly in the command without running `accelerate config`:
+With `torchrun`:

 ```bash
-accelerate launch \
-  --multi_gpu \
-  --num_processes=2 \
-  $(which lerobot-train) \
+torchrun --nproc-per-node=2 $(which lerobot-train) \
  --dataset.repo_id=${HF_USER}/my_dataset \
  --policy.type=act \
  --policy.repo_id=${HF_USER}/my_trained_policy \
@@ -31,32 +32,10 @@ accelerate launch \
  --wandb.enable=true
 ```

-**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:
+With `accelerate launch` (as a plain launcher):

 ```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) \
+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 \
@@ -65,116 +44,133 @@ accelerate launch $(which lerobot-train) \
  --wandb.enable=true
 ```

-## How It Works
+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`).

-When you launch training with accelerate:
+> [!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.

-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
+## Batch semantics, learning rate, and steps

-## Learning Rate and Training Steps Scaling
+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:

-**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) \
-  --optimizer.lr=2e-4 \
-  --dataset.repo_id=lerobot/pusht \
-  --policy.type=act
+```
+effective_batch_size = batch_size × dp_world_size × gradient_accumulation_steps
 ```

-**Training Steps Scaling:**
+The training banner prints this factorization at startup. `--steps` counts loop steps (micro-batches per worker), not optimizer updates.

-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:
+Gradient accumulation is a first-class flag:

 ```bash
-# 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
+torchrun --nproc-per-node=2 $(which lerobot-train) \
+  --batch_size=8 --accelerator.gradient_accumulation.steps=4 ...
 ```

-## Training Large Models with FSDP
+**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`.

-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.
+## Sharded training (FSDP)

-An example on how to launch LeRobot training with FSDP across 4 GPUs (1 machine):
+If a model is too large to train with DDP, shard it with FSDP2:

 ```bash
-accelerate launch --config_file fsdp.yaml --num_processes=4 $(which lerobot-train) \
+torchrun --nproc-per-node=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
 ```

-A minimal `fsdp.yaml` (FSDP1; shards params/grads/optimizer — ZeRO-3-equivalent):
+`--parallelism.dp_shard=-1` shards over however many processes the launcher started.

-```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
+### 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
 ```

-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()`.
+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).

-### FSDP checkpoints
+Other sharding settings:

-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:
+- `--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.

- **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.
+### 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**.

 ## Notes

- 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.
+- 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).

-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).
+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).
@@ -127,6 +127,17 @@ lerobot-edit-dataset \

 Or keep the dataset as-is and pass `--policy.normalization_mapping='{"ACTION": "MEAN_STD", "STATE": "MEAN_STD", "VISUAL": "IDENTITY"}'`.

+Recording, resuming, and merging aggregate quantiles from per-episode summaries, so `meta/stats.json` ends up holding a conservative envelope (`min` for `q <= 50`, `max` for `q > 50`) rather than whole-dataset quantiles. To estimate the latter, scan every episode with a running histogram:
+
+```bash
+python src/lerobot/scripts/augment_dataset_quantile_stats.py \
+    --repo-id=your_dataset \
+    --overwrite \
+    --skip-images
+```
+
+`--skip-images` keeps the existing image statistics and avoids video decoding when only `STATE`/`ACTION` need recomputing, and `--root` reads a local dataset instead of the Hub. These values are histogram estimates, subject to discretization and rebinning error, so they can differ from the conservative ones — which changes π₀.₅'s normalized targets and therefore its loss scale. Statistics already saved inside an existing checkpoint are not affected.
+
 ### Training Command Example

 The same finetune with the VLM frozen: less memory, at some cost in success rate. Swap `--dataset.repo_id` for your own dataset.
@@ -2,6 +2,25 @@

 https://diffusion-policy.cs.columbia.edu

+## Training
+
+The reference implementation maintains an exponential moving average (EMA) of the policy weights during training and evaluates the EMA weights. To reproduce this behavior, enable the trainer's EMA shadow:
+
+```bash
+lerobot-train \
+  --policy.type=diffusion \
+  --ema.enable=true \
+  ...
+```
+
+Checkpoints then contain a directly loadable copy of the EMA weights next to the live ones, e.g. for evaluation:
+
+```bash
+lerobot-eval --policy.path=outputs/train/.../checkpoints/last/pretrained_model_ema ...
+```
+
+The EMA decay schedule (`--ema.inv_gamma`, `--ema.power`, ...) defaults to the reference implementation's values. For a constant decay instead of the warmup schedule (e.g. to match openpi's pi0/pi05 training), set `--ema.decay=0.99`.
+
 ## Citation

 ```bibtex
@@ -59,6 +59,22 @@ When `use_relative_actions=true`, the training script automatically:

 ---

+## EMA of the policy weights
+
+OpenPI maintains an exponential moving average of the weights during training (`ema_decay=0.99` by default) and keeps the EMA copy for inference. To reproduce this with the LeRobot trainer, enable the EMA shadow with a constant decay:
+
+```bash
+python -m lerobot.scripts.lerobot_train \
+  --policy.type=pi05 \
+  --dataset.repo_id=your_org/your_dataset \
+  --ema.enable=true \
+  --ema.decay=0.99
+```
+
+Checkpoints then contain a directly loadable copy of the EMA weights in `pretrained_model_ema/` next to the live ones. Note that the shadow is a full extra copy of the parameters on the GPU. Like OpenPI (which disables EMA in its LoRA configs), EMA is not supported together with PEFT adapters.
+
+---
+
 ## Citation

 If you use this work, please cite both **OpenPI** and the π₀.₅ paper:
@@ -40,3 +40,15 @@ 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.
@@ -0,0 +1,287 @@
+# 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    | &#91;`Robot`&#93;                   |
+| Method, show the full path   | &#91;`Robot.connect`&#93;           |
+| Method, show the bare name   | &#91;`~Robot.connect`&#93;          |
+| Nested path                  | &#91;`~robots.Robot.connect`&#93;   |
+| Object in another HF library | &#91;`~accelerate.Accelerator`&#93; |
+
+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.
@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"

 [project]
 name = "lerobot"
-version = "0.6.1"
+version = "0.6.2"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
@@ -346,6 +346,7 @@ 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"
@@ -400,7 +401,7 @@ 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" #, "A", "S", "D", "RUF"
+    "E", "W", "F", "I", "B", "C4", "T20", "N", "UP", "SIM", "D" #, "A", "S", "RUF"
 ]
 ignore = [
    "E501", # Line too long
@@ -410,9 +411,53 @@ ignore = [
 ]

 [tool.ruff.lint.per-file-ignores]
-"__init__.py" = ["F401", "F403", "E402"]
+"__init__.py" = ["F401", "F403", "E402", "D104"]
 # 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/rl/**" = ["D"]
+"src/lerobot/robots/**" = ["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"]
+# Package root: two one-line docstring fixes land with the docstring PR.
+"src/lerobot/__init__.py" = ["D"]
+"src/lerobot/__version__.py" = ["D"]
 [tool.ruff.lint.isort]
 combine-as-imports = true
 known-first-party = ["lerobot"]
@@ -456,25 +501,34 @@ default.extend-ignore-identifiers-re = [
    "seperated_timestep",
 ]

-# 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"]
+# 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 = 52
+output-format = "term-missing"
+color = true
+paths = ["src/lerobot"]
+exclude = ["src/lerobot/policies/molmoact2/molmoact2_hf_model"]

 # 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
@@ -521,6 +575,15 @@ 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
@@ -13,16 +13,41 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from pathlib import Path
+"""Training-output persistence: checkpoints, two-phase resume, and hub publishing.

-from huggingface_hub import HfApi, snapshot_download
+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 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,
@@ -40,14 +65,39 @@ 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."""
+    """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>`.
+    """
    step_identifier = get_step_identifier(step, total_steps)
    return output_dir / CHECKPOINTS_DIR / step_identifier

@@ -63,37 +113,15 @@ 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 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)
+def update_last_checkpoint(checkpoint_dir: Path) -> None:
+    """Point the `last` symlink in the checkpoints directory at the given checkpoint.

+    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.

-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:
+    Args:
+        checkpoint_dir (Path): The checkpoint step directory the `last` link should target.
+    """
    last_checkpoint_dir = checkpoint_dir.parent / LAST_CHECKPOINT_LINK
    if last_checkpoint_dir.is_symlink():
        last_checkpoint_dir.unlink()
@@ -101,6 +129,68 @@ def update_last_checkpoint(checkpoint_dir: Path) -> Path:
    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,
@@ -110,192 +200,301 @@ def save_checkpoint(
    scheduler: LRScheduler | None = None,
    preprocessor: PolicyProcessorPipeline | None = None,
    postprocessor: PolicyProcessorPipeline | None = None,
-    num_processes: int | None = None,
-    batch_size: int | None = None,
-    model_state_dict: dict | None = None,
-    optim_state_dict: dict | None = None,
+    accelerator: "Accelerator | 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
+    │   ├── 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})
    │   ├── train_config.json  # train config
-    │   ├── processor.json  # processor config (if preprocessor provided)
-    │   └── step_*.safetensors  # processor state files (if any)
+    │   ├── 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
    └── training_state/
-        ├── optimizer_param_groups.json  #  optimizer param groups
-        ├── optimizer_state.safetensors  # optimizer 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)
        ├── rng_state.safetensors  # rng states
-        ├── scheduler_state.json  # scheduler state
-        └── training_step.json  # training step
+        ├── 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.

    Args:
-        cfg (TrainPipelineConfig): The training config used for this run.
+        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.
        policy (PreTrainedPolicy): The policy to save.
-        optimizer (Optimizer | None, optional): The optimizer to save the state from. Defaults to None.
+        optimizer (Optimizer): The optimizer to save the state from.
        scheduler (LRScheduler | None, optional): The scheduler to save the state from. Defaults to None.
-        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.
+        preprocessor (PolicyProcessorPipeline | None, optional): The preprocessor/pipeline to save.
            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
-    policy.save_pretrained(pretrained_dir, state_dict=model_state_dict)
-    cfg.save_pretrained(pretrained_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 ----------------------------------
    if cfg.peft is not None:
-        # 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)
+        # 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)
+
    save_training_state(
-        checkpoint_dir,
-        step,
-        optimizer,
-        scheduler,
-        num_processes=num_processes,
-        batch_size=batch_size,
-        optim_state_dict=optim_state_dict,
+        checkpoint_dir, step, cfg, optimizer, scheduler, accelerator, sharded=sharded, model=policy_to_save
    )
+    if accelerator is not None:
+        accelerator.wait_for_everyone()


 def save_training_state(
    checkpoint_dir: Path,
-    train_step: int,
-    optimizer: Optimizer | None = None,
+    step: int,
+    cfg: TrainPipelineConfig,
+    optimizer: Optimizer | dict[str, Optimizer] | None = None,
    scheduler: LRScheduler | None = None,
-    num_processes: int | None = None,
-    batch_size: int | None = None,
-    optim_state_dict: dict | None = None,
+    accelerator: "Accelerator | None" = None,
+    *,
+    sharded: bool = False,
+    model: PreTrainedPolicy | None = None,
 ) -> None:
-    """
-    Saves the training step, optimizer state, scheduler state, and rng state.
+    """Write training_state/. Collective under sharding: call on every rank.

    Args:
-        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.
+        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.
            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.
+        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.
    """
    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)
-    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)
+
+    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)


-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.
+# ---------------------------------------------------------------------------------------------
+# 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`.

    Args:
-        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).
+        cfg (TrainPipelineConfig): The resumed training config; `cfg.checkpoint_path` locates
+            the checkpoint to restore from.
+
+    Returns:
+        int: The training step recorded in the checkpoint (micro-batch counter).

    Raises:
-        NotADirectoryError: If 'checkpoint_dir' doesn't contain a 'training_state' dir
-
-    Returns:
-        tuple[int, Optimizer, LRScheduler | None]: training step, optimizer and scheduler with their
-            state_dict loaded.
+        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.
    """
-    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
+    training_state_dir = cfg.checkpoint_path / 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)
-    step = load_training_step(training_state_dir)
-    if load_optimizer:
-        optimizer = load_optimizer_state(optimizer, 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)
+
    if scheduler is not None:
-        scheduler = load_scheduler_state(scheduler, training_state_dir)
-
-    return step, optimizer, scheduler
+        load_scheduler_state(scheduler, training_state_dir)


-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)
+# ---------------------------------------------------------------------------------------------
+# Hub: checkpoint push (resume artifact) and publishing (distribution artifact)
+# ---------------------------------------------------------------------------------------------


 def push_checkpoint_to_hub(
@@ -311,6 +510,16 @@ 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)
@@ -338,6 +547,16 @@ 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:
@@ -354,3 +573,214 @@ 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
@@ -22,7 +22,7 @@ Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """

 from .dataset import DatasetRecordConfig
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EMAConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .recipe import MessageTurn, TrainingRecipe, load_recipe
 from .types import (
@@ -57,6 +57,7 @@ __all__ = [
    # Config classes
    "DatasetRecordConfig",
    "DatasetConfig",
+    "EMAConfig",
    "EvalConfig",
    "JobConfig",
    "MessageTurn",
@@ -0,0 +1,273 @@
+#!/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,
+    )
@@ -14,6 +14,7 @@
 # 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
@@ -21,6 +22,8 @@ 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:
@@ -29,10 +32,15 @@ class DatasetConfig:
    # "dataset_index" into the returned item. The index mapping is made according to the order in which the
    # datasets are provided.
    repo_id: str
+    # Hub repository type: "dataset" (default) or "bucket" for an HF Storage Bucket streamed over
+    # hf://buckets/. Buckets are streaming-only, so "bucket" requires streaming=true.
+    repo_type: str = "dataset"
    # Root directory for a concrete local dataset tree (e.g. 'dataset/path'). If None, local datasets are
    # 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
@@ -48,6 +56,16 @@ class DatasetConfig:
    eval_split: float = 0.0

    def __post_init__(self) -> None:
+        if self.repo_type not in ("dataset", "bucket"):
+            raise ValueError(f"repo_type must be 'dataset' or 'bucket', got {self.repo_type!r}")
+        if self.repo_type == "bucket" and not self.streaming:
+            raise ValueError(
+                "repo_type='bucket' is streaming-only: set streaming=true to train from an HF Storage Bucket."
+            )
+        if self.repo_type == "bucket" and self.eval_split != 0.0:
+            raise ValueError(
+                "eval_split requires map-style datasets and is not supported with repo_type='bucket'."
+            )
        if self.depth_output_unit not in (DEPTH_METER_UNIT, DEPTH_MILLIMETER_UNIT):
            raise ValueError(
                f"depth_output_unit must be '{DEPTH_METER_UNIT}' or '{DEPTH_MILLIMETER_UNIT}', got {self.depth_output_unit!r}"
@@ -62,6 +80,14 @@ 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
@@ -113,6 +139,59 @@ class EvalConfig:
        return min(by_cpu, self.n_episodes, 64)


+@dataclass
+class EMAConfig:
+    """Exponential moving average (EMA) of the policy weights.
+
+    Standard practice for diffusion-style policies (Chi et al. 2023, "Diffusion Policy", section V.D):
+    the reference implementation enables it in every config and evaluates the EMA weights. Off by
+    default here because it keeps a second full copy of the parameters in memory.
+
+    The decay follows the warmup schedule from diffusers' `EMAModel`:
+    `decay_t = 1 - (1 + t / inv_gamma) ** -power`, clamped to `[min_decay, max_decay]`.
+    The defaults mirror the reference implementation. Alternatively, set `decay` for a constant
+    decay at every step, as used by openpi for pi0/pi05 (`ema_decay=0.99`).
+    """
+
+    enable: bool = False
+    # Constant decay coefficient (openpi-style, e.g. 0.99 for pi0/pi05). When set, the warmup
+    # schedule below is bypassed and the shadow uses this decay at every step.
+    decay: float | None = None
+    # Number of optimizer steps during which the shadow stays a hard copy of the live weights.
+    update_after_step: int = 0
+    # Warmup schedule parameters (see class docstring).
+    inv_gamma: float = 1.0
+    power: float = 0.75
+    min_decay: float = 0.0
+    max_decay: float = 0.9999
+    # Evaluate the EMA weights (instead of the live ones) during periodic env eval.
+    # Offline eval-loss (--eval_steps) always uses the live weights: it runs on every rank
+    # while the EMA shadow only lives on the main process.
+    use_for_eval: bool = True
+
+    def __post_init__(self) -> None:
+        if not (0.0 <= self.min_decay <= self.max_decay <= 1.0):
+            raise ValueError(
+                "Expected 0 <= ema.min_decay <= ema.max_decay <= 1, got "
+                f"min_decay={self.min_decay} and max_decay={self.max_decay}."
+            )
+        if self.inv_gamma <= 0:
+            raise ValueError(f"ema.inv_gamma must be positive, got {self.inv_gamma}.")
+        if self.power <= 0:
+            raise ValueError(f"ema.power must be positive, got {self.power}.")
+        if self.update_after_step < 0:
+            raise ValueError(f"ema.update_after_step must be >= 0, got {self.update_after_step}.")
+        if self.decay is not None:
+            if not 0.0 <= self.decay <= 1.0:
+                raise ValueError(f"ema.decay must be in [0, 1], got {self.decay}.")
+            # Keep the literals in sync with the field defaults above.
+            if self.min_decay != 0.0 or self.max_decay != 0.9999:
+                raise ValueError(
+                    "ema.decay (constant decay) and ema.min_decay/ema.max_decay (schedule clamp) are "
+                    "mutually exclusive: set one or the other."
+                )
+
+
@dataclass
 class PeftConfig:
    # PEFT offers many fine-tuning methods, layer adapters being the most common and currently also the most
@@ -0,0 +1,190 @@
+#!/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"))
@@ -23,6 +23,7 @@ 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)",
@@ -40,6 +41,7 @@ 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
@@ -78,7 +80,7 @@ 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:
@@ -99,13 +101,16 @@ 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`` is only meaningful inside a blend.
+    sub-recipes). ``weight`` and ``route`` are only meaningful inside a blend;
+    ``route: vqa`` gives sparse VQA annotations priority over normal weighted
+    selection.
    """

    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."""
@@ -113,6 +118,10 @@ 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()
@@ -147,8 +156,9 @@ class TrainingRecipe:
        return cls.from_dict(data)

    def _validate_message_recipe(self) -> None:
-        """Ensure every templated binding is known and at least one turn is a target."""
-        assert self.messages is not 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.")
        known_bindings = set(DEFAULT_BINDINGS) | set(self.bindings or {}) | {"task"}

        for turn in self.messages:
@@ -156,12 +166,19 @@ class TrainingRecipe:
            if missing:
                raise ValueError(f"MessageTurn references unknown binding(s): {sorted(missing)}")

-        if not any(turn.target for turn in self.messages):
-            raise ValueError("Message recipes must contain at least one target turn.")
+        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)."
+            )

    def _validate_blend_recipe(self) -> None:
        """Ensure each blend component is a non-empty, weighted message recipe."""
-        assert self.blend is not None
+        if self.blend is None:
+            raise ValueError("Cannot validate a blend recipe without blend components.")
        if not self.blend:
            raise ValueError("Blend recipes must contain at least one component.")

@@ -0,0 +1,16 @@
+# 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: "${subtask}", stream: low_level, if_present: subtask}
@@ -0,0 +1,13 @@
+# Paper-style joint sequence (pi0.5 §IV-B): one sample supervises the subtask
+# text with CE and, because the assistant turn is part of the prefix, conditions
+# the FAST and flow action losses on the same annotated subtask in one forward.
+# The supervised span is attended causally; the action losses see task + subtask.
+#
+# Pair with `--policy.joint_subtask_conditioning=true` at inference so the flow
+# prefix reproduces this layout (task turn with state + causal generated subtask).
+# Samples without a `subtask` annotation fall back to a plain task-prompt
+# low-level sample via `if_present`.
+
+messages:
+  - {role: user, content: "${task}", stream: low_level}
+  - {role: assistant, content: "${subtask}", stream: low_level, target: true, if_present: subtask}
@@ -0,0 +1,30 @@
+# Trains subtask prediction, subtask-conditioned action flow, and memory updates without plans.
+# Requires `subtask` and `memory`; missing `if_present` bindings skip the affected sub-recipe.
+
+blend:
+
+  high_level_subtask:
+    weight: 0.25
+    messages:
+      - {role: user, content: "${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}
@@ -0,0 +1,72 @@
+# 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}
@@ -18,6 +18,7 @@ import multiprocessing
 import os
 import tempfile
 from dataclasses import dataclass, field
+from enum import Enum
 from pathlib import Path
 from typing import Any

@@ -26,19 +27,49 @@ 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
 from lerobot.utils.sample_weighting import SampleWeightingConfig

 from . import parser
-from .default import DatasetConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
+from .default import DatasetConfig, EMAConfig, EvalConfig, JobConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 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 = (
@@ -121,10 +152,19 @@ 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)
+    # Maintain an EMA shadow of the policy weights during training (see EMAConfig).
+    ema: EMAConfig = field(default_factory=EMAConfig)
    wandb: WandBConfig = field(default_factory=WandBConfig)
    peft: PeftConfig | None = None

@@ -291,6 +331,60 @@ 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."""
@@ -76,7 +76,7 @@ import torch
 from pydantic import BaseModel, Field
 from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration

-from lerobot.datasets import LeRobotDataset
+from lerobot.datasets import LeRobotDataset, resolve_episode_indices


 # Pydantic Models for SARM Subtask Annotation
@@ -1049,7 +1049,10 @@ def main():
    torch_dtype = {"bfloat16": torch.bfloat16, "float16": torch.float16, "float32": torch.float32}[args.dtype]

    # Determine episodes
-    episode_indices = args.episodes or list(range(dataset.meta.total_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))
+    )

    existing_annotations = load_annotations_from_dataset(dataset.root, prefix="sparse")
    if args.skip_existing:
@@ -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
+from .utils import DEFAULT_EPISODES_PATH, create_lerobot_dataset_card, resolve_episode_indices
 from .video_utils import VideoEncodingManager

 # NOTE: Low-level I/O functions (cast_stats_to_numpy, get_parquet_file_size_in_mb, etc.)
@@ -97,6 +97,7 @@ __all__ = [
    "reencode_dataset",
    "remove_feature",
    "resolve_delta_timestamps",
+    "resolve_episode_indices",
    "safe_stop_image_writer",
    "split_dataset",
    "write_stats",
@@ -613,8 +613,15 @@ def aggregate_feature_stats(stats_ft_list: list[dict[str, dict]]) -> dict[str, d
        for q_key in quantile_keys:
            if all(q_key in s for s in stats_ft_list):
                quantile_values = np.stack([s[q_key] for s in stats_ft_list])
-                weighted_quantiles = quantile_values * counts
-                aggregated[q_key] = weighted_quantiles.sum(axis=0) / total_count
+                # Exact global quantiles cannot be recovered from quantile summaries.
+                # Keep a conservative envelope of the available estimates: min
+                # for lower quantiles and max for upper quantiles. The resulting
+                # values are bounds across the inputs, not global quantile estimates.
+                q_percent = int(q_key[1:])
+                if q_percent <= 50:
+                    aggregated[q_key] = np.min(quantile_values, axis=0)
+                else:
+                    aggregated[q_key] = np.max(quantile_values, axis=0)

    return aggregated

@@ -39,6 +39,7 @@ from .io_utils import (
    hf_transform_to_torch,
    load_nested_dataset,
 )
+from .utils import resolve_episode_indices
 from .video_utils import decode_video_frames


@@ -83,7 +84,7 @@ class DatasetReader:
        """
        self._meta = meta
        self.root = root
-        self.episodes = episodes
+        self.episodes = resolve_episode_indices(episodes, meta.total_episodes)
        self._tolerance_s = tolerance_s
        self._video_backend = video_backend
        if image_transforms is not None and not callable(image_transforms):
@@ -163,10 +164,34 @@ 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:
@@ -29,6 +29,7 @@ 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(
@@ -84,14 +85,24 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas

    if isinstance(cfg.dataset.repo_id, str):
        ds_meta = LeRobotDatasetMetadata(
-            cfg.dataset.repo_id, root=cfg.dataset.root, revision=cfg.dataset.revision
+            cfg.dataset.repo_id,
+            root=cfg.dataset.root,
+            revision=cfg.dataset.revision,
+            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(
+                    "repo_type='bucket' is streaming-only: set dataset.streaming=true to train from an HF Storage Bucket."
+                )
            dataset = LeRobotDataset(
                cfg.dataset.repo_id,
                root=cfg.dataset.root,
-                episodes=cfg.dataset.episodes,
+                episodes=episodes,
                delta_timestamps=delta_timestamps,
                image_transforms=image_transforms,
                revision=cfg.dataset.revision,
@@ -104,13 +115,14 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
            dataset = StreamingLeRobotDataset(
                cfg.dataset.repo_id,
                root=cfg.dataset.root,
-                episodes=cfg.dataset.episodes,
+                episodes=episodes,
                delta_timestamps=delta_timestamps,
                image_transforms=image_transforms,
                revision=cfg.dataset.revision,
                max_num_shards=cfg.num_workers,
                tolerance_s=cfg.tolerance_s,
                return_uint8=True,
+                repo_type=cfg.dataset.repo_type,
            )
    else:
        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
@@ -162,14 +162,32 @@ def render_sample(
    task: str | None = None,
    dataset_ctx: Any | None = None,
 ) -> RenderedMessages | None:
-    """Render the chat-style messages for a single dataset sample.
+    """Render recipe-defined messages and supervision for one dataset 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.
+    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.
    """
    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,
@@ -183,6 +201,58 @@ 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:
@@ -201,7 +271,8 @@ def _select_recipe(recipe: TrainingRecipe, sample_idx: int) -> TrainingRecipe:
        cumulative += component.weight or 0.0
        if draw < cumulative:
            return component
-    assert last_component is not None
+    if last_component is None:
+        raise ValueError("Blend recipes must contain at least one component.")
    return last_component


@@ -321,7 +392,8 @@ def _render_message_recipe(
    bindings: dict[str, LanguageRow | str | None],
 ) -> RenderedMessages | None:
    """Expand ``recipe.messages`` into rendered chat messages using ``bindings``."""
-    assert recipe.messages is not None
+    if recipe.messages is None:
+        raise ValueError("Cannot render a blend recipe as a message recipe.")
    messages: list[dict[str, Any]] = []
    streams: list[str | None] = []
    target_indices: list[int] = []
@@ -346,7 +418,9 @@ def _render_message_recipe(
        if turn.target:
            target_indices.append(message_idx)

-    if not target_indices:
+    # 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:
        return None

    rendered = {
@@ -403,14 +477,12 @@ def _validate_rendered(rendered: RenderedMessages) -> None:

    if len(streams) != len(messages):
        raise ValueError("message_streams must be aligned with messages.")
-    if not target_indices:
-        raise ValueError("Rendered samples must contain at least one target message.")
+    # 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.")
    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(
@@ -18,6 +18,7 @@ import dataclasses
 import importlib.resources
 import json
 import logging
+from collections.abc import Sequence
 from dataclasses import dataclass, field
 from pathlib import Path

@@ -98,6 +99,47 @@ 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"
@@ -0,0 +1,43 @@
+#!/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",
+]
@@ -0,0 +1,195 @@
+#!/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"
@@ -0,0 +1,139 @@
+#!/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, making train_config.json lie about what ran.
+_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 _ACCELERATE_ENV_VARS if name in os.environ)
+    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)
@@ -0,0 +1,112 @@
+#!/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)."
+        )
@@ -0,0 +1,94 @@
+#!/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)
@@ -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 PreTrainedPolicy.push_model_to_hub — the two must stay
+    # exact log line emitted by lerobot.common.train_utils.publish_trained_model — 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}"
@@ -20,7 +20,6 @@ from .optimizers import (
    SGDConfig as SGDConfig,
    XVLAAdamWConfig as XVLAAdamWConfig,
    load_optimizer_state,
-    load_optimizer_state_dict,
    save_optimizer_state,
 )
 from .schedulers import (
@@ -51,7 +50,6 @@ __all__ = [
    "VQBeTSchedulerConfig",
    # State management
    "load_optimizer_state",
-    "load_optimizer_state_dict",
    "load_scheduler_state",
    "save_optimizer_state",
    "save_scheduler_state",
@@ -27,7 +27,7 @@ from lerobot.utils.constants import (
    OPTIMIZER_PARAM_GROUPS,
    OPTIMIZER_STATE,
 )
-from lerobot.utils.io_utils import deserialize_json_into_object, load_json, write_json
+from lerobot.utils.io_utils import deserialize_json_into_object, write_json
 from lerobot.utils.utils import flatten_dict, unflatten_dict

 # Type alias for parameters accepted by optimizer build() methods.
@@ -52,6 +52,11 @@ 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"
@@ -245,6 +250,10 @@ 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.

@@ -283,35 +292,27 @@ 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.
+    """Save optimizer state to disk (non-sharded runs; sharded runs use the DCP channel).

    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, optim_state_dict=optim_state_dict)
+        _save_single_optimizer_state(optimizer, save_dir)


-def _save_single_optimizer_state(
-    optimizer: torch.optim.Optimizer, save_dir: Path, optim_state_dict: dict | None = None
-) -> None:
+def _save_single_optimizer_state(optimizer: torch.optim.Optimizer, save_dir: Path) -> None:
    """Save a single optimizer's state to disk."""
-    state = dict(optim_state_dict) if optim_state_dict is not None else optimizer.state_dict()
+    state = optimizer.state_dict()
    param_groups = state.pop("param_groups")
    flat_state = flatten_dict(state)
    save_file(flat_state, save_dir / OPTIMIZER_STATE)
@@ -365,19 +366,3 @@ 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),
-    }
@@ -47,6 +47,8 @@ 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,
@@ -242,6 +242,7 @@ 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.
@@ -252,22 +253,27 @@ def make_policy(
    can either initialize a new policy from scratch or load a pretrained one.

    Args:
-        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"`).
+        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).

    Returns:
-        An instantiated and device-placed policy model.
+        PreTrainedPolicy: 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.")
@@ -332,11 +338,18 @@ def make_policy(
        )

    if cfg.pretrained_path and not cfg.use_peft:
-        # 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)
+        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)
    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
@@ -54,6 +54,9 @@ 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,
@@ -604,6 +604,12 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
    ) -> torch.Tensor:
        """
        Optimized autoregressive decoding for FAST tokens using KV Caching.
+
+        Greedy decoding stops once every sequence emits the end-of-action marker. The
+        returned tensor keeps its fixed shape, with positions not generated after the
+        batch-wide stop left zero-filled. Stochastic decoding always runs to
+        ``max_decoding_steps`` so early stopping does not change the RNG state used by
+        subsequent calls.
        """
        if max_decoding_steps is None:
            max_decoding_steps = self.config.max_action_tokens
@@ -612,6 +618,12 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        device = tokens.device
        lm_head = self.paligemma_with_expert.paligemma.lm_head

+        # detokenize_actions() cuts at the first "|", so greedy decoding can stop once
+        # every sequence has emitted it. Keep stochastic decoding unchanged because
+        # skipping multinomial calls would shift the RNG state for subsequent calls.
+        end_of_action_token_id = self._paligemma_tokenizer.convert_tokens_to_ids("|")
+        finished = torch.zeros(bsize, dtype=torch.bool, device=device) if temperature == 0 else None
+
        # --- 1. PREFILL PHASE ---
        # Process Images + Text Prompt + BOS token once to populate the KV cache.

@@ -663,6 +675,10 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
        # Initialize storage for generated tokens
        generated_action_tokens = torch.zeros((bsize, max_decoding_steps), dtype=torch.long, device=device)
        generated_action_tokens[:, 0] = next_token.squeeze(-1)
+        if finished is not None:
+            finished |= next_token.squeeze(-1) == end_of_action_token_id
+            if bool(finished.all()):
+                return generated_action_tokens

        # Track valid tokens mask (0 for pad, 1 for valid)
        # We need this to tell the new token what it can attend to (images + text + past actions)
@@ -713,6 +729,11 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`

            generated_action_tokens[:, t] = next_token.squeeze(-1)

+            if finished is not None:
+                finished |= next_token.squeeze(-1) == end_of_action_token_id
+                if bool(finished.all()):
+                    break
+
        return generated_action_tokens


@@ -18,20 +18,17 @@ import builtins
 import dataclasses
 import logging
 import os
-from importlib.resources import files
+import warnings
 from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, TypedDict, TypeVar, Unpack
+from typing import TYPE_CHECKING, Any, ClassVar, TypedDict, TypeVar, Unpack

-from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download, save_torch_state_dict
+from huggingface_hub import 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, save_model as save_model_as_safetensor
+from safetensors.torch import load_model as load_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
@@ -46,56 +43,14 @@ 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")

-
-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
+# 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"


 class ActionSelectKwargs(TypedDict, total=False):
@@ -110,6 +65,22 @@ 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):
@@ -127,43 +98,33 @@ 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: 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).
+    def _save_pretrained(self, save_directory: Path) -> None:
+        """Serialize this policy's parameters (and config) into `save_directory`.

-        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.
+        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).
        """
-        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
+        # 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

-    def _save_pretrained(self, save_directory: Path, state_dict: dict[str, Tensor] | None = None) -> None:
-        self.config._save_pretrained(save_directory)
        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))
+        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
-        # 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))
+        self.config._save_pretrained(save_directory)
+        save_torch_state_dict(state_dict, str(save_directory), max_shard_size=_SINGLE_FILE_SHARD_SIZE)

    @classmethod
    def from_pretrained(
@@ -291,92 +252,39 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        peft_model=None,
        state_dict: dict[str, Tensor] | None = None,
        dataset_meta: LeRobotDatasetMetadata | None = None,
-    ):
-        api = HfApi()
-        repo_id = api.create_repo(
-            repo_id=self.config.repo_id, private=self.config.private, exist_ok=True
-        ).repo_id
+    ) -> None:
+        """Publish this policy to the Hub.

-        # Push the files to the repo in a single commit
-        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
-            saved_path = Path(tmp) / repo_id
+        Deprecated: use :func:`lerobot.common.train_utils.publish_trained_model` instead, which
+        also publishes the pre/post-processors alongside the 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)
+        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

-            card = self.generate_model_card(
-                cfg.dataset.repo_id,
-                self.config.type,
-                self.config.license,
-                self.config.tags,
-                cfg=cfg,
-                dataset_meta=dataset_meta,
+        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.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
+        publish_trained_model(cfg, self, None, None, dataset_meta, peft_model=peft_model)

    def wrap_with_peft(
        self,
@@ -175,9 +175,6 @@ 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)):
@@ -647,10 +647,15 @@ def main():
    tags = set(tags).union({"robotics", "lerobot", policy_type})
    tags = list(tags)

-    # Generate model card
-    card = policy.generate_model_card(
-        dataset_repo_id=dataset_repo_id, model_type=policy_type, license=license, tags=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)

    # Save model card locally
    card.save(str(output_dir / "README.md"))
@@ -41,7 +41,7 @@ from pathlib import Path
 from typing import Any, TypedDict, TypeVar, cast

 import torch
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, snapshot_download
 from safetensors.torch import load_file, save_file

 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
@@ -212,6 +212,10 @@ 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
@@ -556,6 +560,22 @@ 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)
@@ -740,7 +760,13 @@ 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, hub_download_kwargs, is_local_source
+            loaded_config,
+            overrides or {},
+            model_id,
+            base_path,
+            config_filename,
+            hub_download_kwargs,
+            is_local_source,
        )

        # 4. Validate that all overrides were used
@@ -936,6 +962,7 @@ 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]]:
@@ -945,6 +972,11 @@ 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
@@ -978,6 +1010,8 @@ 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.

@@ -990,15 +1024,80 @@ 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, hub_download_kwargs, is_local_source
+                step_instance,
+                step_entry,
+                model_id,
+                base_path,
+                config_filename,
+                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,
@@ -1158,6 +1257,7 @@ 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:
@@ -1198,6 +1298,8 @@ 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.

@@ -1223,7 +1325,7 @@ class DataProcessorPipeline[TInput, TOutput](HubMixin):
            # Download from Hub
            state_path = hf_hub_download(
                repo_id=model_id,
-                filename=state_filename,
+                filename=(Path(config_filename).parent / state_filename).as_posix(),
                repo_type="model",
                **hub_download_kwargs,
            )
@@ -16,9 +16,11 @@

 from __future__ import annotations

-from dataclasses import dataclass
+from dataclasses import asdict, 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
@@ -32,25 +34,46 @@ from .pipeline import ProcessorStep, ProcessorStepRegistry
@dataclass
@ProcessorStepRegistry.register(name="render_messages_processor")
 class RenderMessagesStep(ProcessorStep):
-    """Processor step that turns raw language columns into rendered chat messages.
+    """Turn raw language columns into recipe-defined messages and supervision.

-    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.
+    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.
    """

    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 for a single transition; return ``None`` to drop it."""
+        """Render messages, preserving unannotated samples and dropping unmatched annotated ones."""
        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:
-            return transition
+            # 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)

        timestamp = complementary_data.get("timestamp")
        if timestamp is None:
@@ -67,18 +90,171 @@ class RenderMessagesStep(ProcessorStep):
            dataset_ctx=self.dataset_ctx,
        )
        if rendered is None:
-            return 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

        new_transition = transition.copy()
-        new_complementary_data = dict(complementary_data)
+        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.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": [],
+    }
@@ -25,6 +25,7 @@ from __future__ import annotations

 import logging
 from dataclasses import dataclass, field
+from pathlib import Path
 from typing import TYPE_CHECKING, Any

 import torch
@@ -32,6 +33,7 @@ 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,
@@ -136,7 +138,7 @@ 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
@@ -293,6 +295,15 @@ 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]]:
@@ -349,6 +360,7 @@ 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)
@@ -412,14 +424,15 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
            # During inference, no action is available, skip tokenization
            return new_transition

-        # Tokenize and get both tokens and mask
-        tokens, mask = self._tokenize_action(action)
+        # Tokenize and get masks for the full formatted sequence and the discrete action codes.
+        tokens, mask, code_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
@@ -430,7 +443,7 @@ 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]:
+    def _tokenize_action(self, action: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Tokenizes the action tensor and creates a mask.

@@ -459,6 +472,7 @@ 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)
@@ -476,65 +490,82 @@ 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
-            # add bos
+            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)
            tokens = torch.cat(
                [
                    torch.tensor([bos_id], device=action.device),
-                    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),
+                    prompt_tokens,
+                    action_code_tokens,
+                    end_tokens,
                ]
            )
+            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(
-                            self.max_action_tokens - len(tokens), dtype=torch.bool, device=action.device
-                        ),
+                        torch.zeros(pad_len, 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, self.max_action_tokens - len(tokens)), value=0)
+                tokens = torch.nn.functional.pad(tokens, (0, pad_len), 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
+        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)
+        tokens, _, _ = self._tokenize_action(action)
        return tokens

    def get_config(self) -> dict[str, Any]:
@@ -550,6 +581,9 @@ class ActionTokenizerProcessorStep(ActionProcessorStep):
        config = {
            "trust_remote_code": self.trust_remote_code,
            "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
@@ -558,6 +592,14 @@ 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]]:
@@ -16,12 +16,11 @@ import abc
 import builtins
 import logging
 import os
-from importlib.resources import files
+import warnings
 from pathlib import Path
-from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any, TypeVar

-from huggingface_hub import HfApi, ModelCard, ModelCardData, hf_hub_download
+from huggingface_hub import 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
@@ -61,6 +60,22 @@ 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))
@@ -175,53 +190,22 @@ class PreTrainedRewardModel(nn.Module, HubMixin, abc.ABC):
        """
        return type(self).forward is not PreTrainedRewardModel.forward

-    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
+    def push_model_to_hub(self, cfg: "TrainPipelineConfig") -> None:
+        """Publish this reward model to the Hub.

-        # Push the files to the repo in a single commit
-        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
-            saved_path = Path(tmp) / repo_id
+        Deprecated: use :func:`lerobot.common.train_utils.publish_trained_model` instead.

-            self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
+        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

-            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,
+        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,
        )
-
-        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
+        publish_trained_model(cfg, self, None, None, None)
@@ -16,6 +16,7 @@

 from __future__ import annotations

+import logging
 import random
 from typing import TYPE_CHECKING, Any

@@ -69,6 +70,8 @@ from .sarm_utils import (
    pad_state_to_max_dim,
 )

+logger = logging.getLogger(__name__)
+

 class SARMEncodingProcessorStep(ProcessorStep):
    """ProcessorStep that encodes images and text with CLIP and generates stage and progress labels for SARM."""
@@ -108,6 +111,8 @@ class SARMEncodingProcessorStep(ProcessorStep):
            else None
        )

+        self._validate_annotation_columns()
+
        self.device = torch.device(
            self.config.device if self.config.device else "cuda" if torch.cuda.is_available() else "cpu"
        )
@@ -120,6 +125,78 @@ class SARMEncodingProcessorStep(ProcessorStep):
        self.verbs = ["move", "grasp", "rotate", "push", "pull", "slide", "lift", "place"]
        self.fake = Faker()

+    @staticmethod
+    def _resolve_annotation_column(episodes_df: pd.DataFrame, annotation_type: str, suffix: str) -> str:
+        """Resolve a mode-specific annotation column, falling back to the legacy unprefixed name."""
+        prefixed = f"{annotation_type}_{suffix}"
+        return prefixed if prefixed in episodes_df.columns else suffix
+
+    @staticmethod
+    def _annotations_are_usable(names: Any, starts: Any, ends: Any) -> bool:
+        """Return whether an episode has non-empty, aligned annotation arrays."""
+        values = (names, starts, ends)
+        if not all(isinstance(value, (list, tuple, np.ndarray)) for value in values):
+            return False
+
+        lengths = {len(value) for value in values}
+        return len(lengths) == 1 and next(iter(lengths)) > 0
+
+    def _validate_annotation_columns(self) -> None:
+        """Validate annotation coverage before loading models or generating training targets.
+
+        A multi-stage head with no usable episode annotations would otherwise train entirely
+        against all-zero targets. Reject that configuration and warn when only part of the
+        dataset is usable.
+        """
+        if self.dataset_meta is None:
+            return
+        episodes_df = self.dataset_meta.episodes.to_pandas()
+        num_episodes = len(episodes_df)
+
+        modes = []
+        if self.dense_subtask_names and len(self.dense_subtask_names) > 1:
+            modes.append(("dense", self.dense_subtask_names))
+        if self.sparse_subtask_names and len(self.sparse_subtask_names) > 1:
+            modes.append(("sparse", self.sparse_subtask_names))
+
+        for annotation_type, names in modes:
+            columns = [
+                self._resolve_annotation_column(episodes_df, annotation_type, suffix)
+                for suffix in ("subtask_names", "subtask_start_frames", "subtask_end_frames")
+            ]
+            missing_columns = [column for column in columns if column not in episodes_df.columns]
+            if missing_columns:
+                num_usable = 0
+            else:
+                num_usable = sum(
+                    self._annotations_are_usable(*(episodes_df.loc[ep_idx, column] for column in columns))
+                    for ep_idx in episodes_df.index
+                )
+
+            if num_usable == 0:
+                missing_columns_message = (
+                    f" Missing required columns: {', '.join(missing_columns)}." if missing_columns else ""
+                )
+                raise ValueError(
+                    f"SARM {annotation_type} head is configured with {len(names)} stages, but none of "
+                    f"the {num_episodes} episodes have usable annotations in meta/episodes/*.parquet. "
+                    f"Required columns: {', '.join(columns)}.{missing_columns_message} "
+                    "Training would produce all-zero "
+                    "targets. Materialize the annotations into the episodes metadata before training."
+                )
+
+            num_unusable = num_episodes - num_usable
+            if num_unusable:
+                logger.warning(
+                    "SARM %s head: %d/%d episodes have unusable annotations in columns %s; "
+                    "their targets will be 0 and only the %d annotated episodes will train the head.",
+                    annotation_type,
+                    num_unusable,
+                    num_episodes,
+                    ", ".join(columns),
+                    num_usable,
+                )
+
    def _find_episode_for_frame(self, frame_idx: int) -> int:
        """Find the episode index for a given frame index."""
        for ep_idx in range(len(self.dataset_meta.episodes)):
@@ -167,24 +244,18 @@ class SARMEncodingProcessorStep(ProcessorStep):
        if episodes_df is None or len(global_names) == 1:
            return None, None, None

-        # Resolve column name with fallback
-        def col(suffix):
-            prefixed = f"{annotation_type}_{suffix}"
-            return prefixed if prefixed in episodes_df.columns else suffix
-
-        col_names = col("subtask_names")
-        if col_names not in episodes_df.columns or ep_idx >= len(episodes_df):
+        columns = [
+            self._resolve_annotation_column(episodes_df, annotation_type, suffix)
+            for suffix in ("subtask_names", "subtask_start_frames", "subtask_end_frames")
+        ]
+        if any(column not in episodes_df.columns for column in columns) or ep_idx >= len(episodes_df):
            return None, None, None

-        subtask_names = episodes_df.loc[ep_idx, col_names]
-        if subtask_names is None or (isinstance(subtask_names, float) and pd.isna(subtask_names)):
+        annotations = tuple(episodes_df.loc[ep_idx, column] for column in columns)
+        if not self._annotations_are_usable(*annotations):
            return None, None, None

-        return (
-            subtask_names,
-            episodes_df.loc[ep_idx, col("subtask_start_frames")],
-            episodes_df.loc[ep_idx, col("subtask_end_frames")],
-        )
+        return annotations

    def __call__(self, transition: EnvTransition) -> EnvTransition:
        """
@@ -58,12 +58,11 @@ import builtins
 import logging
 import os
 from pathlib import Path
-from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any, TypeVar

 import numpy as np
 import torch
-from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub import hf_hub_download
 from huggingface_hub.constants import CONFIG_NAME
 from huggingface_hub.errors import HfHubHTTPError
 from torch import Tensor
@@ -75,9 +74,6 @@ 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:
@@ -205,34 +201,3 @@ 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}")
@@ -74,13 +74,14 @@ from torch.optim.optimizer import Optimizer
 from lerobot.cameras import opencv  # noqa: F401
 from lerobot.common.train_utils import (
    get_step_checkpoint_dir,
-    load_training_state as utils_load_training_state,
+    load_training_metadata,
    save_checkpoint,
    update_last_checkpoint,
 )
 from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.datasets import LeRobotDataset, make_dataset
+from lerobot.optim import load_optimizer_state
 from lerobot.policies import make_policy, make_pre_post_processors
 from lerobot.robots import so_follower  # noqa: F401
 from lerobot.teleoperators import gamepad, so_leader  # noqa: F401
@@ -103,7 +104,7 @@ from lerobot.utils.constants import (
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.io_utils import load_json, write_json
 from lerobot.utils.process import ProcessSignalHandler, ensure_multiprocessing_start_method
-from lerobot.utils.random_utils import set_seed
+from lerobot.utils.random_utils import load_rng_state, set_seed
 from lerobot.utils.utils import (
    format_big_number,
    init_logging,
@@ -716,15 +717,18 @@ def load_training_state(
    algorithm-owned tensors) from the most recent checkpoint.

    Args:
-        cfg: Training configuration.
-        optimizers: Optimizers to load state into.
-        algorithm: Algorithm whose state dict should be restored.
-            Required for full main-equivalent resume;
-            the policy itself is restored separately via ``make_policy``.
-        device: Device on which to place loaded algorithm tensors.
+        cfg (TrainRLServerPipelineConfig): Training configuration; `cfg.resume` gates the load and
+            `cfg.output_dir` locates the last checkpoint.
+        optimizers (Optimizer | dict[str, Optimizer]): Optimizers to load state into.
+        algorithm (RLAlgorithm | None, optional): Algorithm whose state dict should be restored.
+            Required for full main-equivalent resume; the policy itself is restored separately via
+            `make_policy`. Defaults to None.
+        device (str | torch.device, optional): Device on which to place loaded algorithm tensors.
+            Defaults to "cpu".

    Returns:
-        tuple: (optimization_step, interaction_step) or (None, None) if not resuming
+        tuple[int | None, int | None]: `(optimization_step, interaction_step)`, or `(None, None)`
+        when not resuming or when loading the training state fails.
    """
    if not cfg.resume:
        return None, None
@@ -736,7 +740,10 @@ def load_training_state(

    try:
        # Restore optimizers + RNG + step from the standard `training_state/` folder
-        step, optimizers, _ = utils_load_training_state(checkpoint_dir, optimizers, None)
+        training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
+        load_rng_state(training_state_dir)
+        step = load_training_metadata(training_state_dir)["step"]
+        optimizers = load_optimizer_state(optimizers, training_state_dir)

        # Restore algorithm-owned tensors
        if algorithm is not None:
@@ -404,7 +404,10 @@ class LeKiwi(Robot):
            present_pos = self.bus.sync_read(
                "Present_Position", self.arm_motors, num_retry=self.config.num_read_retries
            )
-            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in arm_goal_pos.items()}
+            # `arm_goal_pos` is keyed with the ".pos" suffix, `present_pos` with bare motor names.
+            goal_present_pos = {
+                key: (g_pos, present_pos[key.removesuffix(".pos")]) for key, g_pos in arm_goal_pos.items()
+            }
            arm_safe_goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
            arm_goal_pos = arm_safe_goal_pos

@@ -25,6 +25,11 @@ quantile statistics (q01, q10, q50, q90, q99) in their metadata. This script:
 3. If missing, computes quantile statistics for all features
 4. Updates the dataset metadata with the new quantile statistics

+Statistics are accumulated into a single running histogram per feature across
+all episodes rather than aggregating per-episode quantile summaries. The
+resulting quantiles are histogram approximations, subject to discretization and
+range-rebinning error; image/video frames are sampled by default.
+
 Usage:

 ```bash
@@ -34,9 +39,7 @@ python src/lerobot/scripts/augment_dataset_quantile_stats.py \
 """

 import argparse
-import concurrent.futures
 import logging
-import os
 from pathlib import Path

 import numpy as np
@@ -49,11 +52,10 @@ from lerobot.datasets import (
    CODEBASE_VERSION,
    DEFAULT_QUANTILES,
    LeRobotDataset,
-    aggregate_stats,
    get_feature_stats,
    write_stats,
 )
-from lerobot.datasets.compute_stats import sample_indices
+from lerobot.datasets.compute_stats import RunningQuantileStats, sample_indices
 from lerobot.utils.utils import init_logging


@@ -79,20 +81,25 @@ def has_quantile_stats(stats: dict[str, dict] | None, quantile_list_keys: list[s
    return False


-def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampling: bool = True) -> dict:
-    """Process a single episode and return its statistics.
+def collect_episode_arrays(
+    dataset: LeRobotDataset,
+    episode_idx: int,
+    use_sampling: bool = True,
+    skip_images: bool = False,
+) -> dict[str, tuple[np.ndarray, int]]:
+    """Collect one episode's frames per feature, flattened to (num_samples, dim).

    Args:
        dataset: The LeRobot dataset
-        episode_idx: Index of the episode to process
-        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).
+        episode_idx: Index of the episode to read
+        use_sampling: If True, sub-sample image/video frames to bound memory.
+            If False, use every frame (higher memory).
+        skip_images: If True, skip image/video features entirely.

    Returns:
-        Dictionary containing episode statistics
+        Mapping of feature name to that episode's values and the number of frames
+        they came from (which differs from the row count for image features).
    """
-    logging.info(f"Computing stats for episode {episode_idx}")
-
    start_idx = dataset.meta.episodes[episode_idx]["dataset_from_index"]
    end_idx = dataset.meta.episodes[episode_idx]["dataset_to_index"]

@@ -102,7 +109,9 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
    # numeric columns are cheap, so read them in full (exact).
    image_keys = [k for k in dataset.features if dataset.features[k]["dtype"] in ("image", "video")]
    numeric_keys = [
-        k for k in dataset.features if dataset.features[k]["dtype"] not in ("image", "video", "string")
+        k
+        for k in dataset.features
+        if dataset.features[k]["dtype"] not in ("image", "video", "string", "language")
    ]

    collected_data: dict[str, list] = {}
@@ -114,7 +123,7 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
            collected_data[key] = [torch.as_tensor(v) for v in numeric_cols[key]]

    # Image/video features: decode only a sampled subset of frames.
-    if image_keys:
+    if image_keys and not skip_images:
        sampled_offsets = sample_indices(episode_len) if use_sampling else list(range(episode_len))
        for offset in sampled_offsets:
            item = dataset[start_idx + offset]
@@ -122,87 +131,82 @@ def process_single_episode(dataset: LeRobotDataset, episode_idx: int, use_sampli
                if key in item:
                    collected_data.setdefault(key, []).append(item[key])

-    ep_stats = {}
+    episode_arrays: dict[str, tuple[np.ndarray, int]] = {}
    for key, data_list in collected_data.items():
-        if dataset.features[key]["dtype"] == "string":
-            continue
-
        data = torch.stack(data_list).cpu().numpy()
        if dataset.features[key]["dtype"] in ["image", "video"]:
            if data.dtype == np.uint8:
                data = data.astype(np.float32) / 255.0
-
-            axes_to_reduce = (0, 2, 3)
-            keepdims = True
+            # (N, C, H, W) -> (N * H * W, C) so quantiles are computed per channel.
+            channels = data.shape[1]
+            values = data.transpose(0, 2, 3, 1).reshape(-1, channels)
        else:
-            axes_to_reduce = 0
-            keepdims = data.ndim == 1
+            values = data.reshape(-1, data.shape[-1]) if data.ndim > 1 else data.reshape(-1, 1)
+        episode_arrays[key] = (values, len(data_list))

-        ep_stats[key] = get_feature_stats(
-            data, axis=axes_to_reduce, keepdims=keepdims, quantile_list=DEFAULT_QUANTILES
-        )
-
-        if dataset.features[key]["dtype"] in ["image", "video"]:
-            ep_stats[key] = {
-                k: v if k == "count" else np.squeeze(v, axis=0) for k, v in ep_stats[key].items()
-            }
-
-    return ep_stats
+    return episode_arrays


-def compute_quantile_stats_for_dataset(dataset: LeRobotDataset, use_sampling: bool = True) -> dict[str, dict]:
-    """Compute quantile statistics for all episodes in the dataset.
+def compute_quantile_stats_for_dataset(
+    dataset: LeRobotDataset,
+    use_sampling: bool = True,
+    skip_images: bool = False,
+) -> dict[str, dict]:
+    """Compute whole-dataset statistics with one running histogram per feature.

    Args:
        dataset: The LeRobot dataset to compute statistics for
        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).
+            memory. If False, use every frame (higher memory).
+        skip_images: If True, skip image/video features and leave their stats untouched.

    Returns:
-        Dictionary containing aggregated statistics with quantiles
+        Dictionary containing statistics with histogram-based global quantile estimates

    Note:
-        Video decoding operations are not thread-safe, so we process episodes sequentially
-        when video keys are present. For datasets without videos, we use parallel processing
-        with ThreadPoolExecutor for better performance.
+        Episodes are accumulated sequentially because the running accumulators are
+        shared across all of them.
    """
    logging.info(f"Computing quantile statistics for dataset with {dataset.num_episodes} episodes")

-    episode_stats_list = []
-    has_videos = len(dataset.meta.video_keys) > 0
+    running_stats: dict[str, RunningQuantileStats] = {}
+    frame_counts: dict[str, int] = {}
+    row_counts: dict[str, int] = {}
+    # Kept only while a feature has a single row, so it can still be finalized.
+    single_row_arrays: dict[str, np.ndarray] = {}

-    if has_videos:
-        logging.info("Dataset contains video keys - using sequential processing for thread safety")
-        for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"):
-            ep_stats = process_single_episode(dataset, episode_idx, use_sampling)
-            episode_stats_list.append(ep_stats)
-    else:
-        logging.info("Dataset has no video keys - using parallel processing for better performance")
-        max_workers = min(dataset.num_episodes, int(os.environ.get("LEROBOT_STATS_MAX_WORKERS", 16)))
+    for episode_idx in tqdm(range(dataset.num_episodes), desc="Processing episodes"):
+        episode_arrays = collect_episode_arrays(
+            dataset, episode_idx, use_sampling=use_sampling, skip_images=skip_images
+        )
+        for key, (array, num_frames) in episode_arrays.items():
+            running_stats.setdefault(key, RunningQuantileStats()).update(array)
+            frame_counts[key] = frame_counts.get(key, 0) + num_frames
+            row_counts[key] = row_counts.get(key, 0) + len(array)
+            if row_counts[key] < 2:
+                single_row_arrays[key] = array
+            else:
+                single_row_arrays.pop(key, None)

-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
-            future_to_episode = {
-                executor.submit(process_single_episode, dataset, episode_idx, use_sampling): episode_idx
-                for episode_idx in range(dataset.num_episodes)
-            }
-
-            episode_results = {}
-            with tqdm(total=dataset.num_episodes, desc="Processing episodes") as pbar:
-                for future in concurrent.futures.as_completed(future_to_episode):
-                    episode_idx = future_to_episode[future]
-                    ep_stats = future.result()
-                    episode_results[episode_idx] = ep_stats
-                    pbar.update(1)
-
-        for episode_idx in range(dataset.num_episodes):
-            if episode_idx in episode_results:
-                episode_stats_list.append(episode_results[episode_idx])
-
-    if not episode_stats_list:
+    if not running_stats:
        raise ValueError("No episode data found for computing statistics")

-    logging.info(f"Aggregating statistics from {len(episode_stats_list)} episodes")
-    return aggregate_stats(episode_stats_list)
+    aggregated_stats: dict[str, dict] = {}
+    for key, accumulator in running_stats.items():
+        if row_counts[key] < 2:
+            # Histograms need at least two samples; mirror get_feature_stats' basic-stats path.
+            stats = get_feature_stats(single_row_arrays[key], axis=0, keepdims=False)
+        else:
+            stats = accumulator.get_statistics()
+        if dataset.features[key]["dtype"] in ["image", "video"]:
+            # Image stats are stored as (C, 1, 1) to broadcast over height and width.
+            stats = {k: v if k == "count" else v[:, np.newaxis, np.newaxis] for k, v in stats.items()}
+        # `get_feature_stats` counts frames, not the per-channel rows the accumulator sees.
+        stats["count"] = np.array([frame_counts[key]])
+        aggregated_stats[key] = stats
+
+    logging.info(f"Computed global histogram statistics for {len(aggregated_stats)} features")
+    return aggregated_stats


 def augment_dataset_with_quantile_stats(
@@ -210,6 +214,7 @@ def augment_dataset_with_quantile_stats(
    root: str | Path | None = None,
    overwrite: bool = False,
    use_sampling: bool = True,
+    skip_images: bool = False,
 ) -> None:
    """Augment a dataset with quantile statistics if they are missing.

@@ -218,7 +223,8 @@ def augment_dataset_with_quantile_stats(
        root: Local root directory for the dataset
        overwrite: Overwrite existing quantile statistics if they already exist
        use_sampling: If True, sub-sample image/video frames per episode to bound
-            memory. If False, use every frame (exact, higher memory).
+            memory. If False, use every frame (higher memory).
+        skip_images: If True, skip image/video features and keep their existing stats
    """
    logging.info(f"Loading dataset: {repo_id}")
    dataset = LeRobotDataset(
@@ -232,7 +238,13 @@ def augment_dataset_with_quantile_stats(

    logging.info("Dataset does not contain quantile statistics. Computing them now...")

-    new_stats = compute_quantile_stats_for_dataset(dataset, use_sampling=use_sampling)
+    new_stats = compute_quantile_stats_for_dataset(
+        dataset, use_sampling=use_sampling, skip_images=skip_images
+    )
+
+    if skip_images and dataset.meta.stats:
+        for key, feature_stats in dataset.meta.stats.items():
+            new_stats.setdefault(key, feature_stats)

    logging.info("Updating dataset metadata with new quantile statistics")
    dataset.meta.stats = new_stats
@@ -276,10 +288,15 @@ def main():
        "--no-sampling",
        action="store_true",
        help=(
-            "Compute stats over every frame (exact, higher memory). By default, "
+            "Compute stats over every frame (higher memory). By default, "
            "image/video frames are sub-sampled per episode to bound memory."
        ),
    )
+    parser.add_argument(
+        "--skip-images",
+        action="store_true",
+        help="Skip image/video features and preserve their existing stats",
+    )

    args = parser.parse_args()
    root = Path(args.root) if args.root else None
@@ -291,6 +308,7 @@ def main():
        root=root,
        overwrite=args.overwrite,
        use_sampling=not args.no_sampling,
+        skip_images=args.skip_images,
    )


@@ -0,0 +1,174 @@
+#!/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.
+"""Convert a DCP-format checkpoint into a distributable safetensors model, offline.
+
+Runs single-process (no GPUs, no process group). Example:
+
+```bash
+lerobot-convert-dcp --checkpoint_dir=outputs/train/run/checkpoints/005000
+lerobot-convert-dcp --checkpoint_dir=... --delete_dcp=true --push_to_hub=user/my-policy
+```
+
+`--push_to_hub` publishes the converted directory as a model repo, degrading gracefully: the
+core artifacts (model.safetensors, config.json, processor files) always upload; the README
+model card is enriched with training/dataset metadata only when `train_config.json` (and the
+dataset it names) are reachable, with a WARNING naming exactly what was skipped otherwise.
+DCP shard artifacts are never uploaded — published repos carry safetensors only.
+"""
+
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+from huggingface_hub import HfApi
+
+from lerobot.configs import parser
+from lerobot.distributed.checkpoint import dcp_to_safetensors
+from lerobot.utils.constants import PRETRAINED_MODEL_DIR
+from lerobot.utils.utils import init_logging
+
+
+@dataclass
+class ConvertDcpConfig:
+    """CLI config for the offline DCP-to-safetensors checkpoint conversion."""
+
+    # A checkpoint step directory (containing pretrained_model/) or a pretrained_model
+    # directory itself.
+    checkpoint_dir: Path
+    # Remove the DCP shard directory after a successful conversion.
+    delete_dcp: bool = False
+    # Publish the converted directory to this Hub repo id (e.g. "user/my-policy").
+    push_to_hub: str | None = None
+    private: bool | None = None
+
+
+def _locate_pretrained_dir(checkpoint_dir: Path) -> Path:
+    """Resolve the pretrained_model/ directory from a user-supplied checkpoint path.
+
+    Args:
+        checkpoint_dir (Path): A checkpoint step directory (containing `pretrained_model/`) or a
+            `pretrained_model` directory itself.
+
+    Returns:
+        Path: The nested `pretrained_model/` directory when present, otherwise `checkpoint_dir`
+        unchanged.
+    """
+    nested = checkpoint_dir / PRETRAINED_MODEL_DIR
+    return nested if nested.is_dir() else checkpoint_dir
+
+
+def _publish_converted(pretrained_dir: Path, repo_id: str, private: bool | None) -> None:
+    """Best-effort publish of a converted checkpoint dir, degrading gracefully.
+
+    The core artifacts (model.safetensors, config.json, processor files) always upload; the README
+    model card gains training/dataset metadata only when `train_config.json` (and the dataset it
+    names) are reachable, with a WARNING naming what was skipped otherwise. DCP shard artifacts are
+    excluded from the upload.
+
+    Args:
+        pretrained_dir (Path): The converted `pretrained_model/` directory to upload.
+        repo_id (str): Target Hub model repo id (e.g. "user/my-policy"); created if missing.
+        private (bool | None): Repo visibility passed to `create_repo`; None keeps the Hub (or
+            existing repo's) default.
+    """
+    from lerobot.common.train_utils import generate_model_card
+    from lerobot.configs.policies import PreTrainedConfig
+    from lerobot.configs.train import TRAIN_CONFIG_NAME, TrainPipelineConfig
+
+    train_cfg = None
+    dataset_meta = None
+    if (pretrained_dir / TRAIN_CONFIG_NAME).is_file():
+        try:
+            train_cfg = TrainPipelineConfig.from_pretrained(pretrained_dir)
+        except Exception as e:  # noqa: BLE001 — degrade, never block the upload
+            logging.warning(f"Could not parse {TRAIN_CONFIG_NAME} ({e}); README will lack training metadata.")
+    else:
+        logging.warning(f"{TRAIN_CONFIG_NAME} missing; README will lack training metadata.")
+    if train_cfg is not None:
+        try:
+            from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+
+            dataset_meta = LeRobotDatasetMetadata(
+                repo_id=train_cfg.dataset.repo_id,
+                root=train_cfg.dataset.root,
+                revision=train_cfg.dataset.revision,
+            )
+        except Exception as e:  # noqa: BLE001
+            logging.warning(
+                f"Dataset '{train_cfg.dataset.repo_id}' unreachable ({e}); README will lack dataset metadata."
+            )
+    try:
+        model_cfg = PreTrainedConfig.from_pretrained(pretrained_dir)
+        card = generate_model_card(model_cfg, cfg=train_cfg, dataset_meta=dataset_meta)
+        card.save(str(pretrained_dir / "README.md"))
+    except Exception as e:  # noqa: BLE001
+        logging.warning(f"Could not build the model card ({e}); publishing without README.")
+
+    api = HfApi()
+    repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
+    commit_info = api.upload_folder(
+        repo_id=repo_id,
+        repo_type="model",
+        folder_path=str(pretrained_dir),
+        commit_message="Upload converted policy (DCP -> safetensors)",
+        allow_patterns=["*.safetensors", "*.json", "*.yaml", "*.md"],
+        # The checkpoint keeps its DCP shard directory unless --delete_dcp was passed; the
+        # allow list above admits neither `.distcp` shards nor their `.metadata` sidecar.
+        ignore_patterns=["*.tmp", "*.log"],
+    )
+    logging.info(f"Model pushed to {commit_info.repo_url.url}")
+
+
+@parser.wrap()
+def convert_checkpoint(cfg: ConvertDcpConfig) -> Path:
+    """Merge a checkpoint's DCP shards into `model.safetensors`, then optionally publish it.
+
+    Args:
+        cfg (ConvertDcpConfig): Conversion options — the checkpoint directory to convert, whether
+            to delete the DCP shards after a successful merge, and the optional Hub repo id (and
+            visibility) to publish the converted directory to.
+
+    Returns:
+        Path: The path to the merged `model.safetensors` file.
+
+    Raises:
+        FileNotFoundError: If the checkpoint has no DCP shard directory, i.e. it was not saved
+            with `checkpoint_format=dcp` (or `safetensors_dcp`).
+    """
+    from accelerate.utils.constants import FSDP_MODEL_NAME
+
+    pretrained_dir = _locate_pretrained_dir(cfg.checkpoint_dir)
+    dcp_dir = pretrained_dir / f"{FSDP_MODEL_NAME}_0"
+    if not dcp_dir.is_dir():
+        raise FileNotFoundError(
+            f"No DCP shard directory at {dcp_dir}. Point --checkpoint_dir at a checkpoint "
+            "saved with checkpoint_format=dcp (or safetensors_dcp)."
+        )
+    logging.info(f"Merging {dcp_dir} -> {pretrained_dir / 'model.safetensors'}")
+    safetensors_path = dcp_to_safetensors(dcp_dir, pretrained_dir, delete_dcp=cfg.delete_dcp)
+    if cfg.push_to_hub:
+        _publish_converted(pretrained_dir, cfg.push_to_hub, cfg.private)
+    return safetensors_path
+
+
+def main() -> None:
+    """`lerobot-convert-dcp` console entry point: set up logging and run the conversion."""
+    init_logging()
+    convert_checkpoint()
+
+
+if __name__ == "__main__":
+    main()
@@ -22,7 +22,7 @@ from torch.utils.data._utils.collate import default_collate

 from lerobot.datasets.language import LANGUAGE_COLUMNS

-_PYTHON_LIST_KEYS = {"messages", "message_streams", "target_message_indices"}
+_PYTHON_LIST_KEYS = {"messages", "message_streams", "target_message_indices", *LANGUAGE_COLUMNS}


 def lerobot_collate_fn(batch: list[dict[str, Any] | None]) -> dict[str, Any] | None:
@@ -26,6 +26,7 @@ OBS_IMAGES = OBS_IMAGE + "s"
 OBS_LANGUAGE = OBS_STR + ".language"
 OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens"
 OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask"
+OBS_LANGUAGE_CAUSAL_MARKS = OBS_LANGUAGE + ".causal_marks"
 OBS_LANGUAGE_SUBTASK = OBS_STR + ".subtask"
 OBS_LANGUAGE_SUBTASK_TOKENS = OBS_LANGUAGE_SUBTASK + ".tokens"
 OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK + ".attention_mask"
@@ -34,6 +35,7 @@ ACTION = "action"
 ACTION_PREFIX = ACTION + "."
 ACTION_TOKENS = ACTION + ".tokens"
 ACTION_TOKEN_MASK = ACTION + ".token_mask"
+ACTION_CODE_TOKEN_MASK = ACTION + ".code_token_mask"
 REWARD = "next.reward"
 TRUNCATED = "next.truncated"
 DONE = "next.done"
@@ -0,0 +1,205 @@
+# 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.
+
+"""Doctest plumbing so the examples in our docstrings actually run.
+
+Adapted from `transformers.testing_utils`. Two stdlib limitations make this necessary:
+
+1. Ruff is configured with `docstring-code-format = true`, which reformats code inside docstrings and
+   removes the blank line before the closing fence. stdlib's `_EXAMPLE_RE` then swallows the ` ``` ` into
+   the expected-output group, so every example that has output fails. [`LeRobotDocTestParser`] patches the
+   regex to stop at a fence.
+2. `doctest.DocTestFinder` reports the wrong line number for `@property` and `functools.wraps` objects
+   (https://bugs.python.org/issue17446). Our hardware API is property-heavy — `observation_features`,
+   `action_features`, `is_connected`, `is_calibrated` are all abstract properties — so
+   [`LeRobotDoctestModule`] unwraps them before locating the example.
+
+Two environment variables skip whole example blocks by content:
+
+- `SKIP_CUDA_DOCTEST=1` skips examples that need a GPU.
+- `SKIP_HARDWARE_DOCTEST=1` skips examples that need a physical robot or a Hub download.
+
+Both are heuristics over the example source. They are deliberately blunt: an example that is skipped
+needlessly costs nothing, whereas one that runs on a machine without the hardware hangs or fails.
+"""
+
+import doctest
+import functools
+import inspect
+import os
+import re
+import sys
+from collections.abc import Iterable
+
+from _pytest.doctest import (
+    DoctestItem,
+    DoctestModule,
+    _get_checker,
+    _get_continue_on_failure,
+    _get_runner,
+    get_optionflags,
+)
+from _pytest.nodes import Collector
+from _pytest.outcomes import skip
+
+# Calls whose progress bars would otherwise be compared against the expected output. The lookahead leaves
+# lines that already carry a directive alone.
+_NOISY_CALL_PATTERN = re.compile(r"(>>> (?!.*# doctest:).*(?:load_dataset|LeRobotDataset)\(.*)")
+
+_CUDA_PATTERN = re.compile(r"cuda|to\(0\)|device=0")
+
+# Serial ports, video devices, and the connect/scan calls that talk to real hardware.
+_HARDWARE_PATTERN = re.compile(r"/dev/tty|/dev/video|COM\d|\.connect\(|find_cameras\(|find_port\(")
+
+# Anything that reaches the Hub over the network.
+_HUB_PATTERN = re.compile(r"from_pretrained\(|push_to_hub\(|snapshot_download\(|load_dataset\(")
+
+
+def preprocess_string(string: str, skip_cuda_tests: bool, skip_hardware_tests: bool) -> str:
+    """Prepare a docstring or `.mdx` file to be run by doctest.
+
+    Args:
+        string (`str`):
+            A whole file's contents for `.mdx`, or a single docstring for a Python file. Either may hold
+            several fenced examples.
+        skip_cuda_tests (`bool`):
+            Whether to drop examples that look like they need a GPU.
+        skip_hardware_tests (`bool`):
+            Whether to drop examples that look like they need a robot or a Hub download.
+
+    Returns:
+        `str`: The input with `# doctest: +IGNORE_RESULT` injected on noisy calls, or an empty string if
+        the examples were skipped — in which case no doctest is collected for it at all.
+    """
+    # Match against the example lines only, not the surrounding prose, so that a docstring merely
+    # *describing* CUDA or a serial port is not mistaken for one that uses them.
+    example_lines = "\n".join(
+        line for line in string.splitlines() if line.lstrip().startswith((">>>", "..."))
+    )
+    if not example_lines:
+        return string
+
+    if skip_cuda_tests and _CUDA_PATTERN.search(example_lines):
+        return ""
+    if skip_hardware_tests and (
+        _HARDWARE_PATTERN.search(example_lines) or _HUB_PATTERN.search(example_lines)
+    ):
+        return ""
+
+    return _NOISY_CALL_PATTERN.sub(r"\1 # doctest: +IGNORE_RESULT", string)
+
+
+class LeRobotDocTestParser(doctest.DocTestParser):
+    """A `DocTestParser` that understands fenced, auto-formatted code blocks.
+
+    Ruff's `docstring-code-format` removes the blank line before a closing fence, after which stdlib's
+    `_EXAMPLE_RE` reads the fence itself as part of the expected output and every example with output
+    fails. The regex below is the stdlib one plus a clause that stops matching at a fence.
+    """
+
+    # fmt: off
+    _EXAMPLE_RE = re.compile(r'''
+        # Source consists of a PS1 line followed by zero or more PS2 lines.
+        (?P<source>
+            (?:^(?P<indent> [ ]*) >>>    .*)    # PS1 line
+            (?:\n           [ ]*  \.\.\. .*)*)  # PS2 lines
+        \n?
+        # Want consists of any non-blank lines that do not start with PS1.
+        (?P<want> (?:(?![ ]*$)    # Not a blank line
+             (?![ ]*>>>)          # Not a line starting with PS1
+             (?:(?!```).)*        # Stop at a closing fence: formatting drops the blank line before it
+             (?:\n|$)  # Match a new line or end of string
+          )*)
+        ''', re.MULTILINE | re.VERBOSE
+    )
+    # fmt: on
+
+    skip_cuda_tests: bool = os.environ.get("SKIP_CUDA_DOCTEST", "0") == "1"
+    skip_hardware_tests: bool = os.environ.get("SKIP_HARDWARE_DOCTEST", "0") == "1"
+
+    def parse(self, string, name="<string>"):
+        """Preprocess `string`, then parse it as stdlib would.
+
+        Args:
+            string (`str`):
+                The docstring or file contents to parse.
+            name (`str`, *optional*, defaults to `"<string>"`):
+                Name used in failure messages.
+
+        Returns:
+            `list`: The examples and interleaved text, as returned by `doctest.DocTestParser.parse`.
+        """
+        string = preprocess_string(string, self.skip_cuda_tests, self.skip_hardware_tests)
+        return super().parse(string, name)
+
+
+class LeRobotDoctestModule(DoctestModule):
+    """A pytest `DoctestModule` that collects with [`LeRobotDocTestParser`].
+
+    `doctest.DocTestFinder` binds its default parser at class-definition time, so patching
+    `doctest.DocTestParser` in `conftest.py` does not reach the finder pytest builds. The parser has to be
+    passed in explicitly, which means reimplementing `collect`. It mirrors pytest's own implementation.
+    """
+
+    def collect(self) -> Iterable[DoctestItem]:
+        """Collect the doctests in this module.
+
+        Returns:
+            `Iterable[DoctestItem]`: One item per example-bearing docstring. Docstrings whose examples were
+            dropped by `preprocess_string` yield nothing.
+        """
+
+        class MockAwareDocTestFinder(doctest.DocTestFinder):
+            """A doctest finder that reports correct line numbers for properties and wrapped callables."""
+
+            # Fixed upstream in CPython 3.11.9 / 3.12.3; kept for older interpreters. Our hardware API is
+            # property-heavy (`observation_features`, `is_connected`, ...), so a wrong line number here
+            # would point every failure at the decorator. https://github.com/python/cpython/issues/61648
+            def _find_lineno(self, obj, source_lines):
+                if isinstance(obj, property):
+                    obj = getattr(obj, "fget", obj)
+                if hasattr(obj, "__wrapped__"):
+                    obj = inspect.unwrap(obj)
+                return super()._find_lineno(obj, source_lines)
+
+            if sys.version_info < (3, 13):
+                # `cached_property` is otherwise never considered part of the current module and its
+                # examples are silently skipped. https://github.com/python/cpython/issues/107995
+                def _from_module(self, module, object):
+                    if isinstance(object, functools.cached_property):
+                        object = object.func
+                    return super()._from_module(module, object)
+
+        try:
+            module = self.obj
+        except Collector.CollectError:
+            if self.config.getvalue("doctest_ignore_import_errors"):
+                skip(f"unable to import module {self.path!r}")
+            else:
+                raise
+
+        # Doctests support fixtures via `getfixture` and autouse.
+        self.session._fixturemanager.parsefactories(self)
+
+        finder = MockAwareDocTestFinder(parser=LeRobotDocTestParser())
+        optionflags = get_optionflags(self.config)
+        runner = _get_runner(
+            verbose=False,
+            optionflags=optionflags,
+            checker=_get_checker(),
+            continue_on_failure=_get_continue_on_failure(self.config),
+        )
+        for test in finder.find(module, module.__name__):
+            if test.examples:  # Skip docstrings with no examples, and blocks dropped by the parser.
+                yield DoctestItem.from_parent(self, name=test.name, runner=runner, dtest=test)
@@ -25,6 +25,9 @@ from .constants import CHECKPOINTS_DIR
 T = TypeVar("T", bound="HubMixin")


+# Sharded-training resume artifacts (torch DCP shard dirs + shard files). Published model repos
+# carry safetensors only, so publishing uploads exclude these — checkpoint pushes (which exist
+# for resume, not distribution) deliberately do not.
 def find_latest_hub_checkpoint(
    repo_id: str,
    *,
@@ -36,6 +39,16 @@ def find_latest_hub_checkpoint(
    Training runs push checkpoints to ``checkpoints/<step>/`` (see
    ``push_checkpoint_to_hub``). This lists those step dirs and returns
    ``checkpoints/<highest-step>``, or ``None`` if the repo has no checkpoints.
+
+    Args:
+        repo_id (str): The Hub model repo to inspect.
+        token (str | bool | None): Hub authentication token. Defaults to None (the token
+            cached by `huggingface-cli login`).
+        revision (str | None): Repo revision to list. Defaults to None (the default branch).
+
+    Returns:
+        str | None: The repo-relative path `checkpoints/<highest-step>`, or None if the repo
+            has no checkpoints.
    """
    files = HfApi().list_repo_files(repo_id=repo_id, repo_type="model", revision=revision, token=token)
    prefix = f"{CHECKPOINTS_DIR}/"
@@ -164,7 +177,7 @@ class HubMixin:
        ignore_patterns: list[str] | str | None = None,
        delete_patterns: list[str] | str | None = None,
        card_kwargs: dict[str, Any] | None = None,
-    ) -> str:
+    ) -> str | None:
        """
        Upload model checkpoint to the Hub.

@@ -172,6 +185,10 @@ class HubMixin:
        `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
        details.

+        Distributed contract: call on EVERY rank. `save_pretrained` runs on all ranks — for
+        sharded objects it can contain a collective gather (rank-gating it would deadlock) —
+        while repo creation and the upload happen on the main process only.
+
        Args:
            repo_id (`str`):
                ID of the repository to push to (example: `"username/my-model"`).
@@ -197,11 +214,17 @@ class HubMixin:
                Additional arguments passed to the card template to customize the card.

        Returns:
-            The url of the commit of your object in the given repository.
+            `str` or `None`: The url of the commit of your object in the given repository, or
+            `None` on non-main ranks of a distributed run (only the main process uploads).
        """
-        api = HfApi(token=token)
-        repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
+        # Lazy import: hub code must not import the distributed package at module load
+        # (configs -> hub is on the import path of lerobot.distributed itself).
+        from lerobot.distributed.utils import is_main_process

+        # Distributed contract: `save_pretrained` runs on EVERY rank — for sharded policies it
+        # contains a collective gather (rank-gating it would deadlock) and it writes into this
+        # rank's private tmpdir only on the main process. Repo creation and upload are then
+        # main-process-only.
        if commit_message is None:
            if "Policy" in self.__class__.__name__:
                commit_message = "Upload policy"
@@ -210,10 +233,13 @@ class HubMixin:
            else:
                commit_message = f"Upload {self.__class__.__name__}"

-        # Push the files to the repo in a single commit
        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
            saved_path = Path(tmp) / repo_id
            self.save_pretrained(saved_path, card_kwargs=card_kwargs)
+            if not is_main_process():
+                return None
+            api = HfApi(token=token)
+            repo_id = api.create_repo(repo_id=repo_id, private=private, exist_ok=True).repo_id
            return api.upload_folder(
                repo_id=repo_id,
                repo_type="model",
@@ -14,10 +14,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from collections import defaultdict
-from collections.abc import Callable
 from typing import Any

 import torch
+import torch.distributed as dist

 from .utils import format_big_number

@@ -69,12 +69,31 @@ class MetricsTracker:
    """
    A helper class to track and log metrics over time.

+    Args:
+        batch_size (int): Per-process batch size (samples per micro-batch on each
+            data-parallel worker).
+        num_frames (int): Total number of frames in the training dataset.
+        num_episodes (int): Total number of episodes in the training dataset.
+        metrics (dict[str, AverageMeter]): The meters to track, keyed by metric name.
+        initial_step (int): Step counter to start from (non-zero when resuming a run).
+            Defaults to 0.
+        dp_world_size (int): Number of distinct data-parallel workers
+            (`dp_replicate * dp_shard`), used to scale sample accounting; context-parallel
+            peers consume the same batch and must not be double counted. Defaults to 1.
+
    Usage pattern:

    ```python
    # initialize, potentially with non-zero initial step (e.g. if resuming run)
    metrics = {"loss": AverageMeter("loss", ":.3f")}
-    train_metrics = MetricsTracker(cfg, dataset, metrics, initial_step=step)
+    train_metrics = MetricsTracker(
+        batch_size,
+        dataset.num_frames,
+        dataset.num_episodes,
+        metrics,
+        initial_step=step,
+        dp_world_size=dp_world,
+    )

    # update metrics derived from step (samples, episodes, epochs) at each training step
    train_metrics.step()
@@ -98,12 +117,12 @@ class MetricsTracker:
        "_batch_size",
        "_num_frames",
        "_avg_samples_per_ep",
+        "_dp_world_size",
        "metrics",
        "steps",
        "samples",
        "episodes",
        "epochs",
-        "accelerator",
        "_caller_metrics",
    ]

@@ -114,22 +133,25 @@ class MetricsTracker:
        num_episodes: int,
        metrics: dict[str, AverageMeter],
        initial_step: int = 0,
-        accelerator: Callable | None = None,
+        dp_world_size: int = 1,
    ):
        self.__dict__.update(dict.fromkeys(self.__keys__))
        self._batch_size = batch_size
        self._num_frames = num_frames
        self._avg_samples_per_ep = num_frames / num_episodes
+        # Sample accounting scales by the number of DISTINCT data-parallel workers, which is
+        # dp_replicate * dp_shard — not the world size: context-parallel peers consume the same
+        # batch and must not be double counted. `step` counts micro-batches, so no
+        # grad-accumulation factor belongs here either.
+        self._dp_world_size = dp_world_size
        self.metrics = metrics

        self.steps = initial_step
-        world_size = accelerator.num_processes if accelerator else 1
        # A sample is an (observation,action) pair, where observation and action
        # can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
-        self.samples = self.steps * self._batch_size * world_size
+        self.samples = self.steps * self._batch_size * self._dp_world_size
        self.episodes = self.samples / self._avg_samples_per_ep
        self.epochs = self.samples / self._num_frames
-        self.accelerator = accelerator
        # Meter names the caller registered up front. update_metrics() leaves these untouched, so a
        # policy that echoes e.g. "loss" in its output dict can't clobber the aggregated meter.
        self._caller_metrics: set[str] = set(self.metrics)
@@ -155,8 +177,7 @@ class MetricsTracker:
        Updates metrics that depend on 'step' for one step.
        """
        self.steps += 1
-        world_size = self.accelerator.num_processes if self.accelerator else 1
-        self.samples += self._batch_size * world_size
+        self.samples += self._batch_size * self._dp_world_size
        self.episodes = self.samples / self._avg_samples_per_ep
        self.epochs = self.samples / self._num_frames

@@ -181,11 +202,16 @@ class MetricsTracker:
        across all distributed processes (in-place).

        This is a collective operation and MUST be invoked on every rank — typically just before
-        logging. With no accelerator or in single-process runs it is a no-op. Without it, metrics
-        reported by the main process only reflect rank 0; for bottleneck-style timings
-        (``dataloading_s``, ``update_s``, ...) that means the slowest worker's stall is invisible.
+        logging. Outside distributed runs it is a no-op. Without it, metrics reported by the
+        main process only reflect rank 0; for bottleneck-style timings (``dataloading_s``,
+        ``update_s``, ...) that means the slowest worker's stall is invisible.
+
+        Torch-native on purpose: metrics code carries no Accelerator dependency.
+        Note the reduction spans the WORLD group — correct for count-free averages (loss values
+        are identical within a context-parallel group, so including CP peers is a weighted
+        no-op).
        """
-        if self.accelerator is None or self.accelerator.num_processes <= 1:
+        if not dist.is_initialized() or dist.get_world_size() <= 1:
            return

        buckets: dict[str, list[str]] = defaultdict(list)
@@ -195,11 +221,20 @@ class MetricsTracker:
        if not buckets:
            return

-        device = self.accelerator.device
+        device = (
+            torch.device("cuda", torch.cuda.current_device())
+            if torch.cuda.is_available()
+            else torch.device("cpu")
+        )
+        reduce_ops = {
+            "mean": dist.ReduceOp.AVG,
+            "sum": dist.ReduceOp.SUM,
+            "max": dist.ReduceOp.MAX,
+        }
        for reduction, names in buckets.items():
            tensor = torch.tensor([self.metrics[n].avg for n in names], dtype=torch.float32, device=device)
-            reduced = self.accelerator.reduce(tensor, reduction=reduction)
-            for name, value in zip(names, reduced.tolist(), strict=True):
+            dist.all_reduce(tensor, op=reduce_ops[reduction])
+            for name, value in zip(names, tensor.tolist(), strict=True):
                meter = self.metrics[name]
                # Preserve avg == sum / count so a later .update() on this meter accumulates
                # against the cluster view, not the stale per-rank history.
@@ -0,0 +1,221 @@
+#!/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.
+"""Legacy-checkpoint contracts.
+
+Two contracts are pinned here so they are documented behavior, not accidents:
+
+- **The v0.6.0 hard break.** The v0.6.0 #3810 FSDP checkpoint layout
+  (full gathered ``model.safetensors`` + full ``optimizer_state.safetensors``, no DCP dirs,
+  no ``checkpoint_format`` in ``train_config.json``) is a hard break with ZERO v0.6.0-aware
+  runtime code — not even layout detection. A sharded resume pointed at such a checkpoint
+  must fail through the ORDINARY missing-artifact path (torch DCP erroring on the absent
+  ``training_state/optimizer_0/``), while the model weights remain loadable forever via
+  ``from_pretrained`` and the old ``num_processes`` key keeps feeding the topology reader.
+- **Converter equivalence.** ``dcp_to_safetensors`` (real ``merge_fsdp_weights``, no mocks)
+  on accelerate's ``save_fsdp_model`` DCP layout reproduces exactly the tensors that the
+  direct-gather ``save_pretrained`` artifact contains.
+"""
+
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+pytest.importorskip("accelerate", reason="accelerate is required (install lerobot[training])")
+
+import torch
+import torch.distributed.checkpoint as dist_cp
+from accelerate.utils.constants import FSDP_MODEL_NAME, OPTIMIZER_NAME
+from safetensors.torch import load_file
+from torch.distributed.checkpoint.api import CheckpointException
+from torch.distributed.fsdp import FSDPModule
+
+from lerobot.common.train_utils import (
+    load_training_metadata,
+    resume_after_prepare,
+    resume_before_prepare,
+)
+from lerobot.configs.accelerator import FSDPConfig
+from lerobot.configs.default import DatasetConfig
+from lerobot.configs.train import TRAIN_CONFIG_NAME, CheckpointFormat, TrainPipelineConfig
+from lerobot.distributed.checkpoint import dcp_to_safetensors, is_sharded_module
+from lerobot.optim.optimizers import save_optimizer_state
+from lerobot.utils.constants import PRETRAINED_MODEL_DIR, TRAINING_STATE_DIR, TRAINING_STEP
+from lerobot.utils.io_utils import write_json
+from lerobot.utils.random_utils import save_rng_state
+from tests.fixtures.dummy_checkpoint_policy import DummyCheckpointPolicy, make_dummy_policy
+
+
+@pytest.fixture
+def accelerate_state():
+    """accelerate's process state, as the trainer's `Accelerator()` would have initialized it.
+
+    `load_fsdp_optimizer` and `merge_fsdp_weights` both consult `PartialState` internals
+    (logging and main-process gating). Single-process CPU state; reset on teardown so no
+    global accelerate state leaks into other tests.
+    """
+    from accelerate.state import AcceleratorState, PartialState
+
+    PartialState()
+    yield
+    AcceleratorState._reset_state(reset_partial_state=True)
+
+
+def make_v060_fsdp_checkpoint(checkpoint_dir: Path) -> dict[str, torch.Tensor]:
+    """Reproduce the v0.6.0 #3810 FSDP checkpoint layout with real artifacts.
+
+    - ``pretrained_model/``: ``config.json`` + full gathered ``model.safetensors`` (real
+      ``save_pretrained`` outputs) and a ``train_config.json`` predating the v0.7 fields
+      (``checkpoint_format``/``parallelism``/``accelerator`` stripped from the draccus dump);
+    - ``training_state/``: old-style ``training_step.json`` (``{"step", "num_processes"}``,
+      no ``dp_world_size``), ``rng_state.safetensors``, and the gathered full optimizer
+      channel (``optimizer_state.safetensors`` + ``optimizer_param_groups.json``) — and,
+      crucially, NO ``optimizer_0/`` DCP directory.
+
+    Returns the saved model weights for later comparison.
+    """
+    policy = make_dummy_policy()
+    optimizer = torch.optim.Adam(policy.parameters())
+    policy.forward({"observation.state": torch.randn(2, 4)})[0].backward()
+    optimizer.step()  # real optimizer state, applied before the weights are saved
+
+    pretrained_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
+    policy.save_pretrained(pretrained_dir)
+    cfg = TrainPipelineConfig(dataset=DatasetConfig(repo_id="lerobot/dummy"), batch_size=3)
+    cfg._save_pretrained(pretrained_dir)
+    config_path = pretrained_dir / TRAIN_CONFIG_NAME
+    raw = json.loads(config_path.read_text())
+    assert "checkpoint_format" in raw  # draccus dumps defaults; a v0.6.0 config predates the key
+    for key in ("checkpoint_format", "parallelism", "accelerator"):
+        raw.pop(key, None)
+    config_path.write_text(json.dumps(raw, indent=4))
+
+    training_state_dir = checkpoint_dir / TRAINING_STATE_DIR
+    training_state_dir.mkdir()
+    write_json({"step": 5000, "num_processes": 4}, training_state_dir / TRAINING_STEP)
+    save_rng_state(training_state_dir)
+    save_optimizer_state(optimizer, training_state_dir)
+    return {key: tensor.clone() for key, tensor in policy.state_dict().items()}
+
+
+def as_fsdp2_module(policy: DummyCheckpointPolicy) -> DummyCheckpointPolicy:
+    """Give the policy FSDP2's runtime identity via the in-place class swap `fully_shard` performs.
+
+    torch's `fully_shard` swaps ``module.__class__`` to a ``(FSDPModule, type(module))``
+    subclass; mirroring that swap is what makes `is_sharded_module` (and thus the sharded
+    branch of `resume_after_prepare`) see a sharded model on a CPU-only single process. The
+    parameters stay plain tensors — sufficient here, because the resume must fail at the DCP
+    read before any sharded state is touched.
+    """
+    policy.__class__ = type(f"FSDP{type(policy).__name__}", (FSDPModule, type(policy)), {})
+    assert is_sharded_module(policy)
+    return policy
+
+
+def sharded_passthrough_accelerator() -> SimpleNamespace:
+    """The accelerator surface the sharded resume touches, carrying the trainer's real plugin.
+
+    `FSDPConfig.build_plugin()` is the exact FSDP2 plugin construction `make_accelerator`
+    hands to accelerate (state_dict_type stays at the FSDP2 default, SHARDED_STATE_DICT).
+    """
+    return SimpleNamespace(
+        unwrap_model=lambda m: m,
+        wait_for_everyone=lambda: None,
+        state=SimpleNamespace(fsdp_plugin=FSDPConfig().build_plugin()),
+    )
+
+
+class TestV060HardBreak:
+    """Pin the v0.6.0 hard break as a contract.
+
+    Zero v0.6.0-aware code ships — not even layout detection — so every assertion here must
+    hold through ORDINARY code paths only: the recorded config parses with plain defaults,
+    phase-1 resume and the weights stay loadable, and the sharded phase-2 resume fails with
+    torch DCP's own missing-artifact error, never a bespoke v0.6.0 message.
+    """
+
+    def test_sharded_resume_fails_with_ordinary_missing_artifact_error(self, tmp_path, accelerate_state):
+        make_v060_fsdp_checkpoint(tmp_path)
+
+        # No checkpoint_format recorded -> plain draccus default, no layout detection anywhere.
+        cfg = TrainPipelineConfig.from_pretrained(tmp_path / PRETRAINED_MODEL_DIR / TRAIN_CONFIG_NAME)
+        assert cfg.checkpoint_format is CheckpointFormat.SAFETENSORS
+        cfg.checkpoint_path = tmp_path
+
+        # Phase 1 (RNG + step counter) is format-independent and still succeeds.
+        assert resume_before_prepare(cfg) == 5000
+
+        # Phase 2 under sharding: the recorded format skips the DCP model preflight (the
+        # weights were already loaded by from_pretrained), then the sharded optimizer load
+        # hits the absent optimizer_0/ and fails inside torch DCP — the ordinary error path.
+        assert not (tmp_path / TRAINING_STATE_DIR / f"{OPTIMIZER_NAME}_0").exists()
+        policy = as_fsdp2_module(make_dummy_policy())
+        optimizer = torch.optim.Adam(policy.parameters())
+        with pytest.raises(CheckpointException) as excinfo:
+            resume_after_prepare(cfg, sharded_passthrough_accelerator(), policy, optimizer, None)
+        message = str(excinfo.value)
+        assert "lerobot-convert-dcp" not in message  # the converter hint belongs to recorded-format=DCP
+        assert "v0.6" not in message  # no bespoke wording: the explanation lives in the migration docs
+
+    def test_weights_remain_loadable_via_from_pretrained(self, tmp_path):
+        saved_weights = make_v060_fsdp_checkpoint(tmp_path)
+        policy = DummyCheckpointPolicy.from_pretrained(tmp_path / PRETRAINED_MODEL_DIR)
+        for key, tensor in policy.state_dict().items():
+            assert torch.equal(tensor, saved_weights[key]), key
+
+    def test_topology_reader_falls_back_to_legacy_num_processes(self, tmp_path):
+        make_v060_fsdp_checkpoint(tmp_path)
+        assert load_training_metadata(tmp_path / TRAINING_STATE_DIR)["dp_world_size"] == 4
+
+
+class TestConverterEquivalence:
+    def test_dcp_to_safetensors_output_equals_direct_gather(self, tmp_path, accelerate_state):
+        """DCP -> safetensors conversion is exactly the direct-gather artifact.
+
+        The DCP checkpoint is written with torch's real `dist_cp.save` (single process, no
+        process group), replicating accelerate's `save_fsdp_model` SHARDED_STATE_DICT branch
+        byte for byte: the ``{"model": state_dict}`` nesting and the ``pytorch_model_fsdp_0``
+        directory name. The conversion runs the real `merge_fsdp_weights` — no mocks.
+        """
+        policy = make_dummy_policy()
+        with torch.no_grad():
+            for param in policy.parameters():
+                param.add_(torch.randn_like(param))  # make every tensor distinct from init
+        reference = {key: tensor.clone() for key, tensor in policy.state_dict().items()}
+
+        # The direct-gather artifact (on a single process the gather is state_dict itself).
+        direct_dir = tmp_path / "direct"
+        policy.save_pretrained(direct_dir)
+
+        # The DCP artifact, laid out exactly as accelerate's save_fsdp_model writes it.
+        pretrained_dir = tmp_path / "checkpoint" / PRETRAINED_MODEL_DIR
+        dcp_dir = pretrained_dir / f"{FSDP_MODEL_NAME}_0"
+        dcp_dir.mkdir(parents=True)
+        dist_cp.save(
+            state_dict={"model": policy.state_dict()},
+            storage_writer=dist_cp.FileSystemWriter(str(dcp_dir)),
+        )
+
+        merged_file = dcp_to_safetensors(dcp_dir, pretrained_dir)
+        assert merged_file == pretrained_dir / "model.safetensors"
+        merged = load_file(merged_file)
+        direct = load_file(direct_dir / "model.safetensors")
+        assert set(merged) == set(direct) == set(reference)
+        for key, tensor in reference.items():
+            assert torch.equal(merged[key], tensor), key
+            assert torch.equal(direct[key], tensor), key
+            assert merged[key].dtype == tensor.dtype, key
@@ -0,0 +1,186 @@
+#!/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.
+"""Checkpoint save/resume round-trips on the non-sharded paths."""
+
+from types import SimpleNamespace
+
+import pytest
+import torch
+from safetensors.torch import load_file
+
+from lerobot.common.train_utils import (
+    load_training_metadata,
+    resume_after_prepare,
+    resume_before_prepare,
+    save_checkpoint,
+)
+from lerobot.configs.default import DatasetConfig
+from lerobot.configs.train import CheckpointFormat, TrainPipelineConfig
+from lerobot.utils.constants import PRETRAINED_MODEL_DIR, TRAINING_STATE_DIR, TRAINING_STEP
+from lerobot.utils.io_utils import load_json, write_json
+from tests.fixtures.dummy_checkpoint_policy import make_dummy_policy
+
+
+def make_cfg(**overrides) -> TrainPipelineConfig:
+    cfg = TrainPipelineConfig(dataset=DatasetConfig(repo_id="lerobot/dummy"), batch_size=3)
+    cfg.parallelism.resolve(1)
+    for name, value in overrides.items():
+        setattr(cfg, name, value)
+    return cfg
+
+
+def passthrough_accelerator() -> SimpleNamespace:
+    """The accelerator surface save/resume touches on non-sharded runs."""
+    return SimpleNamespace(unwrap_model=lambda m: m, wait_for_everyone=lambda: None)
+
+
+class TestSaveCheckpoint:
+    def test_non_sharded_layout(self, tmp_path):
+        policy = make_dummy_policy()
+        optimizer = torch.optim.Adam(policy.parameters())
+        save_checkpoint(
+            tmp_path,
+            step=7,
+            cfg=make_cfg(),
+            policy=policy,
+            optimizer=optimizer,
+            accelerator=passthrough_accelerator(),
+        )
+        pretrained = tmp_path / PRETRAINED_MODEL_DIR
+        state = tmp_path / TRAINING_STATE_DIR
+        assert (pretrained / "model.safetensors").is_file()
+        assert (pretrained / "config.json").is_file()
+        assert (pretrained / "train_config.json").is_file()
+        assert (state / TRAINING_STEP).is_file()
+        assert (state / "rng_state.safetensors").is_file()
+        assert (state / "optimizer_state.safetensors").is_file()
+        # single-file artifact, no index, weights intact
+        weights = load_file(pretrained / "model.safetensors")
+        assert torch.allclose(weights["net.weight"], torch.full_like(weights["net.weight"], 0.5))
+        assert not list(pretrained.glob("*.index.json"))
+
+    def test_training_step_records_topology(self, tmp_path):
+        cfg = make_cfg()
+        cfg.accelerator.gradient_accumulation.steps = 4
+        policy = make_dummy_policy()
+        save_checkpoint(
+            tmp_path,
+            step=11,
+            cfg=cfg,
+            policy=policy,
+            optimizer=torch.optim.Adam(policy.parameters()),
+            accelerator=passthrough_accelerator(),
+        )
+        metadata = load_training_metadata(tmp_path / TRAINING_STATE_DIR)
+        assert metadata["dp_world_size"] == 1
+        assert metadata["batch_size"] == 3
+        assert metadata["grad_accum_steps"] == 4
+
+    def test_dp_world_size_legacy_fallback(self, tmp_path):
+        """Pre-v0.7 checkpoints recorded num_processes; the reader falls back to it."""
+        state_dir = tmp_path / TRAINING_STATE_DIR
+        state_dir.mkdir(parents=True)
+        write_json({"step": 5, "num_processes": 4}, state_dir / TRAINING_STEP)
+        metadata = load_training_metadata(tmp_path / TRAINING_STATE_DIR)
+        assert metadata["dp_world_size"] == 4
+        assert metadata["batch_size"] is None
+
+
+class TestResume:
+    def _checkpointed_run(self, tmp_path):
+        policy = make_dummy_policy()
+        optimizer = torch.optim.Adam(policy.parameters(), lr=0.123)
+        # give the optimizer real state
+        policy.forward({"observation.state": torch.randn(2, 4)})[0].backward()
+        optimizer.step()
+        cfg = make_cfg()
+        save_checkpoint(
+            tmp_path,
+            step=42,
+            cfg=cfg,
+            policy=policy,
+            optimizer=optimizer,
+            accelerator=passthrough_accelerator(),
+        )
+        cfg.checkpoint_path = tmp_path
+        return cfg, policy, optimizer
+
+    def test_two_phase_resume_round_trip(self, tmp_path):
+        cfg, _, optimizer = self._checkpointed_run(tmp_path)
+        assert resume_before_prepare(cfg) == 42
+
+        fresh_policy = make_dummy_policy()
+        fresh_optimizer = torch.optim.Adam(fresh_policy.parameters(), lr=0.999)
+        resume_after_prepare(cfg, passthrough_accelerator(), fresh_policy, fresh_optimizer, None)
+        restored = fresh_optimizer.state_dict()
+        original = optimizer.state_dict()
+        assert restored["param_groups"][0]["lr"] == original["param_groups"][0]["lr"]
+        for key, tensor in original["state"][0].items():
+            assert torch.equal(restored["state"][0][key], tensor), key
+
+    def test_resume_warns_on_changed_cadence_and_topology(self, tmp_path, caplog):
+        """The recorded grad-accum factor and parallelism snapshot must be compared on
+        resume, with one warning naming the diff."""
+        import logging
+
+        cfg, _, _ = self._checkpointed_run(tmp_path)
+        cfg.accelerator.gradient_accumulation.steps = 4
+        cfg.parallelism.dp_replicate = 2  # same dp_world_size story is irrelevant here
+        with caplog.at_level(logging.WARNING):
+            assert resume_before_prepare(cfg) == 42
+        warning = next(m for m in caplog.messages if "differ from the checkpoint" in m)
+        assert "grad_accum_steps: 1 -> 4" in warning
+        assert "dp_replicate: 1 -> 2" in warning
+
+    def test_resume_unchanged_settings_stay_silent(self, tmp_path, caplog):
+        import logging
+
+        cfg, _, _ = self._checkpointed_run(tmp_path)
+        with caplog.at_level(logging.WARNING):
+            resume_before_prepare(cfg)
+        assert not [m for m in caplog.messages if "differ from the checkpoint" in m]
+
+    def test_resume_rejects_non_sharded_checkpoint_on_sharded_run(self, tmp_path):
+        """Resharding works across sizes, not across kinds: non-sharded -> sharded is rejected."""
+        cfg, _, _ = self._checkpointed_run(tmp_path)
+        cfg.parallelism.dp_shard = 2
+        with pytest.raises(ValueError, match="Cannot resume"):
+            resume_before_prepare(cfg)
+
+    def test_resume_rejects_sharded_checkpoint_on_non_sharded_run(self, tmp_path):
+        """The symmetric direction: a checkpoint recorded sharded cannot resume non-sharded."""
+        cfg, _, _ = self._checkpointed_run(tmp_path)
+        state_file = tmp_path / TRAINING_STATE_DIR / TRAINING_STEP
+        state = load_json(state_file)
+        state["parallelism"]["dp_shard"] = 2
+        write_json(state, state_file)
+        with pytest.raises(ValueError, match="Cannot resume"):
+            resume_before_prepare(cfg)
+
+    def test_resume_before_prepare_requires_training_state(self, tmp_path):
+        cfg = make_cfg()
+        cfg.checkpoint_path = tmp_path
+        with pytest.raises(NotADirectoryError):
+            resume_before_prepare(cfg)
+
+    def test_dcp_format_integrity_preflight(self, tmp_path):
+        """A checkpoint declaring DCP shards without the shard dir fails with the converter hint."""
+        pytest.importorskip("accelerate", reason="accelerate is required (install lerobot[training])")
+        cfg, policy, optimizer = self._checkpointed_run(tmp_path)
+        cfg.parallelism.dp_shard = 2  # pretend the recorded run was sharded
+        cfg.checkpoint_format = CheckpointFormat.DCP
+        with pytest.raises(FileNotFoundError, match="lerobot-convert-dcp"):
+            resume_after_prepare(cfg, passthrough_accelerator(), policy, optimizer, None)
@@ -0,0 +1,148 @@
+#!/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.
+"""publish_trained_model: commit set, card, log-line contract, PEFT branch (hub fully mocked)."""
+
+import logging
+from pathlib import Path
+from types import SimpleNamespace
+
+import pytest
+
+import lerobot.common.train_utils as train_utils
+import lerobot.utils.hub as hub
+from lerobot.common.train_utils import generate_model_card, publish_trained_model
+from lerobot.configs.default import DatasetConfig
+from lerobot.configs.train import TrainPipelineConfig
+from tests.fixtures.dummy_checkpoint_policy import make_dummy_policy
+
+
+class FakeHfApi:
+    """Records every repo/upload interaction; shared across both HfApi import sites."""
+
+    calls: list[dict] = []
+
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def create_repo(self, repo_id, private=None, exist_ok=False, **kwargs):
+        return SimpleNamespace(repo_id=repo_id)
+
+    def upload_folder(self, *, repo_id, folder_path, commit_message, **kwargs):
+        FakeHfApi.calls.append(
+            {
+                "repo_id": repo_id,
+                "commit_message": commit_message,
+                "files": sorted(p.name for p in Path(folder_path).iterdir()),
+                "ignore_patterns": kwargs.get("ignore_patterns"),
+            }
+        )
+        return SimpleNamespace(repo_url=SimpleNamespace(url=f"https://huggingface.co/{repo_id}"))
+
+
+@pytest.fixture
+def mocked_hub(monkeypatch):
+    FakeHfApi.calls = []
+    monkeypatch.setattr(train_utils, "HfApi", FakeHfApi)
+    monkeypatch.setattr(hub, "HfApi", FakeHfApi)
+    # card.validate() hits the Hub; publishing must work offline in tests
+    monkeypatch.setattr(train_utils.ModelCard, "validate", lambda self: None)
+    return FakeHfApi
+
+
+def make_cfg() -> TrainPipelineConfig:
+    cfg = TrainPipelineConfig(dataset=DatasetConfig(repo_id="user/dataset"))
+    cfg.parallelism.resolve(1)
+    return cfg
+
+
+class RecordingProcessor:
+    def __init__(self):
+        self.pushed_to = None
+
+    def push_to_hub(self, repo_id, **kwargs):
+        self.pushed_to = repo_id
+
+
+class TestPublishTrainedModel:
+    def test_commit_set_and_log_contract(self, mocked_hub, caplog):
+        policy = make_dummy_policy(repo_id="user/policy")
+        pre, post = RecordingProcessor(), RecordingProcessor()
+        with caplog.at_level(logging.INFO):
+            publish_trained_model(make_cfg(), policy, pre, post, dataset_meta=None)
+
+        # commit 1: the model through HubMixin (config.json + model.safetensors in a tmpdir)
+        model_commit = mocked_hub.calls[0]
+        assert {"config.json", "model.safetensors"} <= set(model_commit["files"])
+        # commits 2-3: processors
+        assert pre.pushed_to == "user/policy" and post.pushed_to == "user/policy"
+        # commit 4: the bundle sidecar
+        bundle = mocked_hub.calls[-1]
+        assert {"README.md", "train_config.json"} <= set(bundle["files"])
+        # the exact line lerobot.jobs.hf watches to end remote runs early
+        assert any(
+            m.startswith("Model pushed to https://huggingface.co/user/policy") for m in caplog.messages
+        )
+
+    def test_peft_branch_skips_model_commit(self, mocked_hub):
+        policy = make_dummy_policy(repo_id="user/policy")
+
+        class FakePeftModel:
+            def save_pretrained(self, path):
+                (Path(path) / "adapter_model.safetensors").write_bytes(b"x")
+
+        publish_trained_model(make_cfg(), policy, None, None, dataset_meta=None, peft_model=FakePeftModel())
+        assert len(mocked_hub.calls) == 1  # only the bundle commit
+        bundle = mocked_hub.calls[0]
+        # adapter weights + the wrapped policy's config + card + train config, no full weights
+        assert {"README.md", "adapter_model.safetensors", "config.json", "train_config.json"} <= set(
+            bundle["files"]
+        )
+        assert "model.safetensors" not in bundle["files"]
+
+    def test_missing_repo_id_fails_loudly(self, mocked_hub):
+        policy = make_dummy_policy(repo_id=None)
+        with pytest.raises(ValueError, match="repo id"):
+            publish_trained_model(make_cfg(), policy, None, None, dataset_meta=None)
+
+
+class TestGenerateModelCard:
+    def test_free_function_renders_from_arguments(self, monkeypatch):
+        monkeypatch.setattr(train_utils.ModelCard, "validate", lambda self: None)
+        policy = make_dummy_policy(repo_id="user/policy")
+        card = generate_model_card(policy.config, cfg=make_cfg(), dataset_meta=None)
+        assert card.data.library_name == "lerobot"
+        assert card.data.datasets == "user/dataset"
+        assert "lerobot" in card.data.tags
+
+
+class TestDeprecatedPushModelToHub:
+    """`push_model_to_hub` stays callable for external scripts, delegating to the publisher."""
+
+    def test_policy_shim_warns_and_publishes(self, mocked_hub):
+        policy = make_dummy_policy(repo_id="user/policy")
+        with pytest.warns(FutureWarning, match="push_model_to_hub is deprecated"):
+            policy.push_model_to_hub(make_cfg())
+
+        # Same artifacts the method produced before: weights + config, then card + train config.
+        model_commit = mocked_hub.calls[0]
+        assert {"config.json", "model.safetensors"} <= set(model_commit["files"])
+        bundle = mocked_hub.calls[-1]
+        assert {"README.md", "train_config.json"} <= set(bundle["files"])
+
+    def test_policy_shim_warns_that_state_dict_is_ignored(self, mocked_hub):
+        policy = make_dummy_policy(repo_id="user/policy")
+        with pytest.warns(FutureWarning, match="`state_dict` argument is ignored"):
+            policy.push_model_to_hub(make_cfg(), state_dict=policy.state_dict())
@@ -0,0 +1,135 @@
+#!/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.
+
+import json
+
+import draccus
+import pytest
+
+from lerobot.configs.accelerator import (
+    AcceleratorConfig,
+    ActivationCheckpointingConfig,
+    ActivationCheckpointingMode,
+    CompileConfig,
+    DDPConfig,
+    FSDPConfig,
+    GradientAccumulationConfig,
+)
+from lerobot.configs.parallelism import ParallelismConfig
+
+
+class TestFieldValidation:
+    def test_wrap_policies_mutually_exclusive(self):
+        with pytest.raises(ValueError, match="mutually exclusive"):
+            FSDPConfig(wrap_modules=["Block"], min_num_params=1000)
+
+    def test_min_num_params_positive(self):
+        with pytest.raises(ValueError, match="min_num_params"):
+            FSDPConfig(min_num_params=0)
+
+    def test_mixed_precision_choices(self):
+        with pytest.raises(ValueError, match="mixed_precision"):
+            AcceleratorConfig(mixed_precision="tf32")
+
+    def test_gradient_accumulation_positive(self):
+        with pytest.raises(ValueError, match="gradient_accumulation.steps"):
+            GradientAccumulationConfig(steps=0)
+
+
+class TestDraccusRoundTrip:
+    @pytest.mark.parametrize(
+        "cfg",
+        [
+            AcceleratorConfig(),
+            AcceleratorConfig(
+                mixed_precision="bf16",
+                gradient_accumulation=GradientAccumulationConfig(steps=4),
+                fsdp=FSDPConfig(
+                    reshard_after_forward=False,
+                    wrap_modules=["ACTEncoderLayer", "ACTDecoderLayer"],
+                    cpu_offload=True,
+                    ignored_modules=r".*pos_embed.*",
+                ),
+                ddp=DDPConfig(find_unused_parameters=False, static_graph=True),
+                compile=CompileConfig(enabled=True, mode="max-autotune", regional=False),
+                activation_checkpointing=ActivationCheckpointingConfig(mode=ActivationCheckpointingMode.FULL),
+            ),
+            AcceleratorConfig(fsdp=FSDPConfig(min_num_params=1_000_000)),
+        ],
+    )
+    def test_encode_json_decode_identity(self, cfg):
+        payload = json.loads(json.dumps(draccus.encode(cfg)))
+        assert draccus.decode(AcceleratorConfig, payload) == cfg
+
+    def test_pre_existing_config_without_fields_gets_defaults(self):
+        assert draccus.decode(AcceleratorConfig, {}) == AcceleratorConfig()
+
+
+class TestRuntimeBuilders:
+    """The mirrors must translate into real accelerate objects (plugins built lazily)."""
+
+    @pytest.fixture(autouse=True)
+    def _requires_accelerate(self):
+        pytest.importorskip("accelerate", reason="accelerate is required (install lerobot[training])")
+
+    def test_fsdp_plugin_translation(self):
+        plugin = FSDPConfig(
+            reshard_after_forward=False, wrap_modules=["MyBlock"], cpu_offload=True
+        ).build_plugin()
+        assert plugin.fsdp_version == 2
+        assert plugin.reshard_after_forward is False
+        assert plugin.transformer_cls_names_to_wrap == ["MyBlock"]
+        # bools are normalized into torch offload policies by the plugin itself
+        assert type(plugin.cpu_offload).__name__ == "CPUOffloadPolicy"
+        # LeRobot never switches state_dict_type: FSDP2's SHARDED default must hold
+        assert plugin.state_dict_type.name == "SHARDED_STATE_DICT"
+        assert not plugin.activation_checkpointing
+
+    def test_fsdp_plugin_size_based_policy(self):
+        plugin = FSDPConfig(min_num_params=1024).build_plugin()
+        assert plugin.min_num_params == 1024
+        assert plugin.transformer_cls_names_to_wrap is None
+
+    def test_ddp_kwargs_translation(self):
+        handler = DDPConfig(find_unused_parameters=False, gradient_as_bucket_view=True).build_kwargs_handler()
+        assert handler.find_unused_parameters is False
+        assert handler.gradient_as_bucket_view is True
+
+    def test_gradient_accumulation_plugin_translation(self):
+        plugin = GradientAccumulationConfig(steps=4).build_plugin()
+        assert plugin.num_steps == 4
+        assert plugin.sync_with_dataloader is False
+
+    def test_gradient_accumulation_never_syncs_with_dataloader(self, monkeypatch):
+        """The loop cycles a finite dataloader, so accelerate's default
+        sync_with_dataloader=True would force an optimizer step at every dataset epoch
+        boundary instead of every num_steps micro-batches."""
+        captured = {}
+
+        class FakeAccelerator:
+            def __init__(self, **kwargs):
+                captured.update(kwargs)
+
+        monkeypatch.setattr("accelerate.Accelerator", FakeAccelerator)
+        parallelism = ParallelismConfig()
+        parallelism.resolve(1)
+        AcceleratorConfig(gradient_accumulation=GradientAccumulationConfig(steps=4)).build(
+            parallelism, cpu=True
+        )
+        ga_plugin = captured["gradient_accumulation_plugin"]
+        assert ga_plugin.num_steps == 4
+        assert ga_plugin.sync_with_dataloader is False
+        assert "gradient_accumulation_steps" not in captured
@@ -36,3 +36,29 @@ def test_dataset_config_none_episodes_ok():

 def test_dataset_config_empty_episodes_ok():
    DatasetConfig(repo_id="user/repo", episodes=[])
+
+
+def test_dataset_config_ignores_negative_excluded_episodes(caplog):
+    config = DatasetConfig(repo_id="user/repo", exclude_episodes=[-2, 1, -1, 3])
+
+    assert config.exclude_episodes == [1, 3]
+    assert "Ignoring negative exclude_episodes entries: [-2, -1]" in caplog.text
+
+
+def test_dataset_config_bucket_streaming_ok():
+    DatasetConfig(repo_id="user/repo", repo_type="bucket", streaming=True)
+
+
+def test_dataset_config_invalid_repo_type():
+    with pytest.raises(ValueError, match="repo_type"):
+        DatasetConfig(repo_id="user/repo", repo_type="model")
+
+
+def test_dataset_config_bucket_requires_streaming():
+    with pytest.raises(ValueError, match="streaming-only"):
+        DatasetConfig(repo_id="user/repo", repo_type="bucket")
+
+
+def test_dataset_config_bucket_rejects_eval_split():
+    with pytest.raises(ValueError, match="eval_split"):
+        DatasetConfig(repo_id="user/repo", repo_type="bucket", streaming=True, eval_split=0.1)
@@ -0,0 +1,118 @@
+#!/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.
+
+import json
+
+import draccus
+import pytest
+
+from lerobot.configs.parallelism import ContextParallelConfig, ParallelismConfig
+
+
+class TestResolve:
+    def test_single_process_defaults(self):
+        cfg = ParallelismConfig()
+        cfg.resolve(1)
+        assert (cfg.dp_replicate, cfg.dp_shard) == (1, 1)
+        assert not cfg.is_sharded and not cfg.is_replicated_only
+        assert cfg.dp_world_size == 1
+
+    def test_untouched_config_fills_ddp(self):
+        """Plain `torchrun --nproc-per-node=8` with a default config resolves to DDP."""
+        cfg = ParallelismConfig()
+        cfg.resolve(8)
+        assert cfg.dp_replicate == 8
+        assert cfg.is_replicated_only and not cfg.is_sharded
+        assert cfg.dp_world_size == 8
+
+    def test_full_shard_sentinel(self):
+        cfg = ParallelismConfig(dp_shard=-1)
+        assert cfg.is_sharded  # sharded even before resolve: -1 is an explicit opt-in
+        cfg.resolve(8)
+        assert cfg.dp_shard == 8 and cfg.dp_replicate == 1
+
+    def test_hsdp_sentinel_infers_shard(self):
+        cfg = ParallelismConfig(dp_replicate=2, dp_shard=-1)
+        cfg.resolve(8)
+        assert (cfg.dp_replicate, cfg.dp_shard) == (2, 4)
+        assert cfg.dp_world_size == 8
+
+    def test_explicit_hsdp(self):
+        cfg = ParallelismConfig(dp_replicate=2, dp_shard=4)
+        cfg.resolve(8)
+        assert cfg.is_sharded and not cfg.is_replicated_only
+
+    def test_product_mismatch_lists_all_degrees(self):
+        cfg = ParallelismConfig(dp_replicate=2, dp_shard=2)
+        with pytest.raises(ValueError, match=r"dp_replicate=2 \* dp_shard=2.*WORLD_SIZE=8"):
+            cfg.resolve(8)
+
+    def test_explicit_replicate_must_match_world(self):
+        cfg = ParallelismConfig(dp_replicate=4)
+        with pytest.raises(ValueError, match="WORLD_SIZE=8"):
+            cfg.resolve(8)
+
+    def test_sentinel_indivisible_world(self):
+        cfg = ParallelismConfig(dp_replicate=3, dp_shard=-1)
+        with pytest.raises(ValueError, match="not divisible"):
+            cfg.resolve(8)
+
+    def test_cp_fails_fast(self):
+        cfg = ParallelismConfig(dp_shard=-1, context_parallel=ContextParallelConfig(ulysses_degree=2))
+        with pytest.raises(ValueError, match="not implemented"):
+            cfg.resolve(8)
+
+
+class TestFieldValidation:
+    @pytest.mark.parametrize("kwargs", [{"dp_replicate": 0}, {"dp_shard": 0}, {"dp_shard": -2}])
+    def test_bad_dp_degrees(self, kwargs):
+        with pytest.raises(ValueError):
+            ParallelismConfig(**kwargs)
+
+    def test_cfg_parallel_capped_at_two(self):
+        ParallelismConfig(cfg_parallel=2)  # reserved but representable
+        with pytest.raises(ValueError, match="cfg_parallel"):
+            ParallelismConfig(cfg_parallel=3)
+
+    @pytest.mark.parametrize("kwargs", [{"ring_degree": 0}, {"ulysses_degree": -1}])
+    def test_bad_cp_degrees(self, kwargs):
+        with pytest.raises(ValueError):
+            ContextParallelConfig(**kwargs)
+
+    def test_dp_world_size_undefined_before_resolve(self):
+        with pytest.raises(RuntimeError, match="resolve"):
+            _ = ParallelismConfig(dp_shard=-1).dp_world_size
+
+
+class TestDraccusRoundTrip:
+    @pytest.mark.parametrize(
+        "cfg",
+        [
+            ParallelismConfig(),
+            ParallelismConfig(dp_replicate=2, dp_shard=4, cfg_parallel=2),
+            ParallelismConfig(
+                dp_shard=-1,
+                context_parallel=ContextParallelConfig(ring_degree=2, ulysses_degree=4),
+            ),
+        ],
+    )
+    def test_encode_json_decode_identity(self, cfg):
+        payload = json.loads(json.dumps(draccus.encode(cfg)))
+        assert draccus.decode(ParallelismConfig, payload) == cfg
+
+    def test_pre_existing_config_without_fields_gets_defaults(self):
+        """Checkpoints written before this feature parse with default topology."""
+        assert draccus.decode(ParallelismConfig, {}) == ParallelismConfig()
@@ -29,6 +29,13 @@ def test_message_recipe_validates_unknown_binding():
        )


+def test_canonical_recipe_loads():
+    """The canonical PI052 blend YAML loads + validates."""
+    recipe = TrainingRecipe.from_yaml(Path("src/lerobot/configs/recipes/subtask_mem_vqa_speech.yaml"))
+    assert recipe.blend is not None
+    assert sum(c.weight for c in recipe.blend.values()) == pytest.approx(1.0)
+
+
 def test_message_turn_requires_a_stream():
    """Every turn must declare a stream — None is rejected at construction.

@@ -81,6 +88,19 @@ def test_blend_component_weight_must_be_positive():
        TrainingRecipe(blend={"a": TrainingRecipe(weight=0.0, messages=[_minimal_target_turn()])})


+def test_recipe_route_must_be_supported():
+    with pytest.raises(ValueError, match="Unsupported recipe route"):
+        TrainingRecipe(weight=1.0, route="other", messages=[_minimal_target_turn()])
+
+
+def test_route_cannot_be_set_on_blend_recipe():
+    with pytest.raises(ValueError, match="only be set on a message recipe"):
+        TrainingRecipe(
+            route="vqa",
+            blend={"a": TrainingRecipe(weight=1.0, messages=[_minimal_target_turn()])},
+        )
+
+
 def test_blend_component_must_define_messages():
    # A bare TrainingRecipe(weight=1.0) would itself raise; build it without
    # going through __post_init__ to exercise the blend-level validator.
@@ -0,0 +1,118 @@
+#!/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.
+"""TrainPipelineConfig integration for the distributed fields: fail-fasts + config compat."""
+
+import draccus
+import pytest
+
+from lerobot.configs.accelerator import ActivationCheckpointingMode
+from lerobot.configs.default import DatasetConfig, PeftConfig
+from lerobot.configs.parallelism import ContextParallelConfig, ParallelismConfig
+from lerobot.configs.train import CheckpointFormat, TrainPipelineConfig
+from lerobot.optim.optimizers import AdamConfig, MultiAdamConfig
+
+
+def make_cfg(**overrides) -> TrainPipelineConfig:
+    cfg = TrainPipelineConfig(dataset=DatasetConfig(repo_id="lerobot/dummy"))
+    for name, value in overrides.items():
+        setattr(cfg, name, value)
+    return cfg
+
+
+def sharded() -> ParallelismConfig:
+    return ParallelismConfig(dp_shard=-1)
+
+
+class TestDistributedFailFasts:
+    def test_defaults_pass(self):
+        make_cfg()._validate_distributed()
+
+    def test_cp_reserved(self):
+        cfg = make_cfg(parallelism=ParallelismConfig(context_parallel=ContextParallelConfig(ring_degree=2)))
+        with pytest.raises(ValueError, match="not implemented"):
+            cfg._validate_distributed()
+
+    def test_cfg_parallel_training_rejected(self):
+        cfg = make_cfg(parallelism=ParallelismConfig(cfg_parallel=2))
+        with pytest.raises(ValueError, match="inference-only"):
+            cfg._validate_distributed()
+
+    def test_compile_placeholder(self):
+        cfg = make_cfg()
+        cfg.accelerator.compile.enabled = True
+        with pytest.raises(ValueError, match="compile"):
+            cfg._validate_distributed()
+
+    def test_activation_checkpointing_placeholder(self):
+        cfg = make_cfg()
+        cfg.accelerator.activation_checkpointing.mode = ActivationCheckpointingMode.FULL
+        with pytest.raises(ValueError, match="activation_checkpointing"):
+            cfg._validate_distributed()
+
+    def test_dcp_format_requires_sharding(self):
+        cfg = make_cfg(checkpoint_format=CheckpointFormat.DCP)
+        with pytest.raises(ValueError, match="sharded"):
+            cfg._validate_distributed()
+        cfg.parallelism = sharded()
+        cfg._validate_distributed()
+
+    def test_fp16_rejected_when_sharded(self):
+        cfg = make_cfg(parallelism=sharded())
+        cfg.accelerator.mixed_precision = "fp16"
+        with pytest.raises(ValueError, match="fp16"):
+            cfg._validate_distributed()
+        cfg.accelerator.mixed_precision = "bf16"
+        cfg._validate_distributed()
+
+    def test_peft_rejected_when_sharded(self):
+        cfg = make_cfg(parallelism=sharded(), peft=PeftConfig())
+        with pytest.raises(ValueError, match="PEFT"):
+            cfg._validate_distributed()
+
+    def test_env_eval_rejected_when_sharded(self):
+        cfg = make_cfg(parallelism=sharded(), env_eval_freq=1000)
+        cfg.env = object()  # any configured env triggers the check
+        with pytest.raises(ValueError, match="environment evaluation"):
+            cfg._validate_distributed()
+
+    def test_multi_optimizer_rejected_when_sharded(self):
+        cfg = make_cfg(parallelism=sharded(), optimizer=MultiAdamConfig())
+        with pytest.raises(ValueError, match="Multi-optimizer"):
+            cfg._validate_distributed()
+        cfg.optimizer = AdamConfig()
+        cfg._validate_distributed()
+
+
+class TestConfigCompat:
+    def test_checkpoint_format_round_trip(self):
+        for fmt in CheckpointFormat:
+            assert draccus.decode(CheckpointFormat, draccus.encode(fmt)) is fmt
+
+    def test_wants_predicates(self):
+        assert CheckpointFormat.SAFETENSORS.wants_safetensors
+        assert not CheckpointFormat.SAFETENSORS.wants_dcp
+        assert CheckpointFormat.DCP.wants_dcp and not CheckpointFormat.DCP.wants_safetensors
+        both = CheckpointFormat.SAFETENSORS_AND_DCP
+        assert both.wants_safetensors and both.wants_dcp
+
+
+def test_reward_model_rejected_when_sharded():
+    """Sharded reward runs previously failed late (missing wrap
+    units, DTensor serialization at the first checkpoint) instead of at validation."""
+    cfg = make_cfg(parallelism=sharded())
+    cfg.reward_model = object()  # any configured reward model triggers the check
+    with pytest.raises(ValueError, match="Reward-model"):
+        cfg._validate_distributed()
@@ -12,8 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+from types import SimpleNamespace
+
 import numpy as np
 import pytest
+import torch

 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

@@ -24,7 +27,9 @@ from lerobot.scripts.augment_dataset_quantile_stats import (


 def _numeric_keys(dataset):
-    return [k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string")]
+    return [
+        k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string", "language")
+    ]


 def _image_keys(dataset):
@@ -102,3 +107,112 @@ def test_quantile_stats_present_after_compute(tmp_path, lerobot_dataset_factory)
    )
    stats = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
    assert has_quantile_stats(stats)
+
+
+class FakeHFDataset:
+    """Minimal stand-in exposing the column slicing used by the augment script."""
+
+    def __init__(self, columns: dict[str, list]):
+        self._columns = columns
+
+    def select_columns(self, keys):
+        return FakeHFDataset({key: self._columns[key] for key in keys})
+
+    def __getitem__(self, index):
+        return {key: values[index] for key, values in self._columns.items()}
+
+
+def test_compute_quantile_stats_skips_language_features():
+    class FakeDataset:
+        num_episodes = 1
+        features = {
+            "action": {"dtype": "float32"},
+            "observation.language": {"dtype": "language"},
+        }
+        meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
+        hf_dataset = FakeHFDataset(
+            {
+                "action": [[0.0], [1.0]],
+                "observation.language": [
+                    [{"role": "user", "content": "pick"}],
+                    [{"role": "assistant", "content": "done"}],
+                ],
+            }
+        )
+
+    stats = compute_quantile_stats_for_dataset(FakeDataset())
+
+    assert set(stats) == {"action"}
+
+
+def test_compute_quantile_stats_skip_images_avoids_decoding():
+    class FakeDataset:
+        num_episodes = 1
+        features = {
+            "action": {"dtype": "float32"},
+            "observation.images.cam": {"dtype": "video"},
+        }
+        meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
+        hf_dataset = FakeHFDataset({"action": [[0.0], [1.0]]})
+
+        def __getitem__(self, index):
+            raise AssertionError(f"video frame {index} was decoded despite skip_images=True")
+
+    stats = compute_quantile_stats_for_dataset(FakeDataset(), skip_images=True)
+
+    assert set(stats) == {"action"}
+
+
+def test_compute_quantile_stats_handles_single_frame():
+    class FakeDataset:
+        num_episodes = 1
+        features = {"action": {"dtype": "float32"}}
+        meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 1}])
+        hf_dataset = FakeHFDataset({"action": [[5.0, 7.0]]})
+
+    stats = compute_quantile_stats_for_dataset(FakeDataset())
+
+    np.testing.assert_array_equal(stats["action"]["count"], np.array([1]))
+    for key in ("min", "max", "mean", "q01", "q10", "q50", "q90", "q99"):
+        np.testing.assert_allclose(stats["action"][key], np.array([5.0, 7.0]))
+
+
+def test_compute_quantile_stats_image_count_uses_frames():
+    frames = [torch.zeros(3, 2, 2), torch.ones(3, 2, 2)]
+
+    class FakeDataset:
+        num_episodes = 1
+        features = {"observation.images.cam": {"dtype": "video"}}
+        meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
+        hf_dataset = FakeHFDataset({})
+
+        def __getitem__(self, index):
+            return {"observation.images.cam": frames[index]}
+
+    stats = compute_quantile_stats_for_dataset(FakeDataset(), use_sampling=False)
+    image_stats = stats["observation.images.cam"]
+
+    np.testing.assert_array_equal(image_stats["count"], np.array([2]))
+    assert image_stats["mean"].shape == (3, 1, 1)
+    np.testing.assert_allclose(image_stats["mean"], np.full((3, 1, 1), 0.5))
+
+
+def test_compute_quantile_stats_accumulates_across_episodes():
+    values = [[float(value)] for value in range(100)] + [[float(value)] for value in range(1000, 1010)]
+
+    class FakeDataset:
+        num_episodes = 2
+        features = {"action": {"dtype": "float32"}}
+        meta = SimpleNamespace(
+            episodes=[
+                {"dataset_from_index": 0, "dataset_to_index": 100},
+                {"dataset_from_index": 100, "dataset_to_index": 110},
+            ]
+        )
+        hf_dataset = FakeHFDataset({"action": values})
+
+    stats = compute_quantile_stats_for_dataset(FakeDataset())
+
+    np.testing.assert_array_equal(stats["action"]["count"], np.array([110]))
+    expected_q90 = np.percentile(np.asarray(values), 90, axis=0)
+    np.testing.assert_allclose(stats["action"]["q90"], expected_q90, atol=0.1)
@@ -688,7 +688,7 @@ def test_compute_episode_stats_string_features_skipped():


 def test_aggregate_feature_stats_with_quantiles():
-    """Test aggregating feature stats that include quantiles."""
+    """Test aggregating feature stats that include quantiles uses conservative bounds."""
    stats_ft_list = [
        {
            "min": np.array([1.0]),
@@ -697,6 +697,9 @@ def test_aggregate_feature_stats_with_quantiles():
            "std": np.array([2.0]),
            "count": np.array([100]),
            "q01": np.array([1.5]),
+            "q10": np.array([2.0]),
+            "q50": np.array([5.0]),
+            "q90": np.array([9.0]),
            "q99": np.array([9.5]),
        },
        {
@@ -706,22 +709,21 @@ def test_aggregate_feature_stats_with_quantiles():
            "std": np.array([2.5]),
            "count": np.array([150]),
            "q01": np.array([2.5]),
+            "q10": np.array([3.0]),
+            "q50": np.array([6.0]),
+            "q90": np.array([11.0]),
            "q99": np.array([11.5]),
        },
    ]

    result = aggregate_feature_stats(stats_ft_list)

-    # Should preserve quantiles
-    assert "q01" in result
-    assert "q99" in result
-
-    # Verify quantile aggregation (weighted average)
-    expected_q01 = (1.5 * 100 + 2.5 * 150) / 250  # ≈ 2.1
-    expected_q99 = (9.5 * 100 + 11.5 * 150) / 250  # ≈ 10.7
-
-    np.testing.assert_allclose(result["q01"], np.array([expected_q01]), atol=1e-6)
-    np.testing.assert_allclose(result["q99"], np.array([expected_q99]), atol=1e-6)
+    # Lower quantiles use min; upper quantiles use max, regardless of counts.
+    np.testing.assert_allclose(result["q01"], np.array([1.5]), atol=1e-6)
+    np.testing.assert_allclose(result["q10"], np.array([2.0]), atol=1e-6)
+    np.testing.assert_allclose(result["q50"], np.array([5.0]), atol=1e-6)
+    np.testing.assert_allclose(result["q90"], np.array([11.0]), atol=1e-6)
+    np.testing.assert_allclose(result["q99"], np.array([11.5]), atol=1e-6)


 def test_aggregate_stats_mixed_quantiles():
@@ -878,3 +880,60 @@ def test_fixed_quantiles_always_computed():
        for q_key in expected_quantiles:
            assert q_key in episode_stats[key]
            assert episode_stats[key][q_key].shape == (features[key]["shape"][0],)
+
+
+def test_aggregate_stats_incremental_resume():
+    """Verify conservative bounds remain associative across incremental additions."""
+    # Start with episode 1 stats (narrow distribution)
+    ep1_stats = {
+        "action": {
+            "min": np.array([-10.0, -5.0]),
+            "max": np.array([10.0, 5.0]),
+            "mean": np.array([0.0, 0.0]),
+            "std": np.array([3.0, 1.5]),
+            "count": np.array([500]),
+            "q01": np.array([-9.0, -4.5]),
+            "q99": np.array([9.0, 4.5]),
+        },
+    }
+
+    # Episode 2: wider distribution on dim 0
+    ep2_stats = {
+        "action": {
+            "min": np.array([-30.0, -5.0]),
+            "max": np.array([40.0, 6.0]),
+            "mean": np.array([5.0, 0.5]),
+            "std": np.array([15.0, 2.0]),
+            "count": np.array([100]),
+            "q01": np.array([-25.0, -4.0]),
+            "q99": np.array([35.0, 5.5]),
+        },
+    }
+
+    # First aggregation: ep1 + ep2 (simulates save_episode for ep2)
+    cumulative = aggregate_stats([ep1_stats, ep2_stats])
+
+    # q01 should take min (conservative lower bound)
+    np.testing.assert_allclose(cumulative["action"]["q01"], np.array([-25.0, -4.5]))
+    # q99 should take max (conservative upper bound)
+    np.testing.assert_allclose(cumulative["action"]["q99"], np.array([35.0, 5.5]))
+
+    # Episode 3: even wider on dim 1
+    ep3_stats = {
+        "action": {
+            "min": np.array([-8.0, -20.0]),
+            "max": np.array([8.0, 25.0]),
+            "mean": np.array([0.0, 3.0]),
+            "std": np.array([2.0, 8.0]),
+            "count": np.array([50]),
+            "q01": np.array([-7.0, -18.0]),
+            "q99": np.array([7.0, 22.0]),
+        },
+    }
+
+    # Second aggregation: cumulative + ep3 (simulates save_episode for ep3)
+    cumulative2 = aggregate_stats([cumulative, ep3_stats])
+
+    # Bounds should widen monotonically
+    np.testing.assert_allclose(cumulative2["action"]["q01"], np.array([-25.0, -18.0]))
+    np.testing.assert_allclose(cumulative2["action"]["q99"], np.array([35.0, 22.0]))
@@ -20,6 +20,7 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

 from lerobot.datasets.dataset_reader import DatasetReader
+from lerobot.datasets.language import LANGUAGE_EVENTS
 from lerobot.utils.import_utils import get_safe_default_video_backend

 # ── Loading ──────────────────────────────────────────────────────────
@@ -66,6 +67,22 @@ def test_try_load_returns_false_when_no_data(tmp_path):
    assert reader.hf_dataset is None


+def test_load_rejects_language_columns_missing_from_metadata(tmp_path, lerobot_dataset_factory):
+    import pyarrow as pa
+    import pyarrow.parquet as pq
+
+    dataset = lerobot_dataset_factory(
+        root=tmp_path / "ds", total_episodes=1, total_frames=10, use_videos=False
+    )
+    parquet_path = next((dataset.root / "data").glob("*/*.parquet"))
+    table = pq.read_table(parquet_path)
+    language_events = pa.array([[] for _ in range(len(table))], type=pa.list_(pa.string()))
+    pq.write_table(table.append_column(LANGUAGE_EVENTS, language_events), parquet_path)
+
+    with pytest.raises(ValueError, match=r"language feature\(s\) missing from metadata.*language_events"):
+        dataset.reader.load_and_activate()
+
+
 # ── Counts ───────────────────────────────────────────────────────────


@@ -28,7 +28,12 @@ from huggingface_hub import DatasetCard

 import lerobot.datasets.utils as dataset_utils
 from lerobot.datasets.io_utils import hf_transform_to_torch
-from lerobot.datasets.utils import create_lerobot_dataset_card, get_repo_versions, get_safe_version
+from lerobot.datasets.utils import (
+    create_lerobot_dataset_card,
+    get_repo_versions,
+    get_safe_version,
+    resolve_episode_indices,
+)
 from lerobot.utils.constants import ACTION, OBS_IMAGES
 from lerobot.utils.feature_utils import combine_feature_dicts

@@ -62,6 +67,20 @@ def test_default_parameters():
    ]


+def test_resolve_episode_indices_applies_allowlist_and_exclusions():
+    assert resolve_episode_indices([4, 1, 3, 0], 5, [1, 4]) == [3, 0]
+
+
+def test_resolve_episode_indices_preserves_none_without_filtering():
+    assert resolve_episode_indices(None, 5) is None
+
+
+def test_resolve_episode_indices_ignores_out_of_range_values(caplog):
+    assert resolve_episode_indices([-1, 0, 3, 5], 4, [-2, 3, 8]) == [0]
+    assert "Ignoring episode indices outside the dataset range [0, 4): [-1, 5]" in caplog.text
+    assert "Ignoring excluded episode indices outside the dataset range [0, 4): [-2, 8]" in caplog.text
+
+
@pytest.mark.parametrize("token", ["hf_test_token", True, False])
 def test_get_repo_versions_forwards_token(monkeypatch, token):
    api = Mock()
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
dependabot[bot]	c2ac99abf2	chore(deps): bump the actions group across 1 directory with 6 updates Bumps the actions group with 6 updates in the / directory: \| Package \| From \| To \| \| --- \| --- \| --- \| \| [docker/login-action](https://github.com/docker/login-action) \| `4.4.0` \| `4.6.0` \| \| [anthropics/claude-code-action](https://github.com/anthropics/claude-code-action) \| `1.0.179` \| `1.0.183` \| \| [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) \| `8.3.2` \| `9.0.0` \| \| [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) \| `1.14.1` \| `1.14.2` \| \| [trufflesecurity/trufflehog](https://github.com/trufflesecurity/trufflehog) \| `3.95.9` \| `3.96.0` \| \| [actions/stale](https://github.com/actions/stale) \| `10` \| `11` \| Updates `docker/login-action` from 4.4.0 to 4.6.0 - [Release notes](https://github.com/docker/login-action/releases) - [Commits](https://github.com/docker/login-action/compare/v4.4.0...v4.6.0) Updates `anthropics/claude-code-action` from 1.0.179 to 1.0.183 - [Release notes](https://github.com/anthropics/claude-code-action/releases) - [Commits](https://github.com/anthropics/claude-code-action/compare/b76a0776ae74036e77cd11018083743453d7ad35...be7b93b1907a4abad570368f3c74b6fe3807510b) Updates `astral-sh/setup-uv` from 8.3.2 to 9.0.0 - [Release notes](https://github.com/astral-sh/setup-uv/releases) - [Commits](https://github.com/astral-sh/setup-uv/compare/v8.3.2...v9.0.0) Updates `pypa/gh-action-pypi-publish` from 1.14.1 to 1.14.2 - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/ba38be9e461d3875417946c167d0b5f3d385a247...dc37677b2e1c63e2034f94d8a5b11f265b73ba33) Updates `trufflesecurity/trufflehog` from 3.95.9 to 3.96.0 - [Release notes](https://github.com/trufflesecurity/trufflehog/releases) - [Commits](https://github.com/trufflesecurity/trufflehog/compare/27b0417c16317ca9a472a9a8092acce143b49c55...6f3c981e7b77f235fd2702dd74af25fc4b72bf11) Updates `actions/stale` from 10 to 11 - [Release notes](https://github.com/actions/stale/releases) - [Changelog](https://github.com/actions/stale/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/stale/compare/v10...v11) --- updated-dependencies: - dependency-name: docker/login-action dependency-version: 4.6.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions - dependency-name: anthropics/claude-code-action dependency-version: 1.0.183 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: actions - dependency-name: astral-sh/setup-uv dependency-version: 9.0.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions - dependency-name: pypa/gh-action-pypi-publish dependency-version: 1.14.2 dependency-type: direct:production update-type: version-update:semver-patch dependency-group: actions - dependency-name: trufflesecurity/trufflehog dependency-version: 3.96.0 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: actions - dependency-name: actions/stale dependency-version: '11' dependency-type: direct:production update-type: version-update:semver-major dependency-group: actions ... Signed-off-by: dependabot[bot] <support@github.com>	2026-08-07 14:00:00 +00:00
Pepijn	6c73c413eb	docs: add API documentation infrastructure (#4348 ) * docs: add API documentation infrastructure LeRobot's documentation build passes `--not_python_module`, which tells doc-builder there is no importable Python package and disables `[[autodoc]]` entirely. The result is that all 90+ pages are hand-written guides and there is no generated API reference at all. This is the machinery to change that. It deliberately contains no docstring changes of its own — every docstring edit lives in the follow-up PR, so this one can be reviewed as tooling and configuration alone. The standard. `docs/source/writing_docstrings.mdx` is the contract: Google section headers with Hugging Face type formatting, the machine-checked argument line, `Attributes:`, doc-builder cross-references, fenced doctest examples. It also records three behaviours that are not discoverable from the source and were verified against a local build: `[[autodoc]]` silently skips members with no docstring; doc-builder does not inherit docstrings from base classes, so a registered config shim whose body is `pass` renders every field with no description; and module-level aliases resolve to the canonical class. Autodoc turned on, with two changes that are not obvious: - `--version main` on the main-docs job. Without `--not_python_module`, doc-builder resolves the version from `lerobot.__version__` and only maps it to the default branch when it contains "dev". transformers relies on that; our main carries 0.6.2. Verified by building both ways — dropping the flag alone would publish the main docs to /lerobot/v0.6.2/ instead of /lerobot/main/ and disable notebook building. - `pre_command` on both jobs. doc-builder ships a mock-deps registry entry for lerobot, so the reusable workflow takes its light-install path, which cannot import the package. The heavy dependencies cannot be mocked either: draccus runs `register_subclass` at import time and `processor/converters.py` calls `functools.singledispatch.register(torch.Tensor)`, which needs a real class. `[dataset]` is the only extra required. Workflow triggers gain `src/`, since the reference is now generated from docstrings. `docs/source/api/` is excluded from the prettier hook, which reads `[[autodoc]]` member lists as lazy paragraph continuations and joins a ten-entry list onto one line. Nine API reference pages, scaffolded with each module's base class. Doctests. `LeRobotDocTestParser` is mandatory rather than optional here: ruff's `docstring-code-format = true` drops the blank line before a closing fence, after which stdlib's `_EXAMPLE_RE` reads the fence as expected output and every example with output fails. It is written against the installed pytest rather than copied from transformers, whose version predates pytest 9's `import_path` signature and its own fix for the `@property` line-number bug. `preprocess_string` also diverges: the upstream fenced-block split puts a single-line example's code in a chunk with no `>>>` in it, so neither the CUDA skip nor the `+IGNORE_RESULT` injection fires for it. Checkers. `utils/check_docstrings.py` is the ~300-line core of the 2203-line transformers original; the `@auto_docstring` system, modular propagation, GitPython and `checkers.py` are not ported. `utils/check_config_docstrings.py` checks that every registered robot config documents its port and calibration semantics. Gates*, all set to values that pass today: ruff `D` with per-file-ignores per unconverted module, `interrogate` at `fail-under = 52` against a measured 52.1%, and Makefile targets wired into the quality workflow. The doctest allowlist ships empty and the `doctest` target handles that, because the files carrying runnable examples arrive with the docstring PR. ci: build the docs on Python 3.12 The shared doc-builder workflows create their virtualenv with the runner's system Python, which is 3.10.12 on ubuntu-22.04. lerobot requires >=3.12, so the build died during "Setup environment": × No solution found when resolving dependencies: ╰─▶ Because the current Python version (3.10.12) does not satisfy Python>=3.12 and lerobot==0.6.2 depends on Python>=3.12 ... That step runs before `pre_command`, so the real install this workflow already performs never got the chance to run. There was no fix available on the caller side either: `env:` does not propagate into a reusable workflow, so `UV_PYTHON` is unavailable, and `uv venv` runs in the runner workspace root rather than the checkout, so a `.python-version` file cannot reach it. The non-light fallback (`uv pip install "./pkg[dev]"`) fails identically, so this is not specific to the mock-deps path — it blocks any package requiring 3.12+. huggingface/doc-builder#808 adds a `python_version` input to both build workflows, which this passes. Pins move to that merge commit, picking up three unrelated fixes in the same range (#810, #811, #812); the upload workflow is unchanged there and is bumped only to keep all three pins on one SHA. * chore: sort imports in vla_jepa tests Enabling pydocstyle in the previous commit changes how ruff determines where a module's import block ends, which makes I001 fire on three vla_jepa tests that were clean before. The blank line between the `conftest` and `lerobot` imports is the trigger: both are first-party, so isort wants them in one contiguous block, and the docstring-aware analysis is what makes it notice. These files are unrelated to the API reference, so the fix is only to satisfy the new gate.	2026-08-07 15:55:25 +02:00
Pepijn	3aabd135d3	fix(tests): port the multi-GPU FSDP test off the superseded accelerate YAML flow (#4370 ) The FSDP multi-GPU test still generated an `accelerate launch --config_file` FSDP1 YAML, which exports ACCELERATE_USE_FSDP into the workers. Since the FSDP2/parallelism rewrite, `guard_against_env_interference()` hard-errors on exactly that variable, so the test failed on every rank. Its assertions were stale too: sharded runs now write DCP optimizer shards, not a gathered `optimizer_state.safetensors`. Drop the YAML generation entirely and use `accelerate launch` as the plain launcher the docs describe, with the topology coming from `--parallelism.*`. Co-authored-by: Claude Opus 5 <noreply@anthropic.com>	2026-08-07 20:54:14 +08:00
Pepijn	2c1adc378e	test(ema): Rename test that trips TruffleHog's Lob detector (#4371 ) TruffleHog's Lob API-key detector matches `\b((live\|test)_[a-zA-Z0-9_]{35})\b`. One EMA config test was named `test_` plus exactly 35 word characters, so it matched, and Lob's verifier treats the 403/422 that api.lob.com returns for junk keys as proof of a live key. That failed the Security workflow on main after huggingface/lerobot#4323 with "Found verified Lob result" and exit code 183. Add one word to the name so the suffix is 39 characters rather than 35. No behavior change. Note that the offending string is deliberately not spelled out here: TruffleHog scans commit messages as well as added lines, so quoting it would retrigger the very detector this commit is working around. Refs #4323 Co-authored-by: Claude Opus 5 <noreply@anthropic.com>	2026-08-07 14:22:41 +02:00
alejodosr	266be2bd17	feat(train): add opt-in EMA of the policy weights (--ema.enable=true) (#4323 ) * feat(train): add opt-in EMA of the policy weights (--ema.enable=true) Maintain an EMA shadow via diffusers' EMAModel (lazy import, no new dependency) with the reference Diffusion Policy schedule. Saves the shadow for exact resume plus a loadable pretrained_model_ema/ per checkpoint, evaluates the EMA weights during env eval, and pushes them to a sibling <repo_id>-ema repo. Fixes huggingface/lerobot#4259. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * docs(diffusion): document the --ema.enable training flag Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix(tests): skip EMA training tests when accelerate/diffusers are missing * feat(train): support constant EMA decay (--ema.decay) for openpi-style policies * fix(train): gate EMA step on sync_gradients; use parallel_dims.is_sharded guard --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>	2026-08-07 13:52:23 +02:00
Haoming Song	ff7cc3de1d	fix(train): narrow the accelerate env guard, and fix a device-bound assert (#4347 ) Two post-merge CI failures on main, both from #4010. Benchmark Integration Tests (Libero) — `accelerate launch` exports whole groups of variables unconditionally (the five ACCELERATE_DYNAMO_* it writes default the backend to "no"), so matching on prefixes refused launches that configure nothing, contradicting the documented flow where accelerate is supported as a plain launcher. The guard now watches only the three switches that hand a subsystem to the environment. GPU Tests — `test_metrics_tracker_reduce_across_ranks_invokes_all_reduce` compared the captured reduction buffer against a CPU tensor, so the assert raised "Expected all tensors to be on the same device" wherever CUDA is available. The expected tensor is built on the buffer's device instead.	2026-08-06 18:39:24 +02:00
Hiroaki.Ishikawa	31fedfd9dd	fix(dataset): use conservative bounds for quantile aggregation instead of incorrect weighted mean (#3804 ) * fix(stats): use conservative bounds for quantile aggregation instead of incorrect weighted mean * docs: add --overwrite/--skip-images/--root options to augment_dataset_quantile_stats usage * fix(dataset): clarify quantile aggregation semantics * fix(augment): handle quantile stats edge cases	2026-08-06 18:39:01 +02:00
Pepijn	b1bf24f565	feat(train): split phase timing metrics (#4344 )	2026-08-06 17:20:35 +02:00
Haoming Song	ef88d4e52b	feat(train): parallel training framework — FSDP2, HSDP, gradient accumulation, and DCP checkpoints (#4010 ) * feat(train): parallel training engine with FSDP2, HSDP, and DCP checkpoints Replace the FSDP1 training path with a config-owned parallel-training engine: - Topology and runtime configs (--parallelism., --accelerator.): dp_replicate x dp_shard degrees select single-process, DDP (unchanged default), FSDP2, or HSDP; mixed precision, first-class gradient accumulation, and FSDP/DDP tuning knobs are mirrored as plain dataclasses that build the accelerate objects at runtime, so every run is reproducible from its train_config.json alone. Accelerate env vars are guarded against configuring the engine behind the config system's back. - Declarative policy surface: policies declare FSDP2 wrap units (_fsdp_wrap_modules) and non-forward entry points (_fsdp_forward_methods); a shared engine resolves them around accelerator.prepare(). Context-parallel fields are reserved and validated to 1. - Checkpoints: selectable --checkpoint_format (safetensors \| dcp \| safetensors_dcp); the sharded optimizer channel is always DCP; two-phase resume (step+RNG before prepare, DCP model/optimizer after) reshards across GPU-topology changes; lerobot-convert-dcp merges DCP shards into a distributable model.safetensors offline. - Publishing: PreTrainedPolicy.push_model_to_hub is replaced by the free publish_trained_model (model + processors + card + train config, all-ranks gather with main-rank writes); PreTrainedPolicy._save_pretrained gathers state dicts internally, removing the state_dict= threading from save_pretrained. - lerobot_train is restructured around the engine: optimizer built before the single prepare() call, deferred weight load on DCP resumes, collective save_checkpoint with no call-site rank branches, dp-world-size-based sample accounting. Breaking changes: FSDP checkpoints from lerobot <= 0.6.x are not resumable (weights stay loadable via from_pretrained; pin lerobot==0.6.x to finish old runs); the `accelerate launch --config_file` yaml flow is superseded by the config flags; training autocast is owned exclusively by --accelerator.mixed_precision (policy.dtype only casts parameters). Also fixes: reward-model hub publishing crash (TypeError on extra kwargs). Verified by ~200 new CPU tests (config round-trips, checkpoint round-trips per format, two-phase resume, publisher contracts, converter equivalence, accelerate canaries), a 5-test 4-GPU suite (FSDP2 save/resume bit-exactness, HSDP/DDP loss parity, changed-topology resume, all-ranks save_pretrained, grad-accum equivalence), and end-to-end ACT (1/4/8 GPUs) + FastWAM 6B (FSDP2 + HSDP) training runs.	2026-08-06 19:16:41 +08:00
Pepijn	64b23178d5	feat(data): add recipe-driven language supervision (#4182 ) * feat(data): add recipe-driven language supervision * test(collate): expect preserved language columns * Address PR review feedback * Address Claude review feedback	2026-08-04 16:48:47 +02:00
Xingdong Zuo	f66e5128ec	fix(robots): clamp LeKiwi relative target without KeyError (#4281 ) `send_action` keys `arm_goal_pos` as "<motor>.pos" but a Present_Position read is keyed by bare motor name, so pairing the two raised KeyError as soon as `max_relative_target` was set, making the safety cap unusable. SOFollower strips the suffix before the same lookup.	2026-08-04 08:58:53 +02:00
Nikodem Bartnik	1e3a158e13	add third party hardware section (#4308 ) * add third party hardware section * fix links to main * fix formatting	2026-08-03 18:33:18 +02:00
Steven Palma	dc0cee9c75	chore(dependencies): Bump lerobot to 0.6.2 (#4313 )	2026-08-03 16:30:05 +02:00
Steven Palma	7e241bd630	chore(dependencies): update uv.lock (#4284 ) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2026-08-03 16:05:01 +02:00
Steven Palma	e867359d09	feat(Train): enable buckets with streaming dataset (#4312 )	2026-08-03 16:00:48 +02:00
Khalil Meftah	f1efa588b8	fix(sarm): fail fast on unusable episode annotations (#4306 ) * fix(sarm): warn when dense/sparse targets silently collapse to all-zero In dense_only/dual modes, if meta/episodes/.parquet has no usable subtask columns (column absent or NaN), _load_episode_annotations returns None and find_stage_and_tau yields stage 0 / tau 0 for every frame. Training "succeeds" but the head silently learns to predict 0 everywhere, with no warning. This complements #2880 (which restored loading of episodes_df): there the DataFrame is loaded but the _subtask_names column is missing/NaN. Add a one-time validation at processor construction that logs a clear warning (all episodes missing -> predict-all-zero; some missing -> partial). Purely additive logging, no change to training math. Closes #3842 * fix(sarm): fail fast on unusable episode annotations --------- Co-authored-by: 1thanShih <Smartshithan1620.en12@nycu.edu.tw>	2026-08-03 13:30:00 +02:00
Steven Palma	3e37269dc6	perf(pi0fast): stop FAST decoding at the end-of-action marker (#4275 ) * perf(pi0fast): stop FAST decoding at the end-of-action marker The decode loop always runs all max_decoding_steps (256) tokens, but detokenize_actions() cuts the output at the first "\|", so most of them get generated and then thrown away. On LIBERO the marker lands around token 25-35. Stopping there gives the same actions ~4x faster end to end. Checked on libero_10: 50/50 episodes came out identical to the old path, same success and same state trajectories. * feat(policies): early stopping + termination --------- Co-authored-by: Thomas Landeg <tomlandeg@gmail.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com>	2026-08-03 13:16:58 +02:00