chore(deps): bump aiohttp in the uv group across 1 directory

--- updated-dependencies: - dependency-name: aiohttp dependency-version: 3.13.4 dependency-type: indirect dependency-group: uv ... Signed-off-by: dependabot[bot] <support@github.com>
2026-07-08 02:22:02 +00:00 · 2026-04-12 18:05:26 +00:00
32 changed files with 397 additions and 3629 deletions
@@ -1,490 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Integration tests: build an isolated Docker image per benchmark and run a
-# 1-episode smoke eval. Each benchmark gets its own image so incompatible
-# dependency trees (e.g. hf-libero vs metaworld==3.0.0) can never collide.
-#
-# To add a new benchmark:
-#   1. Add docker/Dockerfile.benchmark.<name>  (install only lerobot[<name>])
-#   2. Copy one of the jobs below and adjust the image name and eval command.
-name: Benchmark Integration Tests
-
-on:
-  # Run manually from the Actions tab
-  workflow_dispatch:
-
-  # Run every Monday at 02:00 UTC.
-  schedule:
-    - cron: "0 2 * * 1"
-
-  push:
-    branches:
-      - main
-    paths:
-      - "src/lerobot/envs/**"
-      - "src/lerobot/scripts/lerobot_eval.py"
-      - "docker/Dockerfile.benchmark.*"
-      - ".github/workflows/benchmark_tests.yml"
-      - "pyproject.toml"
-
-  pull_request:
-    branches:
-      - main
-    paths:
-      - "src/lerobot/envs/**"
-      - "src/lerobot/scripts/lerobot_eval.py"
-      - "docker/Dockerfile.benchmark.*"
-      - ".github/workflows/benchmark_tests.yml"
-      - "pyproject.toml"
-
-permissions:
-  contents: read
-
-env:
-  UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.12"
-
-# Cancel in-flight runs for the same branch/PR.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  # ── LIBERO ────────────────────────────────────────────────────────────────
-  # Isolated image: lerobot[libero] only (hf-libero, dm-control, mujoco chain)
-  libero-integration-test:
-    name: Libero — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-
-      # Build the benchmark-specific image. The Dockerfile separates dep-install
-      # from source-copy, so code-only changes skip the slow uv-sync layer
-      # when the runner has a warm Docker daemon cache.
-      - name: Build Libero benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.libero
-          push: false
-          load: true
-          tags: lerobot-benchmark-libero:ci
-
-      - name: Run Libero smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          # Named container (no --rm) so we can docker cp artifacts out.
-          # Output to /tmp inside the container — /artifacts doesn't exist
-          # and user_lerobot cannot create root-level dirs.
-          docker run --name libero-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            lerobot-benchmark-libero:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=pepijn223/smolvla_libero \
-                --env.type=libero \
-                --env.task=libero_spatial \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
-                --policy.empty_cameras=1 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env libero --task libero_spatial \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy Libero artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/libero-artifacts
-          docker cp libero-eval:/tmp/eval-artifacts/. /tmp/libero-artifacts/ 2>/dev/null || true
-          docker rm -f libero-eval || true
-
-      - name: Parse Libero eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/libero-artifacts \
-            --env libero \
-            --task libero_spatial \
-            --policy pepijn223/smolvla_libero
-
-      - name: Upload Libero rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-rollout-video
-          path: /tmp/libero-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload Libero eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-metrics
-          path: /tmp/libero-artifacts/metrics.json
-          if-no-files-found: warn
-
-      # ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
-      # Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
-      # immediately runs eval inside the training loop (eval_freq=1, 1 episode).
-      # Tests the full train→eval-within-training pipeline end-to-end.
-      - name: Run Libero train+eval smoke (1 step, eval_freq=1)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name libero-train-smoke --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            lerobot-benchmark-libero:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              accelerate launch --num_processes=1 \$(which lerobot-train) \
-                --policy.path=lerobot/smolvla_base \
-                --policy.load_vlm_weights=true \
-                --policy.scheduler_decay_steps=25000 \
-                --policy.freeze_vision_encoder=false \
-                --policy.train_expert_only=false \
-                --dataset.repo_id=lerobot/libero \
-                --dataset.episodes=[0] \
-                --dataset.use_imagenet_stats=false \
-                --env.type=libero \
-                --env.task=libero_spatial \
-                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
-                --policy.empty_cameras=1 \
-                --output_dir=/tmp/train-smoke \
-                --steps=1 \
-                --batch_size=1 \
-                --eval_freq=1 \
-                --eval.n_episodes=1 \
-                --eval.batch_size=1 \
-                --eval.use_async_envs=false \
-                --save_freq=1 \
-                --policy.push_to_hub=false \
-                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.image2\": \"observation.images.camera2\"}'
-            "
-
-      - name: Copy Libero train-smoke artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/libero-train-smoke-artifacts
-          docker cp libero-train-smoke:/tmp/train-smoke/. /tmp/libero-train-smoke-artifacts/ 2>/dev/null || true
-          docker rm -f libero-train-smoke || true
-
-      - name: Upload Libero train-smoke eval video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-train-smoke-video
-          path: /tmp/libero-train-smoke-artifacts/eval/
-          if-no-files-found: warn
-
-  # ── METAWORLD ─────────────────────────────────────────────────────────────
-  # Isolated image: lerobot[metaworld] only (metaworld==3.0.0, mujoco>=3 chain)
-  metaworld-integration-test:
-    name: MetaWorld — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          username: ${{ secrets.DOCKERHUB_LEROBOT_USERNAME }}
-          password: ${{ secrets.DOCKERHUB_LEROBOT_PASSWORD }}
-
-      - name: Build MetaWorld benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.metaworld
-          push: false
-          load: true
-          tags: lerobot-benchmark-metaworld:ci
-
-      - name: Run MetaWorld smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name metaworld-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            lerobot-benchmark-metaworld:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=pepijn223/smolvla_metaworld \
-                --env.type=metaworld \
-                --env.task=metaworld-push-v3 \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.image\": \"observation.images.camera1\"}' \
-                --policy.empty_cameras=2 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env metaworld --task metaworld-push-v3 \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy MetaWorld artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/metaworld-artifacts
-          docker cp metaworld-eval:/tmp/eval-artifacts/. /tmp/metaworld-artifacts/ 2>/dev/null || true
-          docker rm -f metaworld-eval || true
-
-      - name: Parse MetaWorld eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/metaworld-artifacts \
-            --env metaworld \
-            --task metaworld-push-v3 \
-            --policy pepijn223/smolvla_metaworld
-
-      - name: Upload MetaWorld rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: metaworld-rollout-video
-          path: /tmp/metaworld-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload MetaWorld eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: metaworld-metrics
-          path: /tmp/metaworld-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── LIBERO-plus ───────────────────────────────────────────────────────────
-  libero-plus-integration-test:
-    name: LIBERO-plus — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd  # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Build LIBERO-plus benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.libero_plus
-          push: false
-          load: true
-          tags: lerobot-benchmark-libero-plus:ci
-          cache-from: type=local,src=/tmp/.buildx-cache-libero-plus
-          cache-to: type=local,dest=/tmp/.buildx-cache-libero-plus,mode=max
-
-      - name: Run LIBERO-plus smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name libero-plus-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            lerobot-benchmark-libero-plus:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=lerobot/smolvla_libero_plus \
-                --env.type=libero_plus \
-                --env.task=libero_spatial \
-                '--env.task_ids=[0,100,260,500,1000,1500,2000,2400]' \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--env.camera_name_mapping={\"agentview_image\": \"camera1\", \"robot0_eye_in_hand_image\": \"camera2\"}' \
-                --policy.empty_cameras=1 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env libero_plus --task libero_spatial \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy LIBERO-plus artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/libero-plus-artifacts
-          docker cp libero-plus-eval:/tmp/eval-artifacts/. /tmp/libero-plus-artifacts/ 2>/dev/null || true
-          docker rm -f libero-plus-eval || true
-
-      - name: Parse LIBERO-plus eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/libero-plus-artifacts \
-            --env libero_plus \
-            --task libero_spatial \
-            --policy lerobot/smolvla_libero_plus
-
-      - name: Upload LIBERO-plus rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-plus-rollout-video
-          path: /tmp/libero-plus-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload LIBERO-plus eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: libero-plus-metrics
-          path: /tmp/libero-plus-artifacts/metrics.json
-          if-no-files-found: warn
-
-  # ── ROBOMME ───────────────────────────────────────────────────────────────
-  robomme-integration-test:
-    name: RoboMME — build image + 1-episode eval
-    runs-on:
-      group: aws-g6-4xlarge-plus
-    env:
-      HF_USER_TOKEN: ${{ secrets.LEROBOT_HF_USER }}
-
-    steps:
-      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
-        with:
-          persist-credentials: false
-          lfs: true
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3 # zizmor: ignore[unpinned-uses]
-        with:
-          cache-binary: false
-
-      - name: Build RoboMME benchmark image
-        uses: docker/build-push-action@v6 # zizmor: ignore[unpinned-uses]
-        with:
-          context: .
-          file: docker/Dockerfile.benchmark.robomme
-          push: false
-          load: true
-          tags: lerobot-benchmark-robomme:ci
-
-      - name: Run RoboMME smoke eval (1 episode)
-        if: env.HF_USER_TOKEN != ''
-        run: |
-          docker run --name robomme-eval --gpus all \
-            --shm-size=4g \
-            -e HF_HOME=/tmp/hf \
-            -e HF_USER_TOKEN="${HF_USER_TOKEN}" \
-            -e HF_HUB_DOWNLOAD_TIMEOUT=300 \
-            lerobot-benchmark-robomme:ci \
-            bash -c "
-              hf auth login --token \"\$HF_USER_TOKEN\" --add-to-git-credential 2>/dev/null || true
-              lerobot-eval \
-                --policy.path=lerobot/smolvla_robomme \
-                --env.type=robomme \
-                --env.task=PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
-                --env.dataset_split=test \
-                --eval.batch_size=1 \
-                --eval.n_episodes=1 \
-                --eval.use_async_envs=false \
-                --policy.device=cuda \
-                '--rename_map={\"observation.images.image\": \"observation.images.camera1\", \"observation.images.wrist_image\": \"observation.images.camera2\"}' \
-                --policy.empty_cameras=3 \
-                --output_dir=/tmp/eval-artifacts
-              python scripts/ci/extract_task_descriptions.py \
-                --env robomme --task PickXtimes,BinFill,StopCube,MoveCube,InsertPeg \
-                --output /tmp/eval-artifacts/task_descriptions.json
-            "
-
-      - name: Copy RoboMME artifacts from container
-        if: always()
-        run: |
-          mkdir -p /tmp/robomme-artifacts
-          docker cp robomme-eval:/tmp/eval-artifacts/. /tmp/robomme-artifacts/ 2>/dev/null || true
-          docker rm -f robomme-eval || true
-
-      - name: Parse RoboMME eval metrics
-        if: always()
-        run: |
-          python3 scripts/ci/parse_eval_metrics.py \
-            --artifacts-dir /tmp/robomme-artifacts \
-            --env robomme \
-            --task PickXtimes \
-            --policy lerobot/smolvla_robomme
-
-      - name: Upload RoboMME rollout video
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robomme-rollout-video
-          path: /tmp/robomme-artifacts/videos/
-          if-no-files-found: warn
-
-      - name: Upload RoboMME eval metrics
-        if: always()
-        uses: actions/upload-artifact@v4 # zizmor: ignore[unpinned-uses]
-        with:
-          name: robomme-metrics
-          path: /tmp/robomme-artifacts/metrics.json
-          if-no-files-found: warn
@@ -1 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
@@ -1,60 +0,0 @@
-# LeRobot LIBERO Training Benchmark
-
-Train and evaluate all LeRobot policies on [LIBERO](https://libero-project.github.io/) and publish results as a HuggingFace leaderboard dataset.
-
-## Policies
-
-| Policy         | Base Model           | GPUs | LR     | Chunk | Notes                                 |
-| -------------- | -------------------- | ---- | ------ | ----- | ------------------------------------- |
-| pi0            | lerobot/pi0_base     | 8    | 2.5e-5 | 30    | PaliGemma + Gemma flow matching       |
-| pi0_fast       | lerobot/pi0fast-base | 8    | 2.5e-5 | 30    | Requires tokenizer pre-training       |
-| pi05           | lerobot/pi05_base    | 8    | 2.5e-5 | 30    | Quantiles normalization               |
-| groot          | nvidia/GR00T-N1.5-3B | 8    | 1e-4   | 30    | bf16, diffusion head + projector only |
-| act            | From scratch         | 1    | 1e-5   | 30    | ResNet-18, lightweight                |
-| diffusion      | From scratch         | 1    | 1e-4   | 32\*  | U-Net, horizon must be divisible by 8 |
-| smolvla        | lerobot/smolvla_base | 8    | 1e-4   | 30    | SmolVLM2-500M                         |
-| xvla           | lerobot/xvla-widowx  | 4    | 1e-4   | 32\*  | Florence2 + CLIP                      |
-| multi_task_dit | From scratch         | 1    | 2e-5   | 32\*  | CLIP + DiT                            |
-
-\* These policies use `horizon` rather than `chunk_size`. Set to 32 (nearest valid value to 30).
-
-## Training spec
-
- **Steps**: 5,000 per policy
- **Batch size**: 32 per GPU (effective BS = 256 for multi-GPU)
- **Dataset**: `lerobot/libero` (libero_spatial)
- **Evaluation**: 20 episodes after training
- **LR**: each policy's default optimizer/scheduler preset
- **Results**: each SLURM job publishes its own row to the HF leaderboard dataset automatically
-
-## Quick start
-
-### 1. Generate SLURM scripts
-
-```bash
-python benchmarks/libero/run_benchmark.py \
-    --output_dir /scratch/lerobot-benchmark \
-    --hub_org lerobot
-```
-
-### 2. Submit jobs
-
-```bash
-# If using pi0_fast, submit tokenizer first:
-sbatch /scratch/lerobot-benchmark/slurm_scripts/00_tokenizer.sh
-# Wait, then submit pi0_fast
-
-# All other policies can run in parallel:
-for script in /scratch/lerobot-benchmark/slurm_scripts/[0-9][0-9]_*.sh; do
-    [[ "$script" == *pi0_fast* ]] && continue
-    sbatch "$script"
-done
-```
-
-Each job publishes its result to `lerobot/benchmark-libero` on the Hub when it finishes.
-
-## Prerequisites
-
- SLURM cluster with CUDA GPUs (A100 80GB recommended for VLM policies)
- `pip install lerobot[pi,smolvla,groot,xvla,multi_task_dit,libero] datasets`
- `huggingface-cli login`
@@ -1,606 +0,0 @@
-#!/usr/bin/env python
-"""Generate SLURM sbatch scripts for training all LeRobot policies on LIBERO.
-
-Each generated script trains one policy, evaluates it, and publishes its
-results row to a HuggingFace leaderboard dataset — no separate collection
-step needed.
-
-Usage:
-    # Generate scripts for all policies:
-    python benchmarks/libero/run_benchmark.py \\
-        --output_dir /scratch/lerobot-benchmark --hub_org lerobot
-
-    # Generate for a subset:
-    python benchmarks/libero/run_benchmark.py \\
-        --policies pi0 smolvla act \\
-        --output_dir /scratch/lerobot-benchmark --hub_org lerobot
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import subprocess
-import textwrap
-import uuid
-from dataclasses import dataclass, field
-from datetime import UTC, datetime
-from pathlib import Path
-
-# ──────────────────────────────────────────────────────────────────────
-# Policy benchmark configs
-# ──────────────────────────────────────────────────────────────────────
-
-
-@dataclass
-class PolicyBenchmarkConfig:
-    """Training configuration for a single policy on a benchmark."""
-
-    policy_type: str
-    policy_path: str | None = None
-    num_gpus: int = 1
-    chunk_size: int | None = None  # Set on policies that use chunk_size (not horizon)
-    extra_policy_args: dict[str, str] = field(default_factory=dict)
-    needs_tokenizer: bool = False
-    tokenizer_args: dict[str, str] = field(default_factory=dict)
-
-
-COMMON_TRAINING_ARGS: dict[str, str] = {
-    "dataset.repo_id": "lerobot/libero",
-    "dataset.use_imagenet_stats": "false",
-    "env.type": "libero",
-    "env.task": "libero_spatial",
-    "steps": "5000",
-    "batch_size": "32",
-    "eval_freq": "0",
-    "save_freq": "5000",
-    "save_checkpoint": "true",
-    "log_freq": "100",
-    "wandb.enable": "true",
-    "policy.push_to_hub": "true",
-    "rename_map": (
-        '{"observation.images.image":"observation.images.camera1",'
-        '"observation.images.image2":"observation.images.camera2"}'
-    ),
-}
-
-EVAL_ARGS: dict[str, str] = {
-    "env.type": "libero",
-    "env.task": "libero_spatial",
-    "eval.n_episodes": "20",
-    "eval.batch_size": "10",
-}
-
-POLICY_CONFIGS: dict[str, PolicyBenchmarkConfig] = {
-    "pi0": PolicyBenchmarkConfig(
-        policy_type="pi0",
-        policy_path="lerobot/pi0_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "pi0_fast": PolicyBenchmarkConfig(
-        policy_type="pi0_fast",
-        policy_path="lerobot/pi0fast-base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-        needs_tokenizer=True,
-        tokenizer_args={
-            "repo_id": "lerobot/libero",
-            "action_horizon": "30",
-            "encoded_dims": "0:7",
-            "normalization_mode": "QUANTILES",
-            "vocab_size": "1024",
-            "scale": "10.0",
-            "push_to_hub": "true",
-        },
-    ),
-    "pi05": PolicyBenchmarkConfig(
-        policy_type="pi05",
-        policy_path="lerobot/pi05_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "groot": PolicyBenchmarkConfig(
-        policy_type="groot",
-        policy_path=None,
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.base_model_path": "nvidia/GR00T-N1.5-3B",
-            "policy.tune_diffusion_model": "true",
-            "policy.tune_projector": "true",
-            "policy.tune_llm": "false",
-            "policy.tune_visual": "false",
-            "policy.use_bf16": "true",
-        },
-    ),
-    "act": PolicyBenchmarkConfig(
-        policy_type="act",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=30,
-        extra_policy_args={"policy.n_action_steps": "30"},
-    ),
-    "diffusion": PolicyBenchmarkConfig(
-        policy_type="diffusion",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=None,
-        extra_policy_args={
-            "policy.horizon": "32",
-            "policy.n_action_steps": "30",
-            "policy.n_obs_steps": "2",
-        },
-    ),
-    "smolvla": PolicyBenchmarkConfig(
-        policy_type="smolvla",
-        policy_path="lerobot/smolvla_base",
-        num_gpus=8,
-        chunk_size=30,
-        extra_policy_args={
-            "policy.n_action_steps": "30",
-            "policy.load_vlm_weights": "true",
-            "policy.freeze_vision_encoder": "false",
-            "policy.train_expert_only": "false",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "xvla": PolicyBenchmarkConfig(
-        policy_type="xvla",
-        policy_path="lerobot/xvla-widowx",
-        num_gpus=4,
-        chunk_size=32,
-        extra_policy_args={
-            "policy.n_action_steps": "32",
-            "policy.scheduler_decay_steps": "5000",
-        },
-    ),
-    "multi_task_dit": PolicyBenchmarkConfig(
-        policy_type="multi_task_dit",
-        policy_path=None,
-        num_gpus=1,
-        chunk_size=None,
-        extra_policy_args={
-            "policy.horizon": "32",
-            "policy.n_action_steps": "30",
-        },
-    ),
-}
-
-ALL_POLICY_NAMES = list(POLICY_CONFIGS.keys())
-
-# GPU memory estimates (GB) for SLURM --mem allocation
-GPU_MEM_ESTIMATES: dict[str, int] = {
-    "pi0": 320,
-    "pi0_fast": 320,
-    "pi05": 280,
-    "groot": 320,
-    "act": 64,
-    "diffusion": 64,
-    "smolvla": 160,
-    "xvla": 160,
-    "multi_task_dit": 64,
-}
-
-
-# ──────────────────────────────────────────────────────────────────────
-# SLURM script generation
-# ──────────────────────────────────────────────────────────────────────
-
-
-def _cli_args(args: dict[str, str]) -> str:
-    """Build a backslash-continued CLI arg string with proper shell quoting."""
-    lines = []
-    for key, value in args.items():
-        if any(c in str(value) for c in ["{", "}", " ", '"', "'"]):
-            lines.append(f"    --{key}='{value}'")
-        else:
-            lines.append(f"    --{key}={value}")
-    return " \\\n".join(lines)
-
-
-def _training_cli_args(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-) -> str:
-    cfg = POLICY_CONFIGS[policy_name]
-    args: dict[str, str] = {}
-    args.update(COMMON_TRAINING_ARGS)
-    args["policy.type"] = cfg.policy_type
-    if cfg.policy_path:
-        args["policy.path"] = cfg.policy_path
-    if cfg.chunk_size is not None:
-        args["policy.chunk_size"] = str(cfg.chunk_size)
-    args.update(cfg.extra_policy_args)
-    args["output_dir"] = str(output_dir / "train" / policy_name)
-    args["policy.repo_id"] = f"{hub_org}/{policy_name}_libero"
-    args["wandb.project"] = "lerobot-libero-benchmark"
-    args["wandb.run_name"] = f"{policy_name}_{benchmark_uuid[:8]}"
-    return _cli_args(args)
-
-
-def _publish_snippet(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-    hub_dataset: str,
-) -> str:
-    """Inline Python that each SLURM job runs to publish its own result row."""
-    cfg = POLICY_CONFIGS[policy_name]
-    steps = int(COMMON_TRAINING_ARGS["steps"])
-    bs = int(COMMON_TRAINING_ARGS["batch_size"])
-    eff_bs = bs * cfg.num_gpus
-    train_dir = output_dir / "train" / policy_name
-
-    return textwrap.dedent(f"""\
-        python3 -c "
-        import json, os, re, sys
-        from pathlib import Path
-        from datetime import datetime, timezone
-
-        timing = {{}}
-        tp = Path('{output_dir}/logs/{policy_name}_timing.txt')
-        if tp.exists():
-            for ln in tp.read_text().splitlines():
-                if '=' in ln:
-                    k, _, v = ln.partition('=')
-                    timing[k.strip()] = v.strip()
-
-        # Parse eval results
-        eval_sr, eval_per_task, eval_n = None, '{{}}', 0
-        eval_dir = Path('{train_dir}/eval_results')
-        if eval_dir.exists():
-            for jf in eval_dir.glob('**/*.json'):
-                try:
-                    d = json.loads(jf.read_text())
-                except Exception:
-                    continue
-                if 'avg_success_rate' in d:
-                    eval_sr = d['avg_success_rate']
-                elif 'eval_info' in d and 'avg_success_rate' in d.get('eval_info', {{}}):
-                    eval_sr = d['eval_info']['avg_success_rate']
-                pt = {{k: v for k, v in d.items() if 'success_rate' in k and k != 'avg_success_rate'}}
-                if pt:
-                    eval_per_task = json.dumps(pt)
-                if 'n_episodes' in d:
-                    eval_n = d['n_episodes']
-
-        # Parse final loss from SLURM stdout
-        final_loss = None
-        for lf in sorted(Path('{output_dir}/logs').glob('{policy_name}_*.out'), reverse=True):
-            losses = re.findall(r'\\\"loss\\\"\\s*:\\s*([\\d.e+-]+)', lf.read_text())
-            if losses:
-                final_loss = float(losses[-1])
-                break
-
-        # Parse peak GPU mem
-        peak_mem = 0.0
-        csv_p = Path('{output_dir}/logs/{policy_name}_gpu_mem.csv')
-        if csv_p.exists():
-            for ln in csv_p.read_text().splitlines():
-                parts = ln.strip().split(',')
-                if len(parts) >= 2:
-                    try:
-                        peak_mem = max(peak_mem, float(parts[1].strip()))
-                    except ValueError:
-                        pass
-
-        # Parse train config for optimizer details
-        lr, opt_wd, sched_type, sched_warmup, sched_decay = 0.0, 0.0, '', 0, 0
-        freeze_ve, train_eo, grad_ckpt = False, False, False
-        cfg_path = Path('{train_dir}/checkpoints/{steps:06d}/pretrained_model/train_config.json')
-        if cfg_path.exists():
-            tc = json.loads(cfg_path.read_text())
-            o = tc.get('optimizer', {{}})
-            lr = o.get('lr', 0.0)
-            opt_wd = o.get('weight_decay', 0.0)
-            s = tc.get('scheduler', {{}})
-            sched_type = s.get('type', '')
-            sched_warmup = s.get('num_warmup_steps', 0)
-            sched_decay = s.get('num_decay_steps', 0)
-            p = tc.get('policy', {{}})
-            freeze_ve = p.get('freeze_vision_encoder', False)
-            train_eo = p.get('train_expert_only', False)
-            grad_ckpt = p.get('gradient_checkpointing', False)
-
-        row = {{
-            'benchmark_uuid': '{benchmark_uuid}',
-            'policy_type': '{policy_name}',
-            'policy_repo_id': '{hub_org}/{policy_name}_libero',
-            'base_model_repo_id': '{cfg.policy_path or ""}',
-            'dataset_repo_id': '{COMMON_TRAINING_ARGS["dataset.repo_id"]}',
-            'env_type': '{COMMON_TRAINING_ARGS["env.type"]}',
-            'env_task': '{COMMON_TRAINING_ARGS["env.task"]}',
-            'steps': {steps},
-            'batch_size_per_gpu': {bs},
-            'num_gpus': {cfg.num_gpus},
-            'effective_batch_size': {eff_bs},
-            'total_samples_seen': {steps * eff_bs},
-            'chunk_size': {cfg.chunk_size or 0},
-            'learning_rate': lr,
-            'optimizer_type': 'AdamW',
-            'optimizer_weight_decay': opt_wd,
-            'scheduler_type': sched_type,
-            'scheduler_warmup_steps': sched_warmup,
-            'scheduler_decay_steps': sched_decay,
-            'freeze_vision_encoder': freeze_ve,
-            'train_expert_only': train_eo,
-            'gradient_checkpointing': grad_ckpt,
-            'eval_success_rate': eval_sr,
-            'eval_success_rate_per_task': eval_per_task,
-            'eval_n_episodes': eval_n,
-            'final_train_loss': final_loss,
-            'training_time_s': float(timing.get('TRAINING_TIME_S', 0)),
-            'peak_gpu_memory_mb': peak_mem or float(timing.get('MAX_GPU_MEM_MB', 0)),
-            'gpu_type': timing.get('GPU_TYPE', 'unknown'),
-            'lerobot_commit': timing.get('LEROBOT_COMMIT', 'unknown'),
-            'timestamp': datetime.now(timezone.utc).isoformat(),
-        }}
-
-        # Save locally
-        Path('{train_dir}/benchmark_result.json').write_text(json.dumps(row, indent=2, default=str))
-
-        # Push to HF dataset
-        try:
-            from datasets import Dataset, load_dataset
-            try:
-                existing = load_dataset('{hub_dataset}', split='train')
-                rows = existing.to_list() + [row]
-            except Exception:
-                rows = [row]
-            Dataset.from_list(rows).push_to_hub('{hub_dataset}', split='train')
-            print('Published result to {hub_dataset}')
-        except ImportError:
-            print('datasets library not installed — result saved locally only')
-        except Exception as e:
-            print(f'Failed to push to hub: {{e}} — result saved locally')
-        "
-    """)
-
-
-def _generate_sbatch_script(
-    policy_name: str,
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-    hub_dataset: str,
-    lerobot_commit: str,
-) -> str:
-    cfg = POLICY_CONFIGS[policy_name]
-    steps = int(COMMON_TRAINING_ARGS["steps"])
-    log_dir = output_dir / "logs"
-    train_dir = output_dir / "train" / policy_name
-    checkpoint_path = train_dir / f"checkpoints/{steps:06d}/pretrained_model"
-
-    training_args = _training_cli_args(policy_name, output_dir, hub_org, benchmark_uuid)
-    eval_args = _cli_args(EVAL_ARGS)
-    publish = _publish_snippet(policy_name, output_dir, hub_org, benchmark_uuid, hub_dataset)
-
-    return textwrap.dedent(f"""\
-        #!/bin/bash
-        #SBATCH --job-name=bench_{policy_name}
-        #SBATCH --nodes=1
-        #SBATCH --ntasks-per-node=1
-        #SBATCH --gres=gpu:{cfg.num_gpus}
-        #SBATCH --cpus-per-task={cfg.num_gpus * 8}
-        #SBATCH --mem={GPU_MEM_ESTIMATES.get(policy_name, 128)}G
-        #SBATCH --time=06:00:00
-        #SBATCH --output={log_dir}/{policy_name}_%j.out
-        #SBATCH --error={log_dir}/{policy_name}_%j.err
-
-        set -euo pipefail
-
-        echo "=========================================="
-        echo "LeRobot LIBERO Benchmark — {policy_name}"
-        echo "UUID: {benchmark_uuid}"
-        echo "Start: $(date -Iseconds)"
-        echo "Host: $(hostname) | GPUs: {cfg.num_gpus}"
-        echo "=========================================="
-
-        START_TIME=$(date +%s)
-
-        # GPU memory monitoring (every 30s)
-        nvidia-smi --query-gpu=index,memory.used,memory.total,gpu_name \\
-            --format=csv,noheader,nounits -l 30 \\
-            > "{log_dir}/{policy_name}_gpu_mem.csv" &
-        GPU_MONITOR_PID=$!
-
-        # ── Training ──────────────────────────────────────────────────
-        echo "[$(date -Iseconds)] Starting training..."
-        accelerate launch --num_processes={cfg.num_gpus} \\
-            $(which lerobot-train) \\
-        {training_args}
-        TRAIN_EXIT=$?
-        TRAIN_END=$(date +%s)
-        echo "[$(date -Iseconds)] Training exit code: $TRAIN_EXIT"
-
-        # ── Evaluation ────────────────────────────────────────────────
-        EVAL_EXIT=1
-        if [ $TRAIN_EXIT -eq 0 ]; then
-            echo "[$(date -Iseconds)] Starting evaluation..."
-            lerobot-eval \\
-                --policy.path="{checkpoint_path}" \\
-            {eval_args} \\
-                --output_dir="{train_dir}/eval_results"
-            EVAL_EXIT=$?
-            echo "[$(date -Iseconds)] Eval exit code: $EVAL_EXIT"
-        else
-            echo "[$(date -Iseconds)] Skipping eval — training failed."
-        fi
-
-        # ── Timing ────────────────────────────────────────────────────
-        END_TIME=$(date +%s)
-        kill $GPU_MONITOR_PID 2>/dev/null || true
-
-        cat > "{log_dir}/{policy_name}_timing.txt" <<TIMING_EOF
-        BENCHMARK_UUID={benchmark_uuid}
-        POLICY_TYPE={policy_name}
-        TRAINING_TIME_S=$((TRAIN_END - START_TIME))
-        TOTAL_TIME_S=$((END_TIME - START_TIME))
-        TRAIN_EXIT=$TRAIN_EXIT
-        EVAL_EXIT=$EVAL_EXIT
-        MAX_GPU_MEM_MB=$(awk -F',' '{{print $2}}' "{log_dir}/{policy_name}_gpu_mem.csv" 2>/dev/null | sort -n | tail -1)
-        GPU_TYPE=$(nvidia-smi --query-gpu=gpu_name --format=csv,noheader | head -1 | xargs)
-        LEROBOT_COMMIT={lerobot_commit}
-        TIMING_EOF
-
-        # ── Publish result to HF dataset ──────────────────────────────
-        echo "[$(date -Iseconds)] Publishing result..."
-        {publish}
-
-        echo "=========================================="
-        echo "Done: $(date -Iseconds)"
-        echo "Training: $((TRAIN_END - START_TIME))s | Total: $((END_TIME - START_TIME))s"
-        echo "=========================================="
-    """)
-
-
-def _generate_tokenizer_script(
-    output_dir: Path,
-    hub_org: str,
-    benchmark_uuid: str,
-) -> str:
-    cfg = POLICY_CONFIGS["pi0_fast"]
-    log_dir = output_dir / "logs"
-    tokenizer_hub_repo = f"{hub_org}/fast-tokenizer-libero"
-
-    tok_args = dict(cfg.tokenizer_args)
-    tok_args["hub_repo_id"] = tokenizer_hub_repo
-
-    return textwrap.dedent(f"""\
-        #!/bin/bash
-        #SBATCH --job-name=bench_tokenizer
-        #SBATCH --nodes=1
-        #SBATCH --ntasks-per-node=1
-        #SBATCH --gres=gpu:1
-        #SBATCH --cpus-per-task=8
-        #SBATCH --mem=64G
-        #SBATCH --time=01:00:00
-        #SBATCH --output={log_dir}/tokenizer_%j.out
-        #SBATCH --error={log_dir}/tokenizer_%j.err
-
-        set -euo pipefail
-        echo "LeRobot — FAST Tokenizer | UUID: {benchmark_uuid}"
-
-        lerobot-train-tokenizer \\
-        {_cli_args(tok_args)}
-
-        echo "Tokenizer pushed to: {tokenizer_hub_repo}"
-    """)
-
-
-# ──────────────────────────────────────────────────────────────────────
-# Main
-# ──────────────────────────────────────────────────────────────────────
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Generate SLURM scripts for LeRobot LIBERO benchmark.")
-    parser.add_argument(
-        "--policies",
-        nargs="+",
-        default=ALL_POLICY_NAMES,
-        choices=ALL_POLICY_NAMES,
-        help="Policies to benchmark (default: all).",
-    )
-    parser.add_argument("--output_dir", type=Path, required=True, help="Root output directory.")
-    parser.add_argument("--hub_org", type=str, default="lerobot", help="HuggingFace org.")
-    parser.add_argument("--hub_dataset", type=str, default=None, help="HF dataset repo for results.")
-    parser.add_argument("--uuid", type=str, default=None, help="Override benchmark UUID.")
-    args = parser.parse_args()
-
-    benchmark_uuid = args.uuid or str(uuid.uuid4())
-    output_dir: Path = args.output_dir.resolve()
-    policies: list[str] = args.policies
-    hub_org: str = args.hub_org
-    hub_dataset: str = args.hub_dataset or f"{hub_org}/benchmark-libero"
-
-    try:
-        commit = subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip()
-    except (subprocess.CalledProcessError, FileNotFoundError):
-        commit = "unknown"
-
-    scripts_dir = output_dir / "slurm_scripts"
-    log_dir = output_dir / "logs"
-    scripts_dir.mkdir(parents=True, exist_ok=True)
-    log_dir.mkdir(parents=True, exist_ok=True)
-    for p in policies:
-        (output_dir / "train" / p).mkdir(parents=True, exist_ok=True)
-
-    generated: dict[str, Path] = {}
-
-    # Tokenizer job for pi0_fast
-    tokenizer_path = None
-    if "pi0_fast" in policies:
-        script = _generate_tokenizer_script(output_dir, hub_org, benchmark_uuid)
-        tokenizer_path = scripts_dir / "00_tokenizer.sh"
-        tokenizer_path.write_text(script)
-        tokenizer_path.chmod(0o755)
-        generated["tokenizer"] = tokenizer_path
-        tokenizer_hub_repo = f"{hub_org}/fast-tokenizer-libero"
-        POLICY_CONFIGS["pi0_fast"].extra_policy_args["policy.action_tokenizer_name"] = tokenizer_hub_repo
-
-    # Per-policy scripts
-    for i, name in enumerate(sorted(policies), start=1):
-        script = _generate_sbatch_script(name, output_dir, hub_org, benchmark_uuid, hub_dataset, commit)
-        path = scripts_dir / f"{i:02d}_{name}.sh"
-        path.write_text(script)
-        path.chmod(0o755)
-        generated[name] = path
-
-    # Manifest
-    manifest = {
-        "benchmark_uuid": benchmark_uuid,
-        "timestamp": datetime.now(UTC).isoformat(),
-        "lerobot_commit": commit,
-        "hub_org": hub_org,
-        "hub_dataset": hub_dataset,
-        "policies": policies,
-        "output_dir": str(output_dir),
-        "scripts": {k: str(v) for k, v in generated.items()},
-    }
-    manifest_path = output_dir / "benchmark_manifest.json"
-    manifest_path.write_text(json.dumps(manifest, indent=2))
-
-    # Instructions
-    print("=" * 60)
-    print("LeRobot LIBERO Benchmark — Scripts Generated")
-    print(f"UUID: {benchmark_uuid}")
-    print(f"Output: {output_dir}")
-    print(f"Results dataset: {hub_dataset}")
-    print("=" * 60)
-    print()
-    for _name, path in sorted(generated.items()):
-        print(f"  {path}")
-    print()
-
-    if tokenizer_path:
-        print("IMPORTANT: pi0_fast requires tokenizer training FIRST.")
-        print(f"  1. sbatch {tokenizer_path}")
-        print("  2. Wait for completion")
-        print(f"  3. sbatch {generated.get('pi0_fast', 'N/A')}")
-        print("  4. All other policies can run in parallel")
-    else:
-        print("All scripts can be submitted in parallel.")
-    print()
-    print("Each job publishes its result to the HF dataset automatically.")
-
-
-if __name__ == "__main__":
-    main()
@@ -1,156 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Publish benchmark rows and lightweight artifacts to a Hub dataset."""
-
-from __future__ import annotations
-
-import argparse
-import json
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from lerobot.utils.history_repo import UploadTarget, make_hub_file_url, upload_targets, utc_timestamp_slug
-
-
-def load_json_if_exists(path: Path) -> dict[str, Any] | None:
-    if not path.exists():
-        return None
-    return json.loads(path.read_text())
-
-
-def find_latest_train_config_path(run_root: Path) -> Path | None:
-    checkpoints_dir = run_root / "train" / "checkpoints"
-    if not checkpoints_dir.exists():
-        return None
-    candidates = sorted(
-        checkpoints_dir.glob("*/pretrained_model/train_config.json"),
-        key=lambda path: path.parts[-3],
-    )
-    return candidates[-1] if candidates else None
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--benchmark", required=True)
-    parser.add_argument("--policy", required=True)
-    parser.add_argument("--run_root", required=True, type=Path)
-    parser.add_argument("--results_repo", required=True)
-    parser.add_argument("--git_commit", required=True)
-    parser.add_argument("--num_gpus", required=True, type=int)
-    parser.add_argument("--microbatch_per_gpu", required=True, type=int)
-    parser.add_argument("--gradient_accumulation_steps", required=True, type=int)
-    parser.add_argument("--effective_batch_size", required=True, type=int)
-    parser.add_argument("--train_wall_time_s", required=True, type=float)
-    parser.add_argument("--eval_wall_time_s", required=True, type=float)
-    parser.add_argument("--slurm_job_id", default="")
-    parser.add_argument("--docker_image", required=True)
-    return parser.parse_args()
-
-
-def build_row(args: argparse.Namespace) -> tuple[dict[str, Any], list[UploadTarget]]:
-    now = datetime.now(UTC)
-    created_at = now.isoformat()
-    timestamp = utc_timestamp_slug(now)
-    run_id = f"{timestamp}__{args.benchmark}__{args.policy}__{args.slurm_job_id or 'manual'}"
-    eval_info = load_json_if_exists(args.run_root / "eval" / "eval_info.json") or {}
-    train_config_path = find_latest_train_config_path(args.run_root)
-    train_config = load_json_if_exists(train_config_path) or {}
-
-    artifact_prefix = f"artifacts/{args.benchmark}/{args.policy}/{run_id}"
-    row_path_in_repo = f"rows/{args.benchmark}/{args.policy}/{run_id}.json"
-
-    row = {
-        "schema_version": 1,
-        "created_at": created_at,
-        "run_id": run_id,
-        "benchmark": args.benchmark,
-        "policy": args.policy,
-        "git_commit": args.git_commit,
-        "slurm_job_id": args.slurm_job_id or None,
-        "docker_image": args.docker_image,
-        "resources": {
-            "num_gpus": args.num_gpus,
-            "microbatch_per_gpu": args.microbatch_per_gpu,
-            "gradient_accumulation_steps": args.gradient_accumulation_steps,
-            "effective_batch_size": args.effective_batch_size,
-        },
-        "timings": {
-            "train_wall_time_s": args.train_wall_time_s,
-            "eval_wall_time_s": args.eval_wall_time_s,
-            "total_wall_time_s": args.train_wall_time_s + args.eval_wall_time_s,
-        },
-        "eval": {
-            "overall": eval_info.get("overall", {}),
-            "per_group": eval_info.get("per_group", {}),
-            "per_task_count": len(eval_info.get("per_task", [])),
-        },
-        "paths": {
-            "run_root": str(args.run_root),
-            "train_dir": str(args.run_root / "train"),
-            "eval_dir": str(args.run_root / "eval"),
-        },
-        "train_config": train_config,
-        "artifact_urls": {
-            "row": make_hub_file_url(args.results_repo, row_path_in_repo),
-        },
-    }
-
-    row_path = args.run_root / "benchmark_row.json"
-    row_path.parent.mkdir(parents=True, exist_ok=True)
-    upload_list = [UploadTarget(local_path=row_path, path_in_repo=row_path_in_repo)]
-
-    eval_info_path = args.run_root / "eval" / "eval_info.json"
-    if eval_info_path.exists():
-        row["artifact_urls"]["eval_info"] = make_hub_file_url(
-            args.results_repo, f"{artifact_prefix}/eval_info.json"
-        )
-        upload_list.append(
-            UploadTarget(local_path=eval_info_path, path_in_repo=f"{artifact_prefix}/eval_info.json")
-        )
-
-    if train_config_path is not None and train_config_path.exists():
-        row["artifact_urls"]["train_config"] = make_hub_file_url(
-            args.results_repo, f"{artifact_prefix}/train_config.json"
-        )
-        upload_list.append(
-            UploadTarget(local_path=train_config_path, path_in_repo=f"{artifact_prefix}/train_config.json")
-        )
-
-    row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-    return row, upload_list
-
-
-def main() -> int:
-    args = parse_args()
-    row, upload_list = build_row(args)
-    uploaded = upload_targets(
-        repo_id=args.results_repo,
-        targets=upload_list,
-        repo_type="dataset",
-        private=False,
-        commit_message=f"Add benchmark row {row['run_id']}",
-    )
-    row["uploaded_paths"] = uploaded
-    row_path = args.run_root / "benchmark_row.json"
-    row_path.write_text(json.dumps(row, indent=2, sort_keys=True))
-    print(json.dumps(row, indent=2, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
@@ -1,647 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Generate lightweight SLURM jobs for policy x benchmark benchmarking."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import subprocess
-from dataclasses import asdict, dataclass, field
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from lerobot.utils.history_repo import utc_timestamp_slug
-
-MAX_GPUS = 8
-MIN_GPUS = 1
-DEFAULT_STEPS = 20_000
-DEFAULT_EFFECTIVE_BATCH_SIZE = 256
-DEFAULT_MICROBATCH_PER_GPU = 32
-DEFAULT_EVAL_BATCH_SIZE = 1
-DEFAULT_CPUS_PER_GPU = 8
-DEFAULT_MEMORY_PER_GPU_GB = 40
-
-
-@dataclass(frozen=True)
-class BenchmarkSpec:
-    name: str
-    dataset_repo_id: str
-    docker_image: str
-    eval_env_type: str
-    eval_task: str
-    eval_n_episodes: int
-    train_steps: int = DEFAULT_STEPS
-    effective_batch_size: int = DEFAULT_EFFECTIVE_BATCH_SIZE
-    train_extra_args: dict[str, Any] = field(default_factory=dict)
-    eval_extra_args: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class PolicySpec:
-    name: str
-    policy_type: str
-    num_gpus: int
-    policy_path: str | None = None
-    microbatch_per_gpu: int = DEFAULT_MICROBATCH_PER_GPU
-    extra_train_args: dict[str, Any] = field(default_factory=dict)
-    extra_eval_args: dict[str, Any] = field(default_factory=dict)
-    needs_tokenizer: bool = False
-    tokenizer_args: dict[str, Any] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class PlannedJob:
-    benchmark: str
-    policy: str
-    run_rel: str
-    num_gpus: int
-    microbatch_per_gpu: int
-    gradient_accumulation_steps: int
-    effective_batch_size: int
-    docker_image: str
-    train_args: dict[str, Any]
-    eval_args: dict[str, Any]
-    tokenizer_args: dict[str, Any] | None
-    script_path: str
-
-
-BENCHMARKS: dict[str, BenchmarkSpec] = {
-    "libero_plus": BenchmarkSpec(
-        name="libero_plus",
-        dataset_repo_id="lerobot/libero_plus",
-        docker_image="lerobot-benchmark-libero-plus:latest",
-        eval_env_type="libero_plus",
-        eval_task="libero_spatial,libero_object,libero_goal,libero_10",
-        eval_n_episodes=10,
-        train_extra_args={
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.image2": "observation.images.camera2",
-            },
-        },
-        eval_extra_args={
-            "env.camera_name_mapping": {
-                "agentview_image": "camera1",
-                "robot0_eye_in_hand_image": "camera2",
-            },
-            "env.max_parallel_tasks": 1,
-            "eval.batch_size": DEFAULT_EVAL_BATCH_SIZE,
-            "eval.use_async_envs": False,
-            "eval.max_episodes_rendered": 0,
-            "policy.device": "cuda",
-        },
-    ),
-    "robomme": BenchmarkSpec(
-        name="robomme",
-        dataset_repo_id="lerobot/robomme",
-        docker_image="lerobot-benchmark-robomme:latest",
-        eval_env_type="robomme",
-        eval_task=(
-            "BinFill,PickXtimes,SwingXtimes,StopCube,VideoUnmask,VideoUnmaskSwap,"
-            "ButtonUnmask,ButtonUnmaskSwap,PickHighlight,VideoRepick,VideoPlaceButton,"
-            "VideoPlaceOrder,MoveCube,InsertPeg,PatternLock,RouteStick"
-        ),
-        eval_n_episodes=50,
-        train_extra_args={
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.wrist_image": "observation.images.camera2",
-            },
-        },
-        eval_extra_args={
-            "env.dataset_split": "test",
-            "env.max_parallel_tasks": 1,
-            "rename_map": {
-                "observation.images.image": "observation.images.camera1",
-                "observation.images.wrist_image": "observation.images.camera2",
-            },
-            "eval.batch_size": DEFAULT_EVAL_BATCH_SIZE,
-            "eval.use_async_envs": False,
-            "eval.max_episodes_rendered": 0,
-            "policy.device": "cuda",
-        },
-    ),
-}
-
-
-POLICIES: dict[str, PolicySpec] = {
-    "pi0": PolicySpec(
-        name="pi0",
-        policy_type="pi0",
-        policy_path="lerobot/pi0_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-    ),
-    "pi0_fast": PolicySpec(
-        name="pi0_fast",
-        policy_type="pi0_fast",
-        policy_path="lerobot/pi0fast-base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-        needs_tokenizer=True,
-        tokenizer_args={
-            "action_horizon": 30,
-            "encoded_dims": "0:7",
-            "normalization_mode": "QUANTILES",
-            "vocab_size": 1024,
-            "scale": 10.0,
-            "push_to_hub": True,
-        },
-    ),
-    "pi05": PolicySpec(
-        name="pi05",
-        policy_type="pi05",
-        policy_path="lerobot/pi05_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 0,
-        },
-    ),
-    "groot": PolicySpec(
-        name="groot",
-        policy_type="groot",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.base_model_path": "nvidia/GR00T-N1.5-3B",
-            "policy.tune_diffusion_model": True,
-            "policy.tune_projector": True,
-            "policy.tune_llm": False,
-            "policy.tune_visual": False,
-            "policy.use_bf16": True,
-        },
-    ),
-    "act": PolicySpec(
-        name="act",
-        policy_type="act",
-        num_gpus=1,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-        },
-    ),
-    "diffusion": PolicySpec(
-        name="diffusion",
-        policy_type="diffusion",
-        num_gpus=1,
-        extra_train_args={
-            "policy.horizon": 32,
-            "policy.n_action_steps": 30,
-            "policy.n_obs_steps": 2,
-        },
-    ),
-    "smolvla": PolicySpec(
-        name="smolvla",
-        policy_type="smolvla",
-        policy_path="lerobot/smolvla_base",
-        num_gpus=8,
-        extra_train_args={
-            "policy.n_action_steps": 30,
-            "policy.load_vlm_weights": True,
-            "policy.freeze_vision_encoder": False,
-            "policy.train_expert_only": False,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 1,
-        },
-    ),
-    "xvla": PolicySpec(
-        name="xvla",
-        policy_type="xvla",
-        policy_path="lerobot/xvla-widowx",
-        num_gpus=4,
-        extra_train_args={
-            "policy.n_action_steps": 32,
-            "policy.scheduler_decay_steps": DEFAULT_STEPS,
-            "policy.empty_cameras": 1,
-        },
-    ),
-    "multi_task_dit": PolicySpec(
-        name="multi_task_dit",
-        policy_type="multi_task_dit",
-        num_gpus=1,
-        extra_train_args={
-            "policy.horizon": 32,
-            "policy.n_action_steps": 30,
-        },
-    ),
-}
-
-
-def normalize_repo_id(hub_org: str, repo_or_id: str) -> str:
-    return repo_or_id if "/" in repo_or_id else f"{hub_org}/{repo_or_id}"
-
-
-def get_requested_names(
-    requested: list[str] | None,
-    available: dict[str, Any],
-    *,
-    kind: str,
-) -> list[str]:
-    if not requested:
-        return list(available)
-    unknown = sorted(set(requested) - set(available))
-    if unknown:
-        raise ValueError(f"Unknown {kind}: {', '.join(unknown)}. Available: {', '.join(available)}")
-    return requested
-
-
-def compute_gradient_accumulation_steps(
-    *,
-    effective_batch_size: int,
-    num_gpus: int,
-    microbatch_per_gpu: int,
-) -> int:
-    per_step_batch = num_gpus * microbatch_per_gpu
-    if effective_batch_size % per_step_batch != 0:
-        raise ValueError(
-            f"Cannot reach effective batch {effective_batch_size} with {num_gpus=} and "
-            f"{microbatch_per_gpu=}."
-        )
-    return effective_batch_size // per_step_batch
-
-
-def make_run_slug() -> str:
-    return utc_timestamp_slug()
-
-
-def shell_value(value: Any) -> str:
-    if isinstance(value, bool):
-        value = "true" if value else "false"
-    elif isinstance(value, (dict, list)):
-        value = json.dumps(value, sort_keys=True)
-    else:
-        value = str(value)
-    escaped = (
-        value.replace("\\", "\\\\")
-        .replace('"', '\\"')
-        .replace("$", "\\$")
-        .replace("`", "\\`")
-    )
-    return f'"{escaped}"'
-
-
-def format_cli_args(args: dict[str, Any]) -> str:
-    lines = []
-    for key, value in args.items():
-        lines.append(f"  --{key}={shell_value(value)}")
-    return " \\\n".join(lines)
-
-
-def build_train_args(
-    *,
-    benchmark: BenchmarkSpec,
-    policy: PolicySpec,
-    train_dir: str,
-    gradient_accumulation_steps: int,
-) -> dict[str, Any]:
-    args: dict[str, Any] = {
-        "dataset.repo_id": benchmark.dataset_repo_id,
-        "output_dir": train_dir,
-        "steps": benchmark.train_steps,
-        "batch_size": policy.microbatch_per_gpu,
-        "gradient_accumulation_steps": gradient_accumulation_steps,
-        "eval_freq": 0,
-        "save_freq": benchmark.train_steps,
-        "save_checkpoint": True,
-        "log_freq": 100,
-        "wandb.enable": False,
-        "policy.push_to_hub": False,
-        "policy.device": "cuda",
-    }
-    if policy.policy_path:
-        args["policy.path"] = policy.policy_path
-    else:
-        args["policy.type"] = policy.policy_type
-    args.update(benchmark.train_extra_args)
-    args.update(policy.extra_train_args)
-    return args
-
-
-def build_eval_args(
-    *,
-    benchmark: BenchmarkSpec,
-    policy: PolicySpec,
-    checkpoint_path: str,
-    eval_dir: str,
-) -> dict[str, Any]:
-    args: dict[str, Any] = {
-        "policy.path": checkpoint_path,
-        "env.type": benchmark.eval_env_type,
-        "env.task": benchmark.eval_task,
-        "eval.n_episodes": benchmark.eval_n_episodes,
-        "output_dir": eval_dir,
-    }
-    args.update(benchmark.eval_extra_args)
-    args.update(policy.extra_eval_args)
-    return args
-
-
-def plan_jobs(
-    *,
-    output_dir: Path,
-    hub_org: str,
-    results_repo: str,
-    policies: list[str],
-    benchmarks: list[str],
-) -> list[PlannedJob]:
-    _ = hub_org
-    _ = results_repo
-    scripts_dir = output_dir / "slurm"
-    jobs: list[PlannedJob] = []
-    for benchmark_name in benchmarks:
-        benchmark = BENCHMARKS[benchmark_name]
-        for policy_name in policies:
-            policy = POLICIES[policy_name]
-            num_gpus = max(MIN_GPUS, min(policy.num_gpus, MAX_GPUS))
-            run_rel = f"runs/{benchmark_name}/{policy_name}/{make_run_slug()}"
-            run_root = f"/benchmark-output/{run_rel}"
-            gradient_accumulation_steps = compute_gradient_accumulation_steps(
-                effective_batch_size=benchmark.effective_batch_size,
-                num_gpus=num_gpus,
-                microbatch_per_gpu=policy.microbatch_per_gpu,
-            )
-            train_dir = f"{run_root}/train"
-            checkpoint_path = f"{train_dir}/checkpoints/{benchmark.train_steps:06d}/pretrained_model"
-            eval_dir = f"{run_root}/eval"
-            train_args = build_train_args(
-                benchmark=benchmark,
-                policy=policy,
-                train_dir=train_dir,
-                gradient_accumulation_steps=gradient_accumulation_steps,
-            )
-            eval_args = build_eval_args(
-                benchmark=benchmark,
-                policy=policy,
-                checkpoint_path=checkpoint_path,
-                eval_dir=eval_dir,
-            )
-            tokenizer_args = None
-            if policy.needs_tokenizer:
-                tokenizer_repo_id = f"{hub_org}/{policy_name}-{benchmark_name}-tokenizer"
-                tokenizer_args = {
-                    "repo_id": benchmark.dataset_repo_id,
-                    "output_dir": f"{run_root}/tokenizer",
-                    "hub_repo_id": tokenizer_repo_id,
-                    **policy.tokenizer_args,
-                }
-                train_args["policy.action_tokenizer_name"] = tokenizer_repo_id
-            script_path = str(scripts_dir / f"{benchmark_name}__{policy_name}.sbatch")
-            jobs.append(
-                PlannedJob(
-                    benchmark=benchmark_name,
-                    policy=policy_name,
-                    run_rel=run_rel,
-                    num_gpus=num_gpus,
-                    microbatch_per_gpu=policy.microbatch_per_gpu,
-                    gradient_accumulation_steps=gradient_accumulation_steps,
-                    effective_batch_size=benchmark.effective_batch_size,
-                    docker_image=benchmark.docker_image,
-                    train_args=train_args,
-                    eval_args=eval_args,
-                    tokenizer_args=tokenizer_args,
-                    script_path=script_path,
-                )
-            )
-    return jobs
-
-
-def render_sbatch_script(
-    *,
-    job: PlannedJob,
-    output_dir: Path,
-    results_repo_id: str,
-    git_commit: str,
-) -> str:
-    host_output_dir = output_dir.resolve()
-    run_root = f"/benchmark-output/{job.run_rel}"
-    host_run_root = host_output_dir / job.run_rel
-    cpus_per_task = max(DEFAULT_CPUS_PER_GPU, DEFAULT_CPUS_PER_GPU * job.num_gpus)
-    mem_gb = max(DEFAULT_MEMORY_PER_GPU_GB, DEFAULT_MEMORY_PER_GPU_GB * job.num_gpus)
-    gpu_ids_expr = "${GPU_IDS}"
-    train_cli = format_cli_args(job.train_args)
-    eval_cli = format_cli_args(job.eval_args)
-    tokenizer_command = ""
-    if job.tokenizer_args:
-        tokenizer_cli = format_cli_args(job.tokenizer_args)
-        tokenizer_command = f"""
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES={gpu_ids_expr} \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    lerobot-train-tokenizer \\
-{tokenizer_cli}
-  '
-"""
-    return f"""#!/bin/bash
-#SBATCH --job-name=bench-{job.benchmark}-{job.policy}
-#SBATCH --gres=gpu:{job.num_gpus}
-#SBATCH --cpus-per-task={cpus_per_task}
-#SBATCH --mem={mem_gb}G
-#SBATCH --output={output_dir.resolve()}/logs/{job.benchmark}__{job.policy}__%j.out
-#SBATCH --error={output_dir.resolve()}/logs/{job.benchmark}__{job.policy}__%j.err
-
-set -euo pipefail
-
-HF_TOKEN="${{HF_TOKEN:-${{HF_USER_TOKEN:-}}}}"
-GPU_IDS="$(seq -s, 0 $(({job.num_gpus} - 1)))"
-RUN_ROOT="{run_root}"
-
-mkdir -p "{host_output_dir}/logs"
-mkdir -p "{host_run_root.parent}"
-
-{tokenizer_command}
-
-TRAIN_START="$(date +%s)"
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    accelerate launch --num_processes={job.num_gpus} $(which lerobot-train) \\
-{train_cli}
-  '
-TRAIN_END="$(date +%s)"
-
-EVAL_START="$(date +%s)"
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    lerobot-eval \\
-{eval_cli}
-  '
-EVAL_END="$(date +%s)"
-TRAIN_WALL_TIME_S="$((TRAIN_END - TRAIN_START))"
-EVAL_WALL_TIME_S="$((EVAL_END - EVAL_START))"
-
-docker run --rm --gpus all \\
-  --shm-size=16g \\
-  -e CUDA_VISIBLE_DEVICES="${{GPU_IDS}}" \\
-  -e HF_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_USER_TOKEN="${{HF_TOKEN:-}}" \\
-  -e HF_HOME=/tmp/hf \\
-  -e RUN_ROOT="${{RUN_ROOT}}" \\
-  -e TRAIN_WALL_TIME_S="${{TRAIN_WALL_TIME_S}}" \\
-  -e EVAL_WALL_TIME_S="${{EVAL_WALL_TIME_S}}" \\
-  -v "{host_output_dir}:/benchmark-output" \\
-  -w /lerobot \\
-  "{job.docker_image}" \\
-  bash -lc '
-    set -euo pipefail
-    if [[ -n "${{HF_TOKEN:-}}" ]]; then
-      hf auth login --token "${{HF_TOKEN}}" --add-to-git-credential 2>/dev/null || true
-    fi
-    uv run python benchmarks/publish_benchmark_result.py \\
-      --benchmark={job.benchmark} \\
-      --policy={job.policy} \\
-      --run_root="${{RUN_ROOT}}" \\
-      --results_repo={results_repo_id} \\
-      --git_commit={git_commit} \\
-      --num_gpus={job.num_gpus} \\
-      --microbatch_per_gpu={job.microbatch_per_gpu} \\
-      --gradient_accumulation_steps={job.gradient_accumulation_steps} \\
-      --effective_batch_size={job.effective_batch_size} \\
-      --train_wall_time_s="${{TRAIN_WALL_TIME_S}}" \\
-      --eval_wall_time_s="${{EVAL_WALL_TIME_S}}" \\
-      --slurm_job_id="${{SLURM_JOB_ID:-}}" \\
-      --docker_image={job.docker_image}
-  '
-"""
-
-
-def write_manifest(
-    *,
-    output_dir: Path,
-    jobs: list[PlannedJob],
-    git_commit: str,
-    hub_org: str,
-    results_repo: str,
-) -> Path:
-    manifest = {
-        "generated_at": datetime.now(UTC).isoformat(),
-        "git_commit": git_commit,
-        "hub_org": hub_org,
-        "results_repo": results_repo,
-        "jobs": [asdict(job) for job in jobs],
-    }
-    manifest_path = output_dir / "manifest.json"
-    manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=True))
-    return manifest_path
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--policies", nargs="*", default=None)
-    parser.add_argument("--benchmarks", nargs="*", default=None)
-    parser.add_argument("--output_dir", required=True, type=Path)
-    parser.add_argument("--hub_org", required=True)
-    parser.add_argument("--results_repo", required=True)
-    parser.add_argument("--submit", action="store_true")
-    return parser.parse_args()
-
-
-def get_git_commit() -> str:
-    return subprocess.check_output(["git", "rev-parse", "HEAD"], text=True).strip()
-
-
-def main() -> int:
-    args = parse_args()
-    args.output_dir.mkdir(parents=True, exist_ok=True)
-    (args.output_dir / "slurm").mkdir(parents=True, exist_ok=True)
-    (args.output_dir / "logs").mkdir(parents=True, exist_ok=True)
-
-    selected_policies = get_requested_names(args.policies, POLICIES, kind="policies")
-    selected_benchmarks = get_requested_names(args.benchmarks, BENCHMARKS, kind="benchmarks")
-    git_commit = get_git_commit()
-    results_repo_id = normalize_repo_id(args.hub_org, args.results_repo)
-
-    jobs = plan_jobs(
-        output_dir=args.output_dir,
-        hub_org=args.hub_org,
-        results_repo=results_repo_id,
-        policies=selected_policies,
-        benchmarks=selected_benchmarks,
-    )
-
-    for job in jobs:
-        script = render_sbatch_script(
-            job=job,
-            output_dir=args.output_dir,
-            results_repo_id=results_repo_id,
-            git_commit=git_commit,
-        )
-        script_path = Path(job.script_path)
-        script_path.write_text(script)
-        script_path.chmod(0o755)
-        if args.submit:
-            subprocess.run(["sbatch", str(script_path)], check=True)
-
-    manifest_path = write_manifest(
-        output_dir=args.output_dir,
-        jobs=jobs,
-        git_commit=git_commit,
-        hub_org=args.hub_org,
-        results_repo=results_repo_id,
-    )
-    print(f"Wrote {len(jobs)} benchmark jobs to {args.output_dir}")
-    print(f"Manifest: {manifest_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
@@ -1,42 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for LIBERO integration tests.
-# Extends the nightly GPU image (which already has all extras installed)
-# with the PR's source code and LIBERO-specific asset setup.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.libero -t lerobot-benchmark-libero .
-# Run:    docker run --gpus all --rm lerobot-benchmark-libero lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# Pre-download lerobot/libero-assets from HF Hub so nothing is fetched at
-# runtime (which times out on CI). Point the libero config at the cached path.
-# libero/libero/__init__.py calls input() when ~/.libero/config.yaml is missing,
-# so we write the config before any libero import can happen.
-RUN LIBERO_DIR=$(python -c \
-      "import importlib.util, os; s=importlib.util.find_spec('libero'); \
-       print(os.path.join(os.path.dirname(s.origin), 'libero'))") && \
-    mkdir -p /home/user_lerobot/.libero && \
-    python -c "\
-from huggingface_hub import snapshot_download; \
-snapshot_download(repo_id='lerobot/libero-assets', repo_type='dataset', \
-                  local_dir='/home/user_lerobot/.libero/assets')" && \
-    printf "assets: /home/user_lerobot/.libero/assets\nbddl_files: ${LIBERO_DIR}/bddl_files\ndatasets: ${LIBERO_DIR}/../datasets\ninit_states: ${LIBERO_DIR}/init_files\n" \
-    > /home/user_lerobot/.libero/config.yaml
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]
@@ -1,48 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-FROM huggingface/lerobot-gpu:latest
-
-USER root
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-         unzip libexpat1 libfontconfig1-dev libmagickwand-dev \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-USER user_lerobot
-
-RUN uv pip install --no-cache \
-        "robosuite==1.4.1" bddl easydict mujoco matplotlib wand scikit-image gym
-
-ENV LIBERO_PLUS_ROOT=/home/user_lerobot/libero-plus/libero/libero
-RUN git clone --depth=1 https://github.com/sylvestf/LIBERO-plus.git /home/user_lerobot/libero-plus \
-    && cd /home/user_lerobot/libero-plus && uv pip install --no-cache --no-deps -e "." \
-    && uv pip uninstall hf-libero 2>/dev/null || true
-ENV PYTHONPATH="/home/user_lerobot/libero-plus:${PYTHONPATH}"
-
-RUN python -c "\
-from huggingface_hub import hf_hub_download; \
-hf_hub_download(repo_id='Sylvest/LIBERO-plus', repo_type='dataset', \
-                filename='assets.zip', local_dir='/tmp/libero-plus-dl')" \
-    && unzip -q /tmp/libero-plus-dl/assets.zip -d /tmp/libero-plus-dl/extract \
-    && mv /tmp/libero-plus-dl/extract/inspire/hdd/project/embodied-multimodality/public/syfei/libero_new/release/dataset/LIBERO-plus-0/assets \
-          ${LIBERO_PLUS_ROOT}/assets \
-    && rm -rf /tmp/libero-plus-dl
-
-RUN mkdir -p /home/user_lerobot/.libero \
-    && printf "assets: ${LIBERO_PLUS_ROOT}/assets\nbddl_files: ${LIBERO_PLUS_ROOT}/bddl_files\ndatasets: ${LIBERO_PLUS_ROOT}/../datasets\ninit_states: ${LIBERO_PLUS_ROOT}/init_files\n" \
-       > /home/user_lerobot/.libero/config.yaml
-
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]
@@ -1,27 +0,0 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Benchmark image for MetaWorld integration tests.
-# Extends the nightly GPU image (which already has all extras installed)
-# with the PR's source code.
-#
-# Build:  docker build -f docker/Dockerfile.benchmark.metaworld -t lerobot-benchmark-metaworld .
-# Run:    docker run --gpus all --rm lerobot-benchmark-metaworld lerobot-eval ...
-
-FROM huggingface/lerobot-gpu:latest
-
-# Overlay the PR's source code on top of the nightly image.
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]
@@ -1,39 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-FROM huggingface/lerobot-gpu:latest
-
-ENV NVIDIA_DRIVER_CAPABILITIES=all \
-    VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json
-
-USER root
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends \
-         libvulkan1 libvulkan-dev mesa-vulkan-drivers \
-    && mkdir -p /usr/share/vulkan/icd.d \
-    && echo '{"file_format_version":"1.0.0","ICD":{"library_path":"libGLX_nvidia.so.0","api_version":"1.3.0"}}' \
-       > /usr/share/vulkan/icd.d/nvidia_icd.json \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-USER user_lerobot
-
-COPY --chown=user_lerobot:user_lerobot setup.py pyproject.toml uv.lock README.md MANIFEST.in ./
-RUN printf 'gymnasium==0.29.1\nnumpy==1.26.4\n' > /tmp/robomme_override.txt \
-    && uv pip install --no-cache --override /tmp/robomme_override.txt \
-         -e ".[smolvla,av-dep]" \
-         "robomme @ git+https://github.com/RoboMME/robomme_benchmark.git@main" \
-    && python -c "import robomme; print('robomme import OK')"
-
-COPY --chown=user_lerobot:user_lerobot . .
-
-CMD ["/bin/bash"]
@@ -5,7 +5,7 @@
 #    pip-compile --output-file=requirements-macos.txt requirements.in
 #
 -e .[all]
-    # via -[all]
+    # via -r requirements.in
 absl-py==2.4.0
    # via
    #   dm-control
@@ -13,13 +13,14 @@ absl-py==2.4.0
    #   dm-tree
    #   labmaze
    #   mujoco
+    #   tensorboard
 accelerate==1.13.0
    # via
    #   lerobot
    #   peft
 aiohappyeyeballs==2.6.1
    # via aiohttp
-aiohttp==3.13.3
+aiohttp==3.13.4
    # via fsspec
 aiosignal==1.4.0
    # via aiohttp
@@ -29,6 +30,10 @@ annotated-doc==0.0.4
    #   typer
 annotated-types==0.7.0
    # via pydantic
+antlr4-python3-runtime==4.9.3
+    # via
+    #   hydra-core
+    #   omegaconf
 anyio==4.12.1
    # via
    #   httpx
@@ -41,11 +46,15 @@ attrs==25.4.0
    #   aiohttp
    #   dm-tree
    #   jsonlines
+    #   jsonschema
+    #   referencing
    #   rerun-sdk
 av==15.1.0
    # via
    #   lerobot
    #   qwen-vl-utils
+bddl==1.0.1
+    # via hf-libero
 certifi==2026.2.25
    # via
    #   httpcore
@@ -64,7 +73,9 @@ click==8.3.1
    #   uvicorn
    #   wandb
 cloudpickle==3.1.2
-    # via gymnasium
+    # via
+    #   gymnasium
+    #   hf-libero
 cmake==4.1.3
    # via lerobot
 cmeel==0.59.0
@@ -109,8 +120,12 @@ contourpy==1.3.3
    # via
    #   lerobot
    #   matplotlib
-coverage[toml]==7.13.4
+coverage==7.13.4
    # via pytest-cov
+cuda-bindings==12.9.4
+    # via torch
+cuda-pathfinder==1.5.2
+    # via cuda-bindings
 cycler==0.12.1
    # via matplotlib
 datasets==4.6.1
@@ -143,14 +158,22 @@ draccus==0.10.0
    # via lerobot
 dynamixel-sdk==3.8.4
    # via lerobot
+easydict==1.13
+    # via hf-libero
+egl-probe==1.0.2
+    # via robomimic
 eigenpy==3.10.3
    # via coal-library
 einops==0.8.2
-    # via lerobot
+    # via
+    #   hf-libero
+    #   lerobot
 eiquadprog==1.2.9
    # via placo
-etils[epath,epy]==1.14.0
+etils==1.14.0
    # via mujoco
+evdev==1.9.3
+    # via pynput
 executing==2.2.1
    # via stack-data
 faker==34.0.2
@@ -161,6 +184,8 @@ fastapi==0.135.1
    # via
    #   lerobot
    #   teleop
+fastjsonschema==2.21.2
+    # via nbformat
 feetech-servo-sdk==1.0.0
    # via lerobot
 filelock==3.25.0
@@ -177,12 +202,14 @@ frozenlist==1.8.0
    # via
    #   aiohttp
    #   aiosignal
-fsspec[http]==2026.2.0
+fsspec==2026.2.0
    # via
    #   datasets
    #   etils
    #   huggingface-hub
    #   torch
+future==1.0.0
+    # via hf-libero
 gitdb==4.0.12
    # via gitpython
 gitpython==3.1.46
@@ -197,6 +224,7 @@ grpcio==1.73.1
    #   lerobot
    #   reachy2-sdk
    #   reachy2-sdk-api
+    #   tensorboard
 grpcio-tools==1.73.1
    # via
    #   lerobot
@@ -212,14 +240,21 @@ gymnasium==1.2.3
    #   gym-aloha
    #   gym-hil
    #   gym-pusht
+    #   hf-libero
    #   lerobot
    #   metaworld
 h11==0.16.0
    # via
    #   httpcore
    #   uvicorn
+h5py==3.16.0
+    # via robomimic
 hebi-py==2.11.0
    # via lerobot
+hf-egl-probe==1.0.2
+    # via hf-libero
+hf-libero==0.1.3
+    # via lerobot
 hf-xet==1.3.2
    # via huggingface-hub
 hidapi==0.14.0.post4
@@ -243,6 +278,8 @@ huggingface-hub==1.6.0
    #   peft
    #   tokenizers
    #   transformers
+hydra-core==1.3.2
+    # via hf-libero
 identify==2.6.17
    # via pre-commit
 idna==3.11
@@ -251,15 +288,17 @@ idna==3.11
    #   httpx
    #   requests
    #   yarl
-imageio[ffmpeg]==2.37.2
+imageio==2.37.2
    # via
    #   gym-aloha
    #   gym-hil
-    #   lerobot
    #   metaworld
+    #   robomimic
    #   scikit-image
 imageio-ffmpeg==0.6.0
-    # via imageio
+    # via
+    #   imageio
+    #   robomimic
 importlib-metadata==8.7.1
    # via diffusers
 iniconfig==2.3.0
@@ -276,6 +315,14 @@ jinja2==3.1.6
    # via torch
 jsonlines==4.0.0
    # via lerobot
+jsonschema==4.26.0
+    # via nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-core==5.9.1
+    # via nbformat
+jupytext==1.19.1
+    # via bddl
 kiwisolver==1.4.9
    # via matplotlib
 labmaze==1.0.6
@@ -284,16 +331,29 @@ lazy-loader==0.5
    # via scikit-image
 librt==0.8.1
    # via mypy
+llvmlite==0.47.0
+    # via numba
 lxml==6.0.2
    # via dm-control
+markdown==3.10.2
+    # via tensorboard
 markdown-it-py==4.0.0
-    # via rich
+    # via
+    #   jupytext
+    #   mdit-py-plugins
+    #   rich
 markupsafe==3.0.3
-    # via jinja2
+    # via
+    #   jinja2
+    #   werkzeug
 matplotlib==3.10.8
-    # via lerobot
+    # via
+    #   hf-libero
+    #   lerobot
 matplotlib-inline==0.2.1
    # via ipython
+mdit-py-plugins==0.5.0
+    # via jupytext
 mdurl==0.1.2
    # via markdown-it-py
 mergedeep==1.3.4
@@ -311,7 +371,9 @@ mujoco==3.5.0
    #   dm-control
    #   gym-aloha
    #   gym-hil
+    #   hf-libero
    #   metaworld
+    #   robosuite
 multidict==6.7.1
    # via
    #   aiohttp
@@ -324,17 +386,23 @@ mypy-extensions==1.1.0
    # via
    #   mypy
    #   typing-inspect
+nbformat==5.10.4
+    # via jupytext
 networkx==3.6.1
    # via
+    #   bddl
    #   scikit-image
    #   torch
 nodeenv==1.10.0
    # via pre-commit
 num2words==0.5.14
    # via lerobot
+numba==0.65.0
+    # via robosuite
 numpy==2.2.6
    # via
    #   accelerate
+    #   bddl
    #   cmeel-boost
    #   contourpy
    #   datasets
@@ -343,7 +411,9 @@ numpy==2.2.6
    #   dm-env
    #   dm-tree
    #   gymnasium
+    #   h5py
    #   hebi-py
+    #   hf-libero
    #   imageio
    #   labmaze
    #   lerobot
@@ -351,6 +421,7 @@ numpy==2.2.6
    #   meshcat
    #   metaworld
    #   mujoco
+    #   numba
    #   opencv-python
    #   opencv-python-headless
    #   pandas
@@ -358,18 +429,65 @@ numpy==2.2.6
    #   pyquaternion
    #   reachy2-sdk
    #   rerun-sdk
+    #   robomimic
+    #   robosuite
    #   scikit-image
    #   scipy
    #   shapely
    #   teleop
+    #   tensorboard
+    #   tensorboardx
    #   tifffile
    #   torchvision
    #   transformers
    #   transforms3d
+nvidia-cublas-cu12==12.8.4.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.8.90
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.8.93
+    # via torch
+nvidia-cuda-runtime-cu12==12.8.90
+    # via torch
+nvidia-cudnn-cu12==9.10.2.21
+    # via torch
+nvidia-cufft-cu12==11.3.3.83
+    # via torch
+nvidia-cufile-cu12==1.13.1.3
+    # via torch
+nvidia-curand-cu12==10.3.9.90
+    # via torch
+nvidia-cusolver-cu12==11.7.3.90
+    # via torch
+nvidia-cusparse-cu12==12.5.8.93
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.7.1
+    # via torch
+nvidia-nccl-cu12==2.27.5
+    # via torch
+nvidia-nvjitlink-cu12==12.8.93
+    # via
+    #   nvidia-cufft-cu12
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvshmem-cu12==3.4.5
+    # via torch
+nvidia-nvtx-cu12==12.8.90
+    # via torch
+omegaconf==2.3.0
+    # via hydra-core
 opencv-python==4.13.0.92
    # via
    #   gym-pusht
+    #   hf-libero
    #   reachy2-sdk
+    #   robosuite
 opencv-python-headless==4.12.0.88
    # via lerobot
 orderly-set==5.5.0
@@ -379,14 +497,19 @@ packaging==25.0
    #   accelerate
    #   datasets
    #   huggingface-hub
+    #   hydra-core
+    #   jupytext
    #   lazy-loader
    #   lerobot
    #   matplotlib
    #   peft
    #   pytest
+    #   python-can
    #   qwen-vl-utils
    #   reachy2-sdk
    #   scikit-image
+    #   tensorboard
+    #   tensorboardx
    #   transformers
    #   wandb
 pandas==2.3.3
@@ -405,11 +528,14 @@ pillow==12.1.1
    # via
    #   diffusers
    #   imageio
+    #   lerobot
    #   matplotlib
    #   meshcat
    #   qwen-vl-utils
    #   rerun-sdk
+    #   robosuite
    #   scikit-image
+    #   tensorboard
    #   torchvision
 pin==3.4.0
    # via placo
@@ -417,6 +543,7 @@ placo==0.9.16
    # via lerobot
 platformdirs==4.9.4
    # via
+    #   jupyter-core
    #   python-discovery
    #   virtualenv
    #   wandb
@@ -439,12 +566,15 @@ protobuf==6.31.1
    #   lerobot
    #   reachy2-sdk
    #   reachy2-sdk-api
+    #   tensorboard
+    #   tensorboardx
    #   wandb
 psutil==7.2.2
    # via
    #   accelerate
    #   imageio
    #   peft
+    #   robomimic
 ptyprocess==0.7.0
    # via pexpect
 pure-eval==0.2.3
@@ -452,12 +582,14 @@ pure-eval==0.2.3
 pyarrow==23.0.1
    # via
    #   datasets
+    #   lerobot
    #   rerun-sdk
 pycparser==3.0
    # via cffi
 pydantic==2.12.5
    # via
    #   fastapi
+    #   lerobot
    #   wandb
 pydantic-core==2.41.5
    # via pydantic
@@ -482,26 +614,6 @@ pynput==1.8.1
    # via
    #   gym-hil
    #   lerobot
-pyobjc-core==12.1
-    # via
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-cocoa
-    #   pyobjc-framework-coretext
-    #   pyobjc-framework-quartz
-pyobjc-framework-applicationservices==12.1
-    # via pynput
-pyobjc-framework-cocoa==12.1
-    # via
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-coretext
-    #   pyobjc-framework-quartz
-pyobjc-framework-coretext==12.1
-    # via pyobjc-framework-applicationservices
-pyobjc-framework-quartz==12.1
-    # via
-    #   pynput
-    #   pyobjc-framework-applicationservices
-    #   pyobjc-framework-coretext
 pyopengl==3.1.10
    # via
    #   dm-control
@@ -512,7 +624,7 @@ pyparsing==3.3.2
    #   matplotlib
 pyquaternion==0.9.9
    # via reachy2-sdk
-pyrealsense2-macosx==2.56.5
+pyrealsense2==2.56.5.9235
    # via lerobot
 pyserial==3.5
    # via
@@ -521,6 +633,7 @@ pyserial==3.5
    #   lerobot
 pytest==8.4.2
    # via
+    #   bddl
    #   lerobot
    #   pytest-cov
    #   pytest-timeout
@@ -529,6 +642,8 @@ pytest-cov==7.0.0
    # via lerobot
 pytest-timeout==2.4.0
    # via lerobot
+python-can==4.6.1
+    # via lerobot
 python-dateutil==2.9.0.post0
    # via
    #   faker
@@ -538,6 +653,8 @@ python-discovery==1.1.1
    # via virtualenv
 python-dotenv==1.2.2
    # via uvicorn
+python-xlib==0.33
+    # via pynput
 pytz==2026.1.post1
    # via pandas
 pyyaml==6.0.3
@@ -547,6 +664,8 @@ pyyaml==6.0.3
    #   draccus
    #   hebi-py
    #   huggingface-hub
+    #   jupytext
+    #   omegaconf
    #   peft
    #   pre-commit
    #   pyngrok
@@ -566,6 +685,10 @@ reachy2-sdk==1.0.15
    # via lerobot
 reachy2-sdk-api==1.0.21
    # via reachy2-sdk
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
 regex==2026.2.28
    # via
    #   diffusers
@@ -575,6 +698,7 @@ requests==2.32.5
    #   datasets
    #   diffusers
    #   dm-control
+    #   lerobot
    #   qwen-vl-utils
    #   teleop
    #   wandb
@@ -584,6 +708,16 @@ rhoban-cmeel-jsoncpp==1.9.4.9
    # via placo
 rich==14.3.3
    # via typer
+robomimic==0.2.0
+    # via hf-libero
+robosuite==1.4.0
+    # via hf-libero
+rpds-py==0.30.0
+    # via
+    #   jsonschema
+    #   referencing
+ruff==0.15.10
+    # via lerobot
 safetensors==0.7.0
    # via
    #   accelerate
@@ -600,10 +734,21 @@ scipy==1.17.1
    #   dm-control
    #   lerobot
    #   metaworld
+    #   robosuite
    #   scikit-image
    #   torchdiffeq
 sentry-sdk==2.54.0
    # via wandb
+setuptools==80.10.2
+    # via
+    #   dm-control
+    #   grpcio-tools
+    #   hebi-py
+    #   hidapi
+    #   labmaze
+    #   lerobot
+    #   tensorboard
+    #   torch
 shapely==2.1.2
    # via gym-pusht
 shellingham==1.5.4
@@ -612,6 +757,7 @@ six==1.17.0
    # via
    #   pynput
    #   python-dateutil
+    #   python-xlib
 smmap==5.0.3
    # via gitdb
 stack-data==0.6.3
@@ -622,8 +768,18 @@ sympy==1.14.0
    # via torch
 teleop==0.1.4
    # via lerobot
+tensorboard==2.20.0
+    # via robomimic
+tensorboard-data-server==0.7.2
+    # via tensorboard
+tensorboardx==2.6.5
+    # via robomimic
 termcolor==3.3.0
-    # via lerobot
+    # via
+    #   lerobot
+    #   robomimic
+thop==0.1.1.post2209072238
+    # via hf-libero
 tifffile==2026.3.3
    # via scikit-image
 tokenizers==0.22.2
@@ -635,6 +791,8 @@ torch==2.10.0
    #   accelerate
    #   lerobot
    #   peft
+    #   robomimic
+    #   thop
    #   torchdiffeq
    #   torchvision
 torchcodec==0.10.0
@@ -642,7 +800,9 @@ torchcodec==0.10.0
 torchdiffeq==0.2.5
    # via lerobot
 torchvision==0.25.0
-    # via lerobot
+    # via
+    #   lerobot
+    #   robomimic
 tornado==6.5.4
    # via meshcat
 tqdm==4.67.3
@@ -650,18 +810,25 @@ tqdm==4.67.3
    #   datasets
    #   dm-control
    #   huggingface-hub
+    #   lerobot
    #   peft
+    #   robomimic
    #   transformers
 traitlets==5.14.3
    # via
    #   ipython
+    #   jupyter-core
    #   matplotlib-inline
+    #   nbformat
 transformers==5.3.0
    # via
+    #   hf-libero
    #   lerobot
    #   peft
 transforms3d==0.4.2
    # via teleop
+triton==3.6.0
+    # via torch
 typer==0.24.1
    # via
    #   huggingface-hub
@@ -678,6 +845,8 @@ typing-extensions==4.15.0
    #   mypy
    #   pydantic
    #   pydantic-core
+    #   python-can
+    #   referencing
    #   rerun-sdk
    #   starlette
    #   torch
@@ -698,14 +867,16 @@ urllib3==2.6.3
    # via
    #   requests
    #   sentry-sdk
-uvicorn[standard]==0.41.0
+uvicorn==0.41.0
    # via teleop
 uvloop==0.22.1
    # via uvicorn
 virtualenv==21.1.0
    # via pre-commit
 wandb==0.24.2
-    # via lerobot
+    # via
+    #   hf-libero
+    #   lerobot
 watchfiles==1.1.1
    # via uvicorn
 wcwidth==0.6.0
@@ -714,8 +885,12 @@ websocket-client==1.9.0
    # via teleop
 websockets==16.0
    # via uvicorn
-wrapt==2.1.2
-    # via dm-tree
+werkzeug==3.1.8
+    # via tensorboard
+wrapt==1.17.3
+    # via
+    #   dm-tree
+    #   python-can
 xxhash==3.6.0
    # via datasets
 yarl==1.23.0
@@ -724,6 +899,3 @@ zipp==3.23.0
    # via
    #   etils
    #   importlib-metadata
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools
@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Extract natural-language task descriptions for a benchmark suite.
-
-Runs inside the benchmark Docker container (where the env library is installed)
-immediately after lerobot-eval, writing a JSON file that parse_eval_metrics.py
-picks up and embeds in metrics.json.
-
-Output format: {"<suite>_<task_idx>": "<nl instruction>", ...}
-
-Usage:
-    python scripts/ci/extract_task_descriptions.py \\
-        --env libero --task libero_spatial \\
-        --output /tmp/eval-artifacts/task_descriptions.json
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import sys
-from pathlib import Path
-
-
-# LIBERO-plus derives task.language by space-joining the perturbation-variant
-# filename, so strip the perturbation metadata blob to recover the base prompt.
-_LIBERO_PERTURBATION_TAIL_RE = re.compile(
-    r"(?:\s(?:view|initstate|noise|add|tb|table|light|level)(?:\s\d+)+)+$"
-)
-
-
-def _strip_libero_perturbation_tail(instruction: str) -> str:
-    return _LIBERO_PERTURBATION_TAIL_RE.sub("", instruction).strip()
-
-
-def _libero_descriptions(task_suite: str) -> dict[str, str]:
-    from libero.libero import benchmark  # type: ignore[import-untyped]
-
-    suite_dict = benchmark.get_benchmark_dict()
-    if task_suite not in suite_dict:
-        print(
-            f"[extract_task_descriptions] Unknown LIBERO suite '{task_suite}'. "
-            f"Available: {list(suite_dict.keys())}",
-            file=sys.stderr,
-        )
-        return {}
-    suite = suite_dict[task_suite]()
-    return {
-        f"{task_suite}_{i}": _strip_libero_perturbation_tail(suite.get_task(i).language)
-        for i in range(suite.n_tasks)
-    }
-
-
-def _metaworld_descriptions(task_name: str) -> dict[str, str]:
-    # MetaWorld tasks don't expose a separate NL description attribute;
-    # use a cleaned version of the task name as the description.
-    label = task_name.removeprefix("metaworld-").replace("-", " ").strip()
-    return {f"{task_name}_0": label}
-
-
-def _robomme_descriptions(task_names: str) -> dict[str, str]:
-    return {
-        f"{task_name}_0": task_name.replace("_", " ").strip()
-        for task_name in (task.strip() for task in task_names.split(","))
-        if task_name
-    }
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--env", required=True, help="Environment family (libero, metaworld, ...)")
-    parser.add_argument("--task", required=True, help="Task/suite name (e.g. libero_spatial)")
-    parser.add_argument("--output", required=True, help="Path to write task_descriptions.json")
-    args = parser.parse_args()
-
-    descriptions: dict[str, str] = {}
-    try:
-        if args.env in {"libero", "libero_plus"}:
-            descriptions = _libero_descriptions(args.task)
-        elif args.env == "metaworld":
-            descriptions = _metaworld_descriptions(args.task)
-        elif args.env == "robomme":
-            descriptions = _robomme_descriptions(args.task)
-        else:
-            print(
-                f"[extract_task_descriptions] No description extractor for env '{args.env}'.",
-                file=sys.stderr,
-            )
-    except Exception as exc:
-        print(f"[extract_task_descriptions] Warning: {exc}", file=sys.stderr)
-
-    out_path = Path(args.output)
-    out_path.parent.mkdir(parents=True, exist_ok=True)
-    out_path.write_text(json.dumps(descriptions, indent=2))
-    print(f"[extract_task_descriptions] {len(descriptions)} descriptions → {out_path}")
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Parse lerobot-eval output into a small metrics.json artifact.
-
-Reads eval_info.json written by lerobot-eval --output_dir and extracts the
-key metrics needed by the health dashboard. Handles both single-task and
-multi-task eval output formats.
-
-NOTE: This script runs on the bare CI runner (not inside Docker), so it
-must use only Python stdlib modules. Do not add third-party imports.
-
-Usage:
-    python scripts/ci/parse_eval_metrics.py \\
-        --artifacts-dir /tmp/libero-artifacts \\
-        --env libero \\
-        --task libero_spatial \\
-        --policy pepijn223/smolvla_libero
-
-Writes <artifacts-dir>/metrics.json. The CI workflow then uploads this file
-as a GitHub Actions artifact named "<env>-metrics".
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from pathlib import Path
-
-
-def _safe_float(v: float | int | None) -> float | None:
-    if v is None:
-        return None
-    f = float(v)
-    return None if math.isnan(f) else f
-
-
-def _safe_int(v: float | int | None) -> int | None:
-    if v is None:
-        return None
-    f = float(v)
-    return None if math.isnan(f) else int(f)
-
-
-def _extract_metrics(info: dict) -> tuple[float | None, int | None, float | None, float | None]:
-    """Extract (pc_success, n_episodes, avg_sum_reward, eval_s) from eval_info.json.
-
-    Handles two output shapes:
-      - Single-task: {"aggregated": {"pc_success": 80.0, ...}}
-      - Multi-task:  {"overall": {"pc_success": 80.0, "n_episodes": 5, ...}}
-    """
-    for key in ("aggregated", "overall"):
-        if key not in info:
-            continue
-        agg = info[key]
-        pc = agg.get("pc_success")
-        n = agg.get("n_episodes")
-        reward = agg.get("avg_sum_reward")
-        eval_s = agg.get("eval_s")
-
-        if pc is not None and not math.isnan(pc):
-            return (
-                float(pc),
-                _safe_int(n),
-                _safe_float(reward),
-                _safe_float(eval_s),
-            )
-
-    return None, None, None, None
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
-    )
-    parser.add_argument("--artifacts-dir", required=True, help="Path to the mounted artifacts volume")
-    parser.add_argument("--env", required=True, help="Environment name (e.g. libero)")
-    parser.add_argument("--task", required=True, help="Task name (e.g. libero_spatial)")
-    parser.add_argument("--policy", required=True, help="Policy hub path (e.g. pepijn223/smolvla_libero)")
-    args = parser.parse_args()
-
-    artifacts_dir = Path(args.artifacts_dir)
-    eval_info_path = artifacts_dir / "eval_info.json"
-
-    pc_success: float | None = None
-    n_episodes: int | None = None
-    avg_sum_reward: float | None = None
-    eval_s: float | None = None
-
-    if eval_info_path.exists():
-        try:
-            info = json.loads(eval_info_path.read_text())
-            pc_success, n_episodes, avg_sum_reward, eval_s = _extract_metrics(info)
-        except (json.JSONDecodeError, KeyError, TypeError) as exc:
-            print(f"[parse_eval_metrics] Warning: could not parse eval_info.json: {exc}", file=sys.stderr)
-    else:
-        print(
-            f"[parse_eval_metrics] Warning: {eval_info_path} not found — eval may have failed.",
-            file=sys.stderr,
-        )
-
-    task_descriptions: dict[str, str] = {}
-    task_desc_path = artifacts_dir / "task_descriptions.json"
-    if task_desc_path.exists():
-        try:
-            task_descriptions = json.loads(task_desc_path.read_text())
-        except json.JSONDecodeError as exc:
-            print(
-                f"[parse_eval_metrics] Warning: could not parse task_descriptions.json: {exc}",
-                file=sys.stderr,
-            )
-
-    metrics = {
-        "env": args.env,
-        "task": args.task,
-        "policy": args.policy,
-        "pc_success": pc_success,
-        "n_episodes": n_episodes,
-        "avg_sum_reward": avg_sum_reward,
-        "eval_s": eval_s,
-        "task_descriptions": task_descriptions,
-    }
-
-    out_path = artifacts_dir / "metrics.json"
-    out_path.write_text(json.dumps(metrics, indent=2))
-    print(f"[parse_eval_metrics] Written: {out_path}")
-    print(json.dumps(metrics, indent=2))
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())
@@ -1,27 +0,0 @@
---
-title: LeRobot Benchmark Leaderboard
-emoji: 🤖
-colorFrom: yellow
-colorTo: orange
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: Benchmark history for LeRobot policy x benchmark runs
---
-
-# LeRobot Benchmark Leaderboard
-
-This Space reads immutable benchmark rows from a Hugging Face dataset and shows:
-
- Latest result per policy and benchmark
- Historical trends over time
- Direct links to uploaded eval and config artifacts
-
-## Configuration
-
-Set `BENCHMARK_RESULTS_REPO` in the Space settings if you want to point the UI
-at a different public dataset. The default is:
-
- `lerobot/benchmark-history`
@@ -1,226 +0,0 @@
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import json
-import os
-import time
-from pathlib import Path
-from typing import Any
-
-import gradio as gr
-import pandas as pd
-import plotly.express as px
-from huggingface_hub import HfApi, hf_hub_download
-
-RESULTS_REPO = os.environ.get("BENCHMARK_RESULTS_REPO", "lerobot/benchmark-history")
-CACHE_DIR = Path("/tmp/benchmark-leaderboard-cache")
-CACHE_DIR.mkdir(parents=True, exist_ok=True)
-CACHE_TTL_S = 300
-
-_CACHE: dict[str, tuple[float, pd.DataFrame]] = {}
-
-
-def _row_to_record(row: dict[str, Any]) -> dict[str, Any]:
-    overall = row.get("eval", {}).get("overall", {})
-    resources = row.get("resources", {})
-    timings = row.get("timings", {})
-    artifact_urls = row.get("artifact_urls", {})
-    return {
-        "created_at": row.get("created_at"),
-        "benchmark": row.get("benchmark"),
-        "policy": row.get("policy"),
-        "success_rate": overall.get("pc_success"),
-        "n_episodes": overall.get("n_episodes"),
-        "avg_sum_reward": overall.get("avg_sum_reward"),
-        "train_wall_time_s": timings.get("train_wall_time_s"),
-        "eval_wall_time_s": timings.get("eval_wall_time_s"),
-        "total_wall_time_s": timings.get("total_wall_time_s"),
-        "num_gpus": resources.get("num_gpus"),
-        "microbatch_per_gpu": resources.get("microbatch_per_gpu"),
-        "gradient_accumulation_steps": resources.get("gradient_accumulation_steps"),
-        "effective_batch_size": resources.get("effective_batch_size"),
-        "git_commit": row.get("git_commit"),
-        "row_url": artifact_urls.get("row"),
-        "eval_info_url": artifact_urls.get("eval_info"),
-        "train_config_url": artifact_urls.get("train_config"),
-    }
-
-
-def load_rows(repo_id: str = RESULTS_REPO) -> pd.DataFrame:
-    cache_key = f"rows::{repo_id}"
-    cached = _CACHE.get(cache_key)
-    if cached is not None and (time.monotonic() - cached[0]) < CACHE_TTL_S:
-        return cached[1]
-
-    api = HfApi()
-    files = [path for path in api.list_repo_files(repo_id=repo_id, repo_type="dataset") if path.startswith("rows/")]
-    records: list[dict[str, Any]] = []
-    for path_in_repo in sorted(files, reverse=True):
-        local_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=path_in_repo, cache_dir=CACHE_DIR)
-        with open(local_path) as f:
-            row = json.load(f)
-        records.append(_row_to_record(row))
-
-    df = pd.DataFrame.from_records(records)
-    if not df.empty:
-        df["created_at"] = pd.to_datetime(df["created_at"], utc=True)
-        df = df.sort_values("created_at", ascending=False).reset_index(drop=True)
-    _CACHE[cache_key] = (time.monotonic(), df)
-    return df
-
-
-def make_latest_table(df: pd.DataFrame) -> pd.DataFrame:
-    if df.empty:
-        return df
-    latest = (
-        df.sort_values("created_at", ascending=False)
-        .groupby(["benchmark", "policy"], as_index=False)
-        .first()
-        .sort_values(["benchmark", "success_rate"], ascending=[True, False], na_position="last")
-    )
-    return latest[
-        [
-            "benchmark",
-            "policy",
-            "success_rate",
-            "n_episodes",
-            "train_wall_time_s",
-            "eval_wall_time_s",
-            "num_gpus",
-            "effective_batch_size",
-            "git_commit",
-            "row_url",
-            "eval_info_url",
-            "train_config_url",
-        ]
-    ]
-
-
-def make_history_figure(df: pd.DataFrame, benchmark: str, policy: str | None) -> Any:
-    filtered = df[df["benchmark"] == benchmark]
-    if policy and policy != "All":
-        filtered = filtered[filtered["policy"] == policy]
-    if filtered.empty:
-        return px.line(title="No benchmark rows found")
-    fig = px.line(
-        filtered.sort_values("created_at"),
-        x="created_at",
-        y="success_rate",
-        color="policy",
-        markers=True,
-        hover_data=["git_commit", "num_gpus", "train_wall_time_s", "eval_wall_time_s"],
-        title=f"{benchmark} success rate history",
-    )
-    fig.update_layout(yaxis_title="Success rate (%)", xaxis_title="Run time")
-    return fig
-
-
-def make_run_markdown(df: pd.DataFrame, benchmark: str, policy: str | None) -> str:
-    filtered = df[df["benchmark"] == benchmark]
-    if policy and policy != "All":
-        filtered = filtered[filtered["policy"] == policy]
-    if filtered.empty:
-        return "No matching runs yet."
-    latest = filtered.sort_values("created_at", ascending=False).iloc[0]
-    row_link = latest["row_url"] if pd.notna(latest["row_url"]) else None
-    eval_link = latest["eval_info_url"] if pd.notna(latest["eval_info_url"]) else None
-    train_link = latest["train_config_url"] if pd.notna(latest["train_config_url"]) else None
-    lines = [
-        f"Latest run: `{latest['policy']}` on `{latest['benchmark']}`",
-        f"Success rate: `{latest['success_rate']}`",
-        f"GPUs: `{latest['num_gpus']}`",
-        f"Effective batch size: `{latest['effective_batch_size']}`",
-        f"Commit: `{latest['git_commit']}`",
-    ]
-    if row_link:
-        lines.append(f"Row JSON: [open]({row_link})")
-    if eval_link:
-        lines.append(f"Eval Info: [open]({eval_link})")
-    if train_link:
-        lines.append(f"Train Config: [open]({train_link})")
-    return "\n\n".join(lines)
-
-
-def refresh_view(benchmark: str, policy: str) -> tuple[pd.DataFrame, dict[str, Any], Any, str]:
-    df = load_rows()
-    latest_table = make_latest_table(df)
-    benchmark_names = sorted(df["benchmark"].dropna().unique().tolist()) if not df.empty else []
-    if benchmark not in benchmark_names and benchmark_names:
-        benchmark = benchmark_names[0]
-    policy_choices = ["All"]
-    if benchmark and not df.empty:
-        policy_choices.extend(sorted(df[df["benchmark"] == benchmark]["policy"].dropna().unique().tolist()))
-    if policy not in policy_choices:
-        policy = "All"
-    history = make_history_figure(df, benchmark, policy)
-    summary = make_run_markdown(df, benchmark, policy)
-    return latest_table, gr.update(choices=policy_choices, value=policy), history, summary
-
-
-with gr.Blocks(title="LeRobot Benchmark Leaderboard") as demo:
-    gr.Markdown(
-        f"""
-# LeRobot Benchmark Leaderboard
-
-Results dataset: [`{RESULTS_REPO}`](https://huggingface.co/datasets/{RESULTS_REPO})
-"""
-    )
-
-    with gr.Row():
-        benchmark_dropdown = gr.Dropdown(label="Benchmark", choices=[])
-        policy_dropdown = gr.Dropdown(label="Policy", choices=["All"], value="All")
-        refresh_button = gr.Button("Refresh")
-
-    latest_table = gr.Dataframe(label="Latest Results", interactive=False)
-    history_plot = gr.Plot(label="History")
-    latest_summary = gr.Markdown()
-
-    def _initial_state():
-        df = load_rows()
-        benchmarks = sorted(df["benchmark"].dropna().unique().tolist()) if not df.empty else []
-        benchmark = benchmarks[0] if benchmarks else ""
-        latest, policy_choices, history, summary = refresh_view(benchmark, "All")
-        return (
-            gr.update(choices=benchmarks, value=benchmark),
-            policy_choices,
-            latest,
-            history,
-            summary,
-        )
-
-    demo.load(
-        _initial_state,
-        outputs=[benchmark_dropdown, policy_dropdown, latest_table, history_plot, latest_summary],
-    )
-    refresh_button.click(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-    benchmark_dropdown.change(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-    policy_dropdown.change(
-        refresh_view,
-        inputs=[benchmark_dropdown, policy_dropdown],
-        outputs=[latest_table, policy_dropdown, history_plot, latest_summary],
-    )
-
-
-if __name__ == "__main__":
-    demo.launch()
@@ -1,4 +0,0 @@
-gradio>=5.0.0,<6.0.0
-plotly>=5.18.0
-pandas>=2.0.0
-huggingface-hub>=1.0.0,<2.0.0
@@ -67,17 +67,11 @@ class EvalConfig:
    # `batch_size` specifies the number of environments to use in a gym.vector.VectorEnv.
    # Set to 0 for auto-tuning based on available CPU cores and n_episodes.
    batch_size: int = 0
-    # Number of rollout videos to save per evaluated task. Set to 0 to disable videos.
-    max_episodes_rendered: int = 10
    # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
    # Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
    use_async_envs: bool = True

    def __post_init__(self) -> None:
-        if self.max_episodes_rendered < 0:
-            raise ValueError(
-                f"`max_episodes_rendered` must be non-negative, got {self.max_episodes_rendered}."
-            )
        if self.batch_size == 0:
            self.batch_size = self._auto_batch_size()
        if self.batch_size > self.n_episodes:
@@ -56,7 +56,6 @@ class TrainPipelineConfig(HubMixin):
    # Number of workers for the dataloader.
    num_workers: int = 4
    batch_size: int = 8
-    gradient_accumulation_steps: int = 1
    steps: int = 100_000
    eval_freq: int = 20_000
    log_freq: int = 200
@@ -133,11 +132,6 @@ class TrainPipelineConfig(HubMixin):
        if isinstance(self.dataset.repo_id, list):
            raise NotImplementedError("LeRobotMultiDataset is not currently implemented.")

-        if self.gradient_accumulation_steps <= 0:
-            raise ValueError(
-                f"`gradient_accumulation_steps` must be strictly positive, got {self.gradient_accumulation_steps}."
-            )
-
        if not self.use_policy_training_preset and (self.optimizer is None or self.scheduler is None):
            raise ValueError("Optimizer and Scheduler must be set when the policy presets are not used.")
        elif self.use_policy_training_preset and not self.resume:
@@ -18,15 +18,7 @@
 # from lerobot.utils.import_utils import require_package
 # require_package("gymnasium", extra="<update_extra>", import_name="gymnasium")

-from .configs import (
-    AlohaEnv,
-    EnvConfig,
-    HILSerlRobotEnvConfig,
-    HubEnvConfig,
-    LiberoPlusEnv,
-    PushtEnv,
-    RoboMMEEnv,
-)
+from .configs import AlohaEnv, EnvConfig, HILSerlRobotEnvConfig, HubEnvConfig, PushtEnv
 from .factory import make_env, make_env_config, make_env_pre_post_processors
 from .utils import check_env_attributes_and_types, close_envs, env_to_policy_features, preprocess_observation

@@ -35,9 +27,7 @@ __all__ = [
    "EnvConfig",
    "HILSerlRobotEnvConfig",
    "HubEnvConfig",
-    "LiberoPlusEnv",
    "PushtEnv",
-    "RoboMMEEnv",
    "check_env_attributes_and_types",
    "close_envs",
    "env_to_policy_features",
@@ -574,58 +574,3 @@ class IsaaclabArenaEnv(HubEnvConfig):
            ),
            PolicyProcessorPipeline(steps=[]),
        )
-
-
-@EnvConfig.register_subclass("libero_plus")
-@dataclass
-class LiberoPlusEnv(LiberoEnv):
-    """Config for LIBERO-plus robustness benchmark evaluation."""
-
-    task: str = "libero_spatial"
-
-
-@EnvConfig.register_subclass("robomme")
-@dataclass
-class RoboMMEEnv(EnvConfig):
-    """RoboMME memory-augmented manipulation benchmark."""
-
-    task: str = "PickXtimes"
-    fps: int = 10
-    episode_length: int = 300
-    action_space: str = "joint_angle"
-    dataset_split: str = "test"
-    task_ids: list[int] | None = None
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(8,)),
-            "image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            "wrist_image": PolicyFeature(type=FeatureType.VISUAL, shape=(256, 256, 3)),
-            OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(8,)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            ACTION: ACTION,
-            "image": f"{OBS_IMAGES}.image",
-            "wrist_image": f"{OBS_IMAGES}.wrist_image",
-            OBS_STATE: OBS_STATE,
-        }
-    )
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {}
-
-    def create_envs(self, n_envs: int, use_async_envs: bool = True):
-        from .robomme import create_robomme_envs
-
-        env_cls = _make_vec_env_cls(use_async_envs, n_envs)
-        return create_robomme_envs(
-            task=self.task,
-            n_envs=n_envs,
-            action_space_type=self.action_space,
-            dataset=self.dataset_split,
-            episode_length=self.episode_length,
-            task_ids=self.task_ids,
-            env_cls=env_cls,
-        )
@@ -16,7 +16,6 @@
 from __future__ import annotations

 import os
-import re
 from collections import defaultdict
 from collections.abc import Callable, Iterable, Mapping, Sequence
 from functools import partial
@@ -70,28 +69,14 @@ def _select_task_ids(total_tasks: int, task_ids: Iterable[int] | None) -> list[i
    return ids


-# LIBERO-plus perturbation variants encode the perturbation in the filename
-# but on disk only the base `.pruned_init` exists — strip the suffix to match
-# LIBERO-plus's own suite.get_task_init_states() (we reimplement it here so we
-# can pass weights_only=False for PyTorch 2.6+ numpy pickles).
-_LIBERO_PERTURBATION_SUFFIX_RE = re.compile(r"_(?:language|view|light)_[^.]*|_(?:table|tb)_\d+")
-
-
 def get_task_init_states(task_suite: Any, i: int) -> np.ndarray:
-    task = task_suite.tasks[i]
-    filename = Path(task.init_states_file)
-    root = Path(get_libero_path("init_states"))
-
-    # `_add_` / `_level` variants store extra-object layouts under libero_newobj/
-    # as a flat array that must be reshaped to (1, -1).
-    if "_add_" in filename.name or "_level" in filename.name:
-        init_states_path = root / "libero_newobj" / task.problem_folder / filename.name
-        init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
-        return init_states.reshape(1, -1)
-
-    stripped = _LIBERO_PERTURBATION_SUFFIX_RE.sub("", filename.stem) + filename.suffix
-    init_states_path = root / task.problem_folder / stripped
-    return torch.load(init_states_path, weights_only=False)  # nosec B614
+    init_states_path = (
+        Path(get_libero_path("init_states"))
+        / task_suite.tasks[i].problem_folder
+        / task_suite.tasks[i].init_states_file
+    )
+    init_states = torch.load(init_states_path, weights_only=False)  # nosec B614
+    return init_states


 def get_libero_dummy_action():
@@ -1,209 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""RoboMME environment wrapper for LeRobot evaluation."""
-
-from __future__ import annotations
-
-from collections.abc import Callable, Sequence
-from functools import partial
-from typing import Any
-
-import gymnasium as gym
-import numpy as np
-from gymnasium import spaces
-
-ROBOMME_TASKS = [
-    "BinFill",
-    "PickXtimes",
-    "SwingXtimes",
-    "StopCube",
-    "VideoUnmask",
-    "VideoUnmaskSwap",
-    "ButtonUnmask",
-    "ButtonUnmaskSwap",
-    "PickHighlight",
-    "VideoRepick",
-    "VideoPlaceButton",
-    "VideoPlaceOrder",
-    "MoveCube",
-    "InsertPeg",
-    "PatternLock",
-    "RouteStick",
-]
-
-
-class RoboMMEGymEnv(gym.Env):
-    """Thin Gymnasium wrapper around a single RoboMME episode env."""
-
-    metadata = {"render_modes": ["rgb_array"], "render_fps": 10}
-
-    def __init__(
-        self,
-        task: str = "PickXtimes",
-        action_space_type: str = "joint_angle",
-        dataset: str = "test",
-        episode_idx: int = 0,
-        max_steps: int = 300,
-    ):
-        super().__init__()
-        from robomme.env_record_wrapper import BenchmarkEnvBuilder
-
-        self._builder = BenchmarkEnvBuilder(
-            env_id=task,
-            dataset=dataset,
-            action_space=action_space_type,
-            gui_render=False,
-            max_steps=max_steps,
-        )
-        self._max_episode_steps = max_steps
-        self._episode_idx = episode_idx
-        self._max_steps = max_steps
-        self._env = None
-        self._last_raw_obs: dict | None = None
-
-        action_dim = 8 if action_space_type == "joint_angle" else 7
-        self.action_space = spaces.Box(low=-1.0, high=1.0, shape=(action_dim,), dtype=np.float32)
-        self.observation_space = spaces.Dict(
-            {
-                "image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
-                "wrist_image": spaces.Box(0, 255, shape=(256, 256, 3), dtype=np.uint8),
-                "state": spaces.Box(-np.inf, np.inf, shape=(8,), dtype=np.float32),
-            }
-        )
-
-    def reset(self, *, seed=None, options=None):
-        super().reset(seed=seed)
-        self._env = self._builder.make_env_for_episode(
-            episode_idx=self._episode_idx,
-            max_steps=self._max_steps,
-        )
-        obs, info = self._env.reset()
-        self._last_raw_obs = obs
-        return self._convert_obs(obs), self._convert_info(info)
-
-    def step(self, action):
-        obs, reward, terminated, truncated, info = self._env.step(action)
-        self._last_raw_obs = obs
-
-        terminated_bool = bool(terminated.item()) if hasattr(terminated, "item") else bool(terminated)
-        truncated_bool = bool(truncated.item()) if hasattr(truncated, "item") else bool(truncated)
-
-        status = info.get("status", "ongoing")
-        conv_info = self._convert_info(info)
-        conv_info["is_success"] = status == "success"
-
-        return self._convert_obs(obs), float(reward), terminated_bool, truncated_bool, conv_info
-
-    def render(self) -> np.ndarray | None:
-        if self._last_raw_obs is None:
-            return np.zeros((256, 256, 3), dtype=np.uint8)
-        front = self._last_raw_obs.get("front_rgb_list")
-        if front is None:
-            return np.zeros((256, 256, 3), dtype=np.uint8)
-        frame = front[-1] if isinstance(front, list) else front
-        return np.asarray(frame, dtype=np.uint8)
-
-    def _convert_obs(self, obs: dict) -> dict:
-        front_rgb = (
-            obs["front_rgb_list"][-1] if isinstance(obs["front_rgb_list"], list) else obs["front_rgb_list"]
-        )
-        wrist_rgb = (
-            obs["wrist_rgb_list"][-1] if isinstance(obs["wrist_rgb_list"], list) else obs["wrist_rgb_list"]
-        )
-        joint_state = (
-            obs["joint_state_list"][-1]
-            if isinstance(obs["joint_state_list"], list)
-            else obs["joint_state_list"]
-        )
-        gripper_state = (
-            obs["gripper_state_list"][-1]
-            if isinstance(obs["gripper_state_list"], list)
-            else obs["gripper_state_list"]
-        )
-
-        joint = np.asarray(joint_state, dtype=np.float32).flatten()[:7]
-        gripper = np.asarray(gripper_state, dtype=np.float32).flatten()[:1]
-        state = np.concatenate([joint, gripper])
-
-        return {
-            "image": np.asarray(front_rgb, dtype=np.uint8),
-            "wrist_image": np.asarray(wrist_rgb, dtype=np.uint8),
-            "state": state,
-        }
-
-    def _convert_info(self, info: dict) -> dict:
-        return {
-            "status": info.get("status", "ongoing"),
-            "task_goal": info.get("task_goal", ""),
-        }
-
-
-def _make_env_fns(
-    *,
-    task: str,
-    n_envs: int,
-    action_space_type: str,
-    dataset: str,
-    episode_length: int,
-    task_id: int,
-) -> list[Callable[[], RoboMMEGymEnv]]:
-    def _make_one(episode_index: int) -> RoboMMEGymEnv:
-        return RoboMMEGymEnv(
-            task=task,
-            action_space_type=action_space_type,
-            dataset=dataset,
-            episode_idx=episode_index,
-            max_steps=episode_length,
-        )
-
-    return [partial(_make_one, task_id + i) for i in range(n_envs)]
-
-
-def create_robomme_envs(
-    task: str,
-    n_envs: int = 1,
-    action_space_type: str = "joint_angle",
-    dataset: str = "test",
-    episode_length: int = 300,
-    task_ids: list[int] | None = None,
-    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
-) -> dict[str, dict[int, gym.vector.VectorEnv]]:
-    """Create vectorized RoboMME environments for evaluation."""
-    if env_cls is None or not callable(env_cls):
-        raise ValueError("env_cls must be a callable that wraps a list of env factory callables.")
-    if not isinstance(n_envs, int) or n_envs <= 0:
-        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
-
-    if task_ids is None:
-        task_ids = [0]
-
-    task_names = [t.strip() for t in task.split(",") if t.strip()]
-    out: dict[str, dict[int, gym.vector.VectorEnv]] = {}
-    for task_name in task_names:
-        envs_by_task: dict[int, gym.vector.VectorEnv] = {}
-        for task_id in task_ids:
-            fns = _make_env_fns(
-                task=task_name,
-                n_envs=n_envs,
-                action_space_type=action_space_type,
-                dataset=dataset,
-                episode_length=episode_length,
-                task_id=task_id,
-            )
-            envs_by_task[task_id] = env_cls(fns)
-        out[task_name] = envs_by_task
-    return out
@@ -216,14 +216,6 @@ class FeetechMotorsBus(SerialMotorsBus):
                self.write("Maximum_Acceleration", motor, maximum_acceleration)
            self.write("Acceleration", motor, acceleration)

-            # Clear bit 4 (0x10) of the Phase register (0x12) to set angle feedback mode to 0.
-            # This forces position readings to be in the range [0, resolution - 1] and prevents overflow or negative values.
-            # Only known to be necessary for the STS3215.
-            if self.motors[motor].model == "sts3215":
-                phase = self.read("Phase", motor, normalize=False)
-                if phase & 0x10:
-                    self.write("Phase", motor, phase & ~0x10)
-
    @property
    def is_calibrated(self) -> bool:
        motors_calibration = self.read_calibration()
@@ -175,36 +175,33 @@ def actor_cli(cfg: TrainRLServerPipelineConfig):
    interactions_process.start()
    receive_policy_process.start()

-    try:
-        act_with_policy(
-            cfg=cfg,
-            shutdown_event=shutdown_event,
-            parameters_queue=parameters_queue,
-            transitions_queue=transitions_queue,
-            interactions_queue=interactions_queue,
-        )
-        logging.info("[ACTOR] Policy loop finished")
-    except Exception:
-        logging.exception("[ACTOR] Unhandled exception in act_with_policy")
-        shutdown_event.set()
-    finally:
-        logging.info("[ACTOR] Closing queues")
-        transitions_queue.close()
-        interactions_queue.close()
-        parameters_queue.close()
+    act_with_policy(
+        cfg=cfg,
+        shutdown_event=shutdown_event,
+        parameters_queue=parameters_queue,
+        transitions_queue=transitions_queue,
+        interactions_queue=interactions_queue,
+    )
+    logging.info("[ACTOR] Policy process joined")

-        transitions_process.join()
-        logging.info("[ACTOR] Transitions process joined")
-        interactions_process.join()
-        logging.info("[ACTOR] Interactions process joined")
-        receive_policy_process.join()
-        logging.info("[ACTOR] Receive policy process joined")
+    logging.info("[ACTOR] Closing queues")
+    transitions_queue.close()
+    interactions_queue.close()
+    parameters_queue.close()

-        transitions_queue.cancel_join_thread()
-        interactions_queue.cancel_join_thread()
-        parameters_queue.cancel_join_thread()
+    transitions_process.join()
+    logging.info("[ACTOR] Transitions process joined")
+    interactions_process.join()
+    logging.info("[ACTOR] Interactions process joined")
+    receive_policy_process.join()
+    logging.info("[ACTOR] Receive policy process joined")

-        logging.info("[ACTOR] Cleanup complete")
+    logging.info("[ACTOR] join queues")
+    transitions_queue.cancel_join_thread()
+    interactions_queue.cancel_join_thread()
+    parameters_queue.cancel_join_thread()
+
+    logging.info("[ACTOR] queues closed")


 # Core algorithm functions
@@ -218,33 +218,30 @@ def start_learner_threads(
    )
    communication_process.start()

-    try:
-        add_actor_information_and_train(
-            cfg=cfg,
-            wandb_logger=wandb_logger,
-            shutdown_event=shutdown_event,
-            transition_queue=transition_queue,
-            interaction_message_queue=interaction_message_queue,
-            parameters_queue=parameters_queue,
-        )
-        logging.info("[LEARNER] Training process stopped")
-    except Exception:
-        logging.exception("[LEARNER] Unhandled exception in training loop")
-        shutdown_event.set()
-    finally:
-        logging.info("[LEARNER] Closing queues")
-        transition_queue.close()
-        interaction_message_queue.close()
-        parameters_queue.close()
+    add_actor_information_and_train(
+        cfg=cfg,
+        wandb_logger=wandb_logger,
+        shutdown_event=shutdown_event,
+        transition_queue=transition_queue,
+        interaction_message_queue=interaction_message_queue,
+        parameters_queue=parameters_queue,
+    )
+    logging.info("[LEARNER] Training process stopped")

-        communication_process.join()
-        logging.info("[LEARNER] Communication process joined")
+    logging.info("[LEARNER] Closing queues")
+    transition_queue.close()
+    interaction_message_queue.close()
+    parameters_queue.close()

-        transition_queue.cancel_join_thread()
-        interaction_message_queue.cancel_join_thread()
-        parameters_queue.cancel_join_thread()
+    communication_process.join()
+    logging.info("[LEARNER] Communication process joined")

-        logging.info("[LEARNER] Cleanup complete")
+    logging.info("[LEARNER] join queues")
+    transition_queue.cancel_join_thread()
+    interaction_message_queue.cancel_join_thread()
+    parameters_queue.cancel_join_thread()
+
+    logging.info("[LEARNER] queues closed")


 # Core algorithm functions
@@ -572,7 +572,7 @@ def eval_main(cfg: EvalPipelineConfig):
            preprocessor=preprocessor,
            postprocessor=postprocessor,
            n_episodes=cfg.eval.n_episodes,
-            max_episodes_rendered=cfg.eval.max_episodes_rendered,
+            max_episodes_rendered=10,
            videos_dir=Path(cfg.output_dir) / "videos",
            start_seed=cfg.seed,
            max_parallel_tasks=cfg.env.max_parallel_tasks,
@@ -71,9 +71,6 @@ def update_policy(
    lr_scheduler=None,
    lock=None,
    rabc_weights_provider=None,
-    *,
-    do_optimizer_step: bool = True,
-    loss_divisor: int = 1,
 ) -> tuple[MetricsTracker, dict]:
    """
    Performs a single training step to update the policy's weights.
@@ -125,38 +122,34 @@ def update_policy(
            loss, output_dict = policy.forward(batch)

        # TODO(rcadene): policy.unnormalize_outputs(out_dict)
-        logged_loss = loss.detach()
-        if loss_divisor > 1:
-            loss = loss / loss_divisor

    # Use accelerator's backward method
    accelerator.backward(loss)

-    grad_norm_value = 0.0
-    if do_optimizer_step:
-        if grad_clip_norm > 0:
-            grad_norm = accelerator.clip_grad_norm_(policy.parameters(), grad_clip_norm)
-        else:
-            grad_norm = torch.nn.utils.clip_grad_norm_(
-                policy.parameters(), float("inf"), error_if_nonfinite=False
-            )
-        grad_norm_value = grad_norm.item()
+    # Clip gradients if specified
+    if grad_clip_norm > 0:
+        grad_norm = accelerator.clip_grad_norm_(policy.parameters(), grad_clip_norm)
+    else:
+        grad_norm = torch.nn.utils.clip_grad_norm_(
+            policy.parameters(), float("inf"), error_if_nonfinite=False
+        )

-        with lock if lock is not None else nullcontext():
-            optimizer.step()
+    # Optimizer step
+    with lock if lock is not None else nullcontext():
+        optimizer.step()

-        optimizer.zero_grad()
+    optimizer.zero_grad()

-        # Step through pytorch scheduler at every optimizer step instead of epoch
-        if lr_scheduler is not None:
-            lr_scheduler.step()
+    # Step through pytorch scheduler at every batch instead of epoch
+    if lr_scheduler is not None:
+        lr_scheduler.step()

-        # Update internal buffers if policy has update method
-        if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
-            accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()
+    # Update internal buffers if policy has update method
+    if has_method(accelerator.unwrap_model(policy, keep_fp32_wrapper=True), "update"):
+        accelerator.unwrap_model(policy, keep_fp32_wrapper=True).update()

-    train_metrics.loss = logged_loss.item()
-    train_metrics.grad_norm = grad_norm_value
+    train_metrics.loss = loss.item()
+    train_metrics.grad_norm = grad_norm.item()
    train_metrics.lr = optimizer.param_groups[0]["lr"]
    train_metrics.update_s = time.perf_counter() - start_time
    return train_metrics, output_dict
@@ -366,16 +359,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        logging.info(f"{dataset.num_frames=} ({format_big_number(dataset.num_frames)})")
        logging.info(f"{dataset.num_episodes=}")
        num_processes = accelerator.num_processes
-        micro_batch = cfg.batch_size
-        logical_batch = cfg.batch_size * cfg.gradient_accumulation_steps
-        effective_bs = logical_batch * num_processes
-        logging.info(
-            "Effective batch size: %s x %s x %s = %s",
-            micro_batch,
-            cfg.gradient_accumulation_steps,
-            num_processes,
-            effective_bs,
-        )
+        effective_bs = cfg.batch_size * num_processes
+        logging.info(f"Effective batch size: {cfg.batch_size} x {num_processes} = {effective_bs}")
        logging.info(f"{num_learnable_params=} ({format_big_number(num_learnable_params)})")
        logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")

@@ -422,10 +407,9 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
    }

    # Keep global batch size for logging; MetricsTracker handles world size internally.
-    logical_batch_size = cfg.batch_size * cfg.gradient_accumulation_steps
-    effective_batch_size = logical_batch_size * accelerator.num_processes
+    effective_batch_size = cfg.batch_size * accelerator.num_processes
    train_tracker = MetricsTracker(
-        logical_batch_size,
+        cfg.batch_size,
        dataset.num_frames,
        dataset.num_episodes,
        train_metrics,
@@ -447,62 +431,21 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
        )

    for _ in range(step, cfg.steps):
-        step_dataloading_s = 0.0
-        step_update_s = 0.0
-        step_losses = []
-        step_grad_norm = 0.0
-        step_lr = optimizer.param_groups[0]["lr"]
-        output_dict = {}
-        optimizer.zero_grad()
-        for accumulation_idx in range(cfg.gradient_accumulation_steps):
-            start_time = time.perf_counter()
-            batch = next(dl_iter)
-            batch = preprocessor(batch)
-            step_dataloading_s += time.perf_counter() - start_time
+        start_time = time.perf_counter()
+        batch = next(dl_iter)
+        batch = preprocessor(batch)
+        train_tracker.dataloading_s = time.perf_counter() - start_time

-            is_last_microbatch = accumulation_idx == cfg.gradient_accumulation_steps - 1
-            micro_metrics = MetricsTracker(
-                cfg.batch_size,
-                dataset.num_frames,
-                dataset.num_episodes,
-                {
-                    "loss": AverageMeter("loss", ":.3f"),
-                    "grad_norm": AverageMeter("grdn", ":.3f"),
-                    "lr": AverageMeter("lr", ":0.1e"),
-                    "update_s": AverageMeter("updt_s", ":.3f"),
-                },
-                accelerator=accelerator,
-            )
-            sync_context = (
-                nullcontext()
-                if is_last_microbatch or accelerator.num_processes == 1
-                else accelerator.no_sync(policy)
-            )
-            with sync_context:
-                micro_metrics, micro_output_dict = update_policy(
-                    micro_metrics,
-                    policy,
-                    batch,
-                    optimizer,
-                    cfg.optimizer.grad_clip_norm,
-                    accelerator=accelerator,
-                    lr_scheduler=lr_scheduler if is_last_microbatch else None,
-                    rabc_weights_provider=rabc_weights,
-                    do_optimizer_step=is_last_microbatch,
-                    loss_divisor=cfg.gradient_accumulation_steps,
-                )
-            step_update_s += micro_metrics.update_s.val
-            step_losses.append(micro_metrics.loss.val)
-            if is_last_microbatch:
-                step_grad_norm = micro_metrics.grad_norm.val
-                step_lr = micro_metrics.lr.val
-                output_dict = micro_output_dict
-
-        train_tracker.loss = sum(step_losses) / len(step_losses)
-        train_tracker.grad_norm = step_grad_norm
-        train_tracker.lr = step_lr
-        train_tracker.update_s = step_update_s
-        train_tracker.dataloading_s = step_dataloading_s
+        train_tracker, output_dict = update_policy(
+            train_tracker,
+            policy,
+            batch,
+            optimizer,
+            cfg.optimizer.grad_clip_norm,
+            accelerator=accelerator,
+            lr_scheduler=lr_scheduler,
+            rabc_weights_provider=rabc_weights,
+        )

        # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
        # increment `step` here.
@@ -567,7 +510,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                        postprocessor=postprocessor,
                        n_episodes=cfg.eval.n_episodes,
                        videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
-                        max_episodes_rendered=cfg.eval.max_episodes_rendered,
+                        max_episodes_rendered=4,
                        start_seed=cfg.seed,
                        max_parallel_tasks=cfg.env.max_parallel_tasks,
                    )
@@ -598,9 +541,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                if wandb_logger:
                    wandb_log_dict = {**eval_tracker.to_dict(), **eval_info}
                    wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
-                    video_paths = eval_info["overall"].get("video_paths", [])
-                    if video_paths:
-                        wandb_logger.log_video(video_paths[0], step, mode="eval")
+                    wandb_logger.log_video(eval_info["overall"]["video_paths"][0], step, mode="eval")

            accelerator.wait_for_everyone()

@@ -1,70 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import json
-from dataclasses import dataclass
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from huggingface_hub import HfApi
-
-
-def utc_timestamp_slug(now: datetime | None = None) -> str:
-    current = now or datetime.now(UTC)
-    return current.strftime("%Y%m%dT%H%M%SZ")
-
-
-def make_hub_file_url(repo_id: str, path_in_repo: str, repo_type: str = "dataset") -> str:
-    prefix = "datasets/" if repo_type == "dataset" else ""
-    return f"https://huggingface.co/{prefix}{repo_id}/resolve/main/{path_in_repo}"
-
-
-def write_json(path: Path, payload: dict[str, Any]) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    path.write_text(json.dumps(payload, indent=2, sort_keys=True))
-
-
-@dataclass(frozen=True)
-class UploadTarget:
-    local_path: Path
-    path_in_repo: str
-
-
-def upload_targets(
-    repo_id: str,
-    targets: list[UploadTarget],
-    *,
-    repo_type: str = "dataset",
-    token: str | None = None,
-    private: bool | None = None,
-    commit_message: str | None = None,
-) -> dict[str, str]:
-    api = HfApi(token=token)
-    api.create_repo(repo_id=repo_id, repo_type=repo_type, private=private, exist_ok=True)
-    uploaded: dict[str, str] = {}
-    for target in targets:
-        api.upload_file(
-            path_or_fileobj=str(target.local_path),
-            path_in_repo=target.path_in_repo,
-            repo_id=repo_id,
-            repo_type=repo_type,
-            commit_message=commit_message or f"Upload {target.path_in_repo}",
-        )
-        uploaded[target.path_in_repo] = make_hub_file_url(repo_id, target.path_in_repo, repo_type=repo_type)
-    return uploaded
@@ -1,142 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-
-from benchmarks.run_benchmark_matrix import (
-    PlannedJob,
-    compute_gradient_accumulation_steps,
-    plan_jobs,
-    render_sbatch_script,
-    write_manifest,
-)
-
-
-def _one_job(job_list: list[PlannedJob]) -> PlannedJob:
-    assert len(job_list) == 1
-    return job_list[0]
-
-
-def test_compute_gradient_accumulation_steps_for_fixed_effective_batch():
-    assert compute_gradient_accumulation_steps(
-        effective_batch_size=256,
-        num_gpus=8,
-        microbatch_per_gpu=32,
-    ) == 1
-    assert compute_gradient_accumulation_steps(
-        effective_batch_size=256,
-        num_gpus=4,
-        microbatch_per_gpu=32,
-    ) == 2
-    assert compute_gradient_accumulation_steps(
-        effective_batch_size=256,
-        num_gpus=1,
-        microbatch_per_gpu=32,
-    ) == 8
-
-
-def test_plan_jobs_filters_libero_plus_only(tmp_path):
-    jobs = plan_jobs(
-        output_dir=tmp_path,
-        hub_org="lerobot",
-        results_repo="lerobot/benchmark-history",
-        policies=["pi0", "act"],
-        benchmarks=["libero_plus"],
-    )
-
-    assert [job.benchmark for job in jobs] == ["libero_plus", "libero_plus"]
-    assert [job.policy for job in jobs] == ["pi0", "act"]
-
-
-def test_plan_jobs_includes_libero_plus_and_robomme(tmp_path):
-    jobs = plan_jobs(
-        output_dir=tmp_path,
-        hub_org="lerobot",
-        results_repo="lerobot/benchmark-history",
-        policies=["pi0"],
-        benchmarks=["libero_plus", "robomme"],
-    )
-
-    assert [job.benchmark for job in jobs] == ["libero_plus", "robomme"]
-    assert jobs[0].effective_batch_size == 256
-    assert jobs[1].effective_batch_size == 256
-
-
-def test_plan_jobs_sets_expected_gpu_and_accumulation(tmp_path):
-    jobs = plan_jobs(
-        output_dir=tmp_path,
-        hub_org="lerobot",
-        results_repo="lerobot/benchmark-history",
-        policies=["pi0", "xvla", "act"],
-        benchmarks=["robomme"],
-    )
-    by_policy = {job.policy: job for job in jobs}
-
-    assert by_policy["pi0"].num_gpus == 8
-    assert by_policy["pi0"].gradient_accumulation_steps == 1
-    assert by_policy["xvla"].num_gpus == 4
-    assert by_policy["xvla"].gradient_accumulation_steps == 2
-    assert by_policy["act"].num_gpus == 1
-    assert by_policy["act"].gradient_accumulation_steps == 8
-
-
-def test_render_sbatch_script_contains_train_eval_and_publish(tmp_path):
-    job = _one_job(
-        plan_jobs(
-            output_dir=tmp_path,
-            hub_org="lerobot",
-            results_repo="lerobot/benchmark-history",
-            policies=["pi0_fast"],
-            benchmarks=["robomme"],
-        )
-    )
-
-    script = render_sbatch_script(
-        job=job,
-        output_dir=tmp_path,
-        results_repo_id="lerobot/benchmark-history",
-        git_commit="deadbeef",
-    )
-
-    assert "docker/Dockerfile" not in script
-    assert "lerobot-benchmark-robomme:latest" in script
-    assert '--dataset.repo_id="lerobot/robomme"' in script
-    assert '--env.type="robomme"' in script
-    assert "--gradient_accumulation_steps=1" in script
-    assert "lerobot-train-tokenizer" in script
-    assert "benchmarks/publish_benchmark_result.py" in script
-
-
-def test_write_manifest_records_job_metadata(tmp_path):
-    jobs = plan_jobs(
-        output_dir=tmp_path,
-        hub_org="lerobot",
-        results_repo="lerobot/benchmark-history",
-        policies=["pi0"],
-        benchmarks=["libero_plus", "robomme"],
-    )
-    manifest_path = write_manifest(
-        output_dir=tmp_path,
-        jobs=jobs,
-        git_commit="deadbeef",
-        hub_org="lerobot",
-        results_repo="lerobot/benchmark-history",
-    )
-
-    manifest = json.loads(manifest_path.read_text())
-    assert manifest["git_commit"] == "deadbeef"
-    assert manifest["results_repo"] == "lerobot/benchmark-history"
-    assert [job["benchmark"] for job in manifest["jobs"]] == ["libero_plus", "robomme"]
@@ -1,123 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import annotations
-
-import sys
-from types import ModuleType
-from unittest.mock import MagicMock
-
-import numpy as np
-
-
-def _install_robomme_stub():
-    stub = ModuleType("robomme")
-    wrapper_stub = ModuleType("robomme.env_record_wrapper")
-
-    class FakeBuilder:
-        def __init__(self, **kwargs):
-            pass
-
-        def make_env_for_episode(self, episode_idx: int, max_steps: int):
-            env = MagicMock()
-            obs = {
-                "front_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
-                "wrist_rgb_list": [np.zeros((256, 256, 3), dtype=np.uint8)],
-                "joint_state_list": [np.zeros(7, dtype=np.float32)],
-                "gripper_state_list": [np.zeros(2, dtype=np.float32)],
-            }
-            env.reset.return_value = (obs, {"status": "ongoing", "task_goal": "pick the cube"})
-            env.step.return_value = (obs, 0.0, False, False, {"status": "ongoing", "task_goal": ""})
-            return env
-
-    wrapper_stub.BenchmarkEnvBuilder = FakeBuilder
-    stub.env_record_wrapper = wrapper_stub
-    sys.modules["robomme"] = stub
-    sys.modules["robomme.env_record_wrapper"] = wrapper_stub
-
-
-def _uninstall_robomme_stub():
-    sys.modules.pop("robomme", None)
-    sys.modules.pop("robomme.env_record_wrapper", None)
-
-
-def test_robomme_env_config_defaults():
-    from lerobot.envs.configs import RoboMMEEnv
-
-    cfg = RoboMMEEnv()
-    assert cfg.task == "PickXtimes"
-    assert cfg.fps == 10
-    assert cfg.episode_length == 300
-    assert cfg.action_space == "joint_angle"
-    assert cfg.dataset_split == "test"
-    assert cfg.task_ids is None
-
-
-def test_robomme_features_map():
-    from lerobot.envs.configs import RoboMMEEnv
-    from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE
-
-    cfg = RoboMMEEnv()
-    assert cfg.features_map[ACTION] == ACTION
-    assert cfg.features_map["image"] == f"{OBS_IMAGES}.image"
-    assert cfg.features_map["wrist_image"] == f"{OBS_IMAGES}.wrist_image"
-    assert cfg.features_map[OBS_STATE] == OBS_STATE
-
-
-def test_convert_obs_list_format():
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import RoboMMEGymEnv
-
-        env = RoboMMEGymEnv.__new__(RoboMMEGymEnv)
-
-        front = np.full((256, 256, 3), 42, dtype=np.uint8)
-        wrist = np.full((256, 256, 3), 7, dtype=np.uint8)
-        joints = np.arange(7, dtype=np.float32)
-        gripper = np.array([0.5, 0.5], dtype=np.float32)
-
-        obs_raw = {
-            "front_rgb_list": [np.zeros_like(front), front],
-            "wrist_rgb_list": [np.zeros_like(wrist), wrist],
-            "joint_state_list": [np.zeros(7, dtype=np.float32), joints],
-            "gripper_state_list": [np.zeros(2, dtype=np.float32), gripper],
-        }
-
-        result = env._convert_obs(obs_raw)
-        np.testing.assert_array_equal(result["image"], front)
-        np.testing.assert_array_equal(result["wrist_image"], wrist)
-        assert result["state"].shape == (8,)
-        np.testing.assert_array_almost_equal(result["state"][:7], joints)
-        assert result["state"][7] == gripper[0]
-    finally:
-        _uninstall_robomme_stub()
-
-
-def test_create_robomme_envs_multi_task():
-    _install_robomme_stub()
-    try:
-        from lerobot.envs.robomme import create_robomme_envs
-
-        env_cls = MagicMock(return_value=MagicMock())
-        result = create_robomme_envs(
-            task="PickXtimes,BinFill,StopCube",
-            n_envs=1,
-            env_cls=env_cls,
-        )
-
-        assert set(result.keys()) == {"PickXtimes", "BinFill", "StopCube"}
-    finally:
-        _uninstall_robomme_stub()
@@ -429,67 +429,6 @@ def test_set_half_turn_homings(mock_motors, dummy_motors):
    assert all(mock_motors.stubs[stub].wait_called() for stub in write_homing_stubs)


-@pytest.mark.parametrize(
-    "initial_phase, expected_phase",
-    [
-        (0b00010000, 0b00000000),  # bit 4 set - cleared
-        (0b11111111, 0b11101111),  # all bits set - bit 4 cleared, others preserved
-        (0b00000000, 0b00000000),  # bit 4 already 0 - unchanged
-    ],
-    ids=["bit4_set", "all_bits_set", "bit4_already_cleared"],
-)
-def test_configure_motors_clears_sts3215_phase_bit4(initial_phase, expected_phase, mock_motors, dummy_motors):
-    """Phase register bit 4 (angle feedback mode) must be cleared for sts3215, other bits preserved."""
-    phase_read_stubs = []
-    phase_write_stubs = []
-    for motor in dummy_motors.values():
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Return_Delay_Time"], motor.id, 0)
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Maximum_Acceleration"], motor.id, 254)
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Acceleration"], motor.id, 254)
-        phase_read_stubs.append(
-            mock_motors.build_read_stub(*STS_SMS_SERIES_CONTROL_TABLE["Phase"], motor.id, initial_phase)
-        )
-        if initial_phase != expected_phase:
-            phase_write_stubs.append(
-                mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Phase"], motor.id, expected_phase)
-            )
-
-    bus = FeetechMotorsBus(port=mock_motors.port, motors=dummy_motors)
-    bus.connect(handshake=False)
-
-    with patch.object(bus, "write", wraps=bus.write) as mock_write:
-        bus.configure_motors()
-
-    assert all(mock_motors.stubs[stub].called for stub in phase_read_stubs)
-    if initial_phase != expected_phase:  # ensure that phase is written only if it needs to be changed
-        assert all(mock_motors.stubs[stub].wait_called() for stub in phase_write_stubs)
-    else:  # If no write should be made, ensure that Phase is not written for any motor
-        write_data_names = [call.args[0] for call in mock_write.call_args_list]
-        assert "Phase" not in write_data_names
-
-
-def test_configure_motors_skips_phase_for_non_sts3215(mock_motors):
-    """Phase register must not be touched for motors other than sts3215."""
-    motors = {
-        "dummy_1": Motor(1, "sts3250", MotorNormMode.RANGE_M100_100),
-        "dummy_2": Motor(2, "sts3250", MotorNormMode.RANGE_M100_100),
-        "dummy_3": Motor(3, "sts3250", MotorNormMode.RANGE_M100_100),
-    }
-    for motor in motors.values():
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Return_Delay_Time"], motor.id, 0)
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Maximum_Acceleration"], motor.id, 254)
-        mock_motors.build_write_stub(*STS_SMS_SERIES_CONTROL_TABLE["Acceleration"], motor.id, 254)
-
-    bus = FeetechMotorsBus(port=mock_motors.port, motors=motors)
-    bus.connect(handshake=False)
-
-    with patch.object(bus, "read", wraps=bus.read) as mock_read:
-        bus.configure_motors()
-        read_data_names = [call.args[0] for call in mock_read.call_args_list]
-
-    assert "Phase" not in read_data_names
-
-
 def test_record_ranges_of_motion(mock_motors, dummy_motors):
    positions = {
        1: [351, 42, 1337],
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
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@@ -69,7 +69,7 @@ wheels = [

 [[package]]
 name = "aiohttp"
-version = "3.13.5"
+version = "3.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "aiohappyeyeballs" },
@@ -80,76 +80,76 @@ dependencies = [
    { name = "propcache" },
    { name = "yarl" },
 ]
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				`@@ -1 +0,0 @@`
				`# Copyright 2026 The HuggingFace Inc. team. All rights reserved.`