Tune native HTTP range diagnostics

* fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion when videos are not used

* fix(docstrings): improving docstrings

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

---------

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

.github/workflows/benchmark_tests.yml

@@ -167,9 +167,9 @@ jobs:
 
       # ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
       # Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
-      # immediately runs eval inside the training loop (env_eval_freq=1, 1 episode).
+      # 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, env_eval_freq=1)
+      - 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 \
@@ -196,7 +196,7 @@ jobs:
                 --output_dir=/tmp/train-smoke \
                 --steps=1 \
                 --batch_size=1 \
-                --env_eval_freq=1 \
+                --eval_freq=1 \
                 --eval.n_episodes=1 \
                 --eval.batch_size=1 \
                 --eval.use_async_envs=false \

Makefile

@@ -58,7 +58,7 @@ test-act-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \
@@ -96,7 +96,7 @@ test-diffusion-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -126,7 +126,7 @@ test-tdmpc-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -161,7 +161,7 @@ test-smolvla-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \

docs/source/hilserl.mdx

@@ -719,7 +719,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
   "num_workers": 4,
   "steps": 5000,
   "log_freq": 10,
-  "env_eval_freq": 1000,
+  "eval_freq": 1000,
   "save_freq": 1000,
   "save_checkpoint": true,
   "seed": 2,

docs/source/libero.mdx

@@ -143,7 +143,7 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Reproducing published results

docs/source/libero_plus.mdx

@@ -173,7 +173,7 @@ lerobot-train \
     --batch_size=4 \
     --eval.batch_size=1 \
     --eval.n_episodes=1 \
-    --env_eval_freq=1000
+    --eval_freq=1000
 ```
 
 ## Relationship to LIBERO

docs/source/metaworld.mdx

@@ -120,11 +120,11 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Practical tips
 
 - Use the one-hot task conditioning for multi-task training (MT10/MT50 conventions) so policies have explicit task context.
 - Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.
-- Adjust `batch_size`, `steps`, and `env_eval_freq` to match your compute budget.
+- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.

docs/source/molmoact2.mdx

@@ -103,7 +103,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```
@@ -142,7 +142,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```

docs/source/multi_task_dit.mdx

@@ -314,7 +314,7 @@ lerobot-train \
   --steps=30000 \
   --save_freq=1000 \
   --log_freq=100 \
-  --env_eval_freq=1000 \
+  --eval_freq=1000 \
   --policy.type=multi_task_dit \
   --policy.device=cuda \
   --policy.horizon=32 \

docs/source/robocasa.mdx

@@ -166,7 +166,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_robocasa_CloseFridge \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=5 \
   --save_freq=10000

docs/source/vlabench.mdx

@@ -165,7 +165,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_vlabench_primitive \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
   --save_freq=10000

pyproject.toml

@@ -355,6 +355,8 @@ explicit = true
 [tool.uv.sources]
 torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
 torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
+huggingface-hub = { git = "https://github.com/huggingface/huggingface_hub.git", branch = "feat/hffs-cache-cdn-range-reads" }
+datasets = { git = "https://github.com/huggingface/datasets.git", branch = "main" }
 
 [tool.setuptools.package-data]
 lerobot = ["envs/*.json", "annotations/steerable_pipeline/prompts/*.txt"]
@@ -421,6 +423,7 @@ exclude_dirs = [
 skips = ["B101", "B311", "B404", "B603", "B615"]
 
 [tool.typos]
+default.extend-words = { trak = "trak" }
 default.extend-ignore-re = [
     "(?Rm)^.*(#|//)\\s*spellchecker:disable-line$",                      # spellchecker:disable-line
     "(?s)(#|//)\\s*spellchecker:off.*?\\n\\s*(#|//)\\s*spellchecker:on", # spellchecker:<on|off>

scripts/bench_episode_byte_cache.py

@@ -0,0 +1,860 @@
+#!/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
+
+from __future__ import annotations
+
+import argparse
+import random
+import resource
+import tempfile
+import threading
+import time
+from collections.abc import Sequence
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import fsspec
+import numpy as np
+import pyarrow as pa
+import pyarrow.compute as pc
+import pyarrow.parquet as pq
+
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.episode_video_streaming import (
+    EpisodeByteCache,
+    EpisodeVideoManifest,
+    NativeHTTPRangeFetcher,
+    assert_hf_hub_range_cache_branch,
+)
+from lerobot.datasets.video_utils import VideoDecoderCache, decode_video_frames_torchcodec
+
+DEFAULT_REPO = "allenai/MolmoAct2-BimanualYAM-Dataset"
+DEFAULT_REVISION = "e9f21ae15074330839f2ac25ed4b49d76dfa1f9c"
+DEFAULT_DATA_ROOT = "hf://buckets/pepijn223/MolmoAct2-BimanualYAM-Dataset-bucket"
+SIDECAR_CACHE_DIR = Path(tempfile.gettempdir()) / "lerobot-sidecars"
+FULL_SIDECAR_NAME = "molmoact2-full.npz"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Benchmark episode-level streaming mini-MP4 cache.")
+    parser.add_argument("--repo-id", default=DEFAULT_REPO)
+    parser.add_argument("--revision", default=DEFAULT_REVISION)
+    parser.add_argument("--data-root", default=DEFAULT_DATA_ROOT)
+    parser.add_argument(
+        "--strategy",
+        choices=("both", "full", "indexed", "remote-decoder", "native-http"),
+        default="both",
+        help=argparse.SUPPRESS,
+    )
+    parser.add_argument(
+        "--range-backend",
+        choices=("fsspec", "native-http"),
+        default="fsspec",
+        help="Range reader used by indexed/full episode-pool fetch tracks.",
+    )
+    parser.add_argument("--num-episodes", type=int, default=512)
+    parser.add_argument(
+        "--manifest-episodes",
+        type=int,
+        default=None,
+        help="Limit manifest construction to the first N episodes for local smoke tests.",
+    )
+    parser.add_argument("--pool-size", type=int, default=16)
+    parser.add_argument("--workers", type=int, default=8)
+    parser.add_argument(
+        "--native-http-connections",
+        type=int,
+        default=None,
+        help="Max HTTP connections for --range-backend native-http. Defaults to --workers.",
+    )
+    parser.add_argument(
+        "--native-http-retries",
+        type=int,
+        default=8,
+        help="Retries per native HTTP range request.",
+    )
+    parser.add_argument(
+        "--native-http-timeout",
+        type=float,
+        default=120.0,
+        help="Timeout in seconds for native HTTP requests.",
+    )
+    parser.add_argument(
+        "--include-decode",
+        action="store_true",
+        help="Also run decoder-opening/frame-decode comparison tracks. Fetch-only is the default.",
+    )
+    parser.add_argument("--decode-workers", type=int, default=1)
+    parser.add_argument("--prefetch-ahead", type=int, default=8)
+    parser.add_argument("--frames-per-episode", type=int, default=16)
+    parser.add_argument("--max-probe-mb", type=int, default=64)
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument("--byte-budget-gb", type=float, default=80)
+    parser.add_argument(
+        "--in-memory", action="store_true", help="Accepted for compatibility; manifest is always in memory."
+    )
+    parser.add_argument("--no-hub-branch-assert", action="store_true")
+    return parser.parse_args()
+
+
+def _episode_pool(total: int, requested: int, pool_size: int, seed: int) -> list[int]:
+    rng = random.Random(seed)
+    upper = min(total, requested)
+    if pool_size > upper:
+        raise ValueError(f"pool-size={pool_size} exceeds available episodes={upper}")
+    return rng.sample(range(upper), pool_size)
+
+
+def _timestamps(manifest: EpisodeVideoManifest, episodes: Sequence[int], frames_per_episode: int, seed: int):
+    rng = random.Random(seed)
+    out: dict[tuple[int, str], list[float]] = {}
+    for ep in episodes:
+        for camera_key in manifest.video_keys:
+            span = manifest.lookup(ep, camera_key)
+            lo = span.first_pts
+            hi = max(span.last_pts, lo)
+            out[(ep, camera_key)] = sorted(rng.uniform(lo, hi) for _ in range(frames_per_episode))
+    return out
+
+
+def _timestamps_from_meta(
+    meta: LeRobotDatasetMetadata, episodes: Sequence[int], frames_per_episode: int, seed: int
+) -> dict[tuple[int, str], list[float]]:
+    rng = random.Random(seed)
+    out: dict[tuple[int, str], list[float]] = {}
+    for ep in episodes:
+        row = meta.episodes[ep]
+        for camera_key in meta.video_keys:
+            lo = float(row[f"videos/{camera_key}/from_timestamp"])
+            hi = max(float(row[f"videos/{camera_key}/to_timestamp"]), lo)
+            out[(ep, camera_key)] = sorted(rng.uniform(lo, hi) for _ in range(frames_per_episode))
+    return out
+
+
+def _bytes_for(manifest: EpisodeVideoManifest, episodes: Sequence[int]) -> int:
+    total = 0
+    for ep in episodes:
+        for camera_key in manifest.video_keys:
+            total += manifest.lookup(ep, camera_key).mdat_length
+    return total
+
+
+def _decode_all(
+    cache: EpisodeByteCache, timestamps: dict[tuple[int, str], list[float]], *, decode_workers: int
+) -> float:
+    start = time.perf_counter()
+    items = list(timestamps.items())
+    if decode_workers <= 1:
+        for (ep, camera_key), ts in items:
+            cache.get_frames(ep, camera_key, ts)
+    else:
+        with ThreadPoolExecutor(max_workers=decode_workers) as pool:
+            futures = [pool.submit(cache.get_frames, ep, camera_key, ts) for (ep, camera_key), ts in items]
+            for future in futures:
+                future.result()
+    return time.perf_counter() - start
+
+
+def _fill_cache(cache: EpisodeByteCache, episodes: Sequence[int]) -> float:
+    start = time.perf_counter()
+    for ep in episodes:
+        cache.submit_prefetch(ep)
+    for ep in episodes:
+        cache.ensure_ready(ep)
+    return time.perf_counter() - start
+
+
+def _samples_per_s(elapsed_s: float, episodes: Sequence[int], frames_per_episode: int) -> float:
+    if elapsed_s <= 0:
+        return float("inf")
+    return len(episodes) * frames_per_episode / elapsed_s
+
+
+def _log(message: str) -> None:
+    print(message, flush=True)
+
+
+def _format_duration(seconds: float) -> str:
+    if seconds < 60:
+        return f"{seconds:.1f}s"
+    if seconds < 3600:
+        return f"{seconds / 60:.1f}m"
+    return f"{seconds / 3600:.1f}h"
+
+
+def _current_rss_mib() -> float | None:
+    status_path = Path("/proc/self/status")
+    if not status_path.exists():
+        return None
+    for line in status_path.read_text().splitlines():
+        if line.startswith("VmRSS:"):
+            return float(line.split()[1]) / 1024
+    return None
+
+
+def _peak_rss_mib() -> float:
+    rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+    # Linux reports KiB; macOS reports bytes.
+    if rss > 10**8:
+        return rss / 1024**2
+    return rss / 1024
+
+
+def _memory_snapshot() -> dict[str, float | None]:
+    return {"rss_mib": _current_rss_mib(), "peak_rss_mib": _peak_rss_mib()}
+
+
+def _print_memory_summary(start: dict[str, float | None], end: dict[str, float | None]) -> None:
+    start_rss = start["rss_mib"]
+    end_rss = end["rss_mib"]
+    delta = None if start_rss is None or end_rss is None else end_rss - start_rss
+    print()
+    print("| Memory | MiB |")
+    print("|---|---:|")
+    if start_rss is not None:
+        print(f"| rss start | {start_rss:.1f} |")
+    if end_rss is not None:
+        print(f"| rss end | {end_rss:.1f} |")
+    if delta is not None:
+        print(f"| rss delta | {delta:.1f} |")
+    print(f"| peak rss | {end['peak_rss_mib']:.1f} |")
+
+
+def _root_join(data_root: str, relative_path: str) -> str:
+    if data_root.startswith("hf://"):
+        return f"{data_root.rstrip('/')}/{relative_path}"
+    return str(Path(data_root) / relative_path)
+
+
+def _find_or_download_sidecar(data_root: str, manifest_episode_count: int) -> Path | None:
+    _ = manifest_episode_count
+    local = SIDECAR_CACHE_DIR / FULL_SIDECAR_NAME
+    if _valid_sidecar(local):
+        return local
+    if local.exists():
+        print(f"mp4_sidecar_invalid_local: {local}")
+        local.unlink()
+    remote_relative = f"meta/mp4-sidecars/{FULL_SIDECAR_NAME}"
+    remote = _root_join(data_root, remote_relative)
+    protocol = "hf" if data_root.startswith("hf://") else "file"
+    fs = fsspec.filesystem(protocol)
+    if not fs.exists(remote):
+        return None
+    local.parent.mkdir(parents=True, exist_ok=True)
+    print(f"downloading_mp4_sidecar: {remote} -> {local}")
+    if data_root.startswith("hf://"):
+        _download_sidecar_native_http(data_root, remote_relative, local)
+    else:
+        fs.get(remote, str(local))
+    return local
+
+
+def _valid_sidecar(path: Path) -> bool:
+    if not path.exists():
+        return False
+    try:
+        with np.load(path, allow_pickle=False) as data:
+            return "manifest_json" in data
+    except Exception:
+        return False
+
+
+def _download_sidecar_native_http(data_root: str, relative_path: str, local: Path) -> None:
+    fetcher = NativeHTTPRangeFetcher(data_root, max_connections=16)
+    tmp = local.with_suffix(local.suffix + ".tmp")
+    try:
+        size = fetcher.info_size(relative_path)
+        chunk_size = 16 * 1024 * 1024
+        ranges = [(offset, min(chunk_size, size - offset)) for offset in range(0, size, chunk_size)]
+        with tmp.open("wb") as out_file:
+            out_file.truncate(size)
+
+        def read_chunk(offset_length: tuple[int, int]) -> tuple[int, bytes]:
+            offset, length = offset_length
+            return offset, fetcher.read_range(relative_path, offset, length)
+
+        start = time.perf_counter()
+        done = 0
+        with ThreadPoolExecutor(max_workers=8) as pool:
+            futures = [pool.submit(read_chunk, item) for item in ranges]
+            with tmp.open("r+b") as rw_file:
+                for future in futures:
+                    offset, data = future.result()
+                    rw_file.seek(offset)
+                    rw_file.write(data)
+                    done += len(data)
+                    elapsed = max(time.perf_counter() - start, 1e-9)
+                    print(
+                        f"sidecar_download: {done / 1024**2:.1f}/{size / 1024**2:.1f} MiB "
+                        f"({done / elapsed / 1024**2:.1f} MiB/s)",
+                        flush=True,
+                    )
+        tmp.replace(local)
+    finally:
+        fetcher.close()
+
+
+class EpisodeParquetReader:
+    def __init__(self, meta: LeRobotDatasetMetadata, data_root: str):
+        self.meta = meta
+        self.data_root = data_root
+        protocol = "hf" if data_root.startswith("hf://") else "file"
+        self.fs = fsspec.filesystem(protocol)
+        self._episode_row_groups = self._build_episode_row_groups()
+        self._table_cache: dict[str, pa.Table] = {}
+        self._cache_lock = threading.Lock()
+
+    def read_episode(self, episode_index: int) -> None:
+        relative_path = str(self.meta.get_data_file_path(episode_index))
+        table = self._read_table(relative_path)
+        table.filter(pc.equal(table["episode_index"], episode_index))
+
+    def _read_table(self, relative_path: str) -> pa.Table:
+        with self._cache_lock:
+            table = self._table_cache.get(relative_path)
+        if table is not None:
+            return table
+        with self.fs.open(
+            _root_join(self.data_root, relative_path), "rb", block_size=2**20, cache_type="none"
+        ) as f:
+            table = pq.ParquetFile(f).read()
+        with self._cache_lock:
+            return self._table_cache.setdefault(relative_path, table)
+
+    def submit_read_episode(self, pool: ThreadPoolExecutor, episode_index: int):
+        return pool.submit(self.read_episode, episode_index)
+
+    def read_episodes(self, episodes: Sequence[int], *, workers: int) -> float:
+        start = time.perf_counter()
+        if workers <= 1:
+            for ep in episodes:
+                self.read_episode(ep)
+        else:
+            with ThreadPoolExecutor(max_workers=workers) as pool:
+                futures = [pool.submit(self.read_episode, ep) for ep in episodes]
+                for future in futures:
+                    future.result()
+        return time.perf_counter() - start
+
+    def _build_episode_row_groups(self) -> dict[int, int]:
+        counts: dict[tuple[int, int], int] = {}
+        row_groups = {}
+        for ep_idx in range(int(self.meta.total_episodes)):
+            ep = self.meta.episodes[ep_idx]
+            key = (int(ep["data/chunk_index"]), int(ep["data/file_index"]))
+            row_groups[ep_idx] = counts.get(key, 0)
+            counts[key] = row_groups[ep_idx] + 1
+        return row_groups
+
+
+def run_fetch_pool(
+    manifest: EpisodeVideoManifest,
+    data_root: str,
+    episodes: Sequence[int],
+    byte_budget: int,
+    workers: int,
+    range_backend: str,
+    args: argparse.Namespace,
+) -> dict[str, float]:
+    with EpisodeByteCache(
+        manifest,
+        data_root,
+        byte_budget=byte_budget,
+        workers=workers,
+        range_backend=range_backend,
+        native_http_connections=args.native_http_connections,
+        native_http_timeout=args.native_http_timeout,
+        native_http_retries=args.native_http_retries,
+        open_decoders=False,
+    ) as cache:
+        elapsed = _fill_cache(cache, episodes)
+        timings = cache.timing_summary()
+    byte_count = _bytes_for(manifest, episodes)
+    episode_mb = byte_count / len(episodes) / 1024**2
+    job_count = max(timings["jobs"], 1.0)
+    result = {
+        "fetch_s": elapsed,
+        "fetch_mbps": byte_count / elapsed / 1024**2,
+        "fetch_episodes_s": len(episodes) / elapsed,
+        "episode_mb": episode_mb,
+        "avg_mb_miss": byte_count / (len(episodes) * len(manifest.video_keys)) / 1024**2,
+        "jobs": timings["jobs"],
+        "lookup_ms": timings["lookup_s"] * 1000 / job_count,
+        "range_fetch_ms": timings["fetch_s"] * 1000 / job_count,
+        "synthesize_ms": timings["synthesize_s"] * 1000 / job_count,
+        "store_ms": timings["store_s"] * 1000 / job_count,
+    }
+    result.update({key: value for key, value in timings.items() if key.startswith("range_")})
+    return result
+
+
+def run_parallel(
+    manifest: EpisodeVideoManifest,
+    data_root: str,
+    episodes: Sequence[int],
+    timestamps: dict[tuple[int, str], list[float]],
+    byte_budget: int,
+    workers: int,
+    decode_workers: int,
+    frames_per_episode: int,
+    parquet_reader: EpisodeParquetReader,
+    range_backend: str,
+) -> dict[str, float]:
+    with EpisodeByteCache(
+        manifest,
+        data_root,
+        byte_budget=byte_budget,
+        workers=workers,
+        range_backend=range_backend,
+        open_decoders=False,
+    ) as cache:
+        parquet_s = parquet_reader.read_episodes(episodes, workers=workers)
+        fetch_s = _fill_cache(cache, episodes)
+        decoder_start = time.perf_counter()
+        for ep in episodes:
+            for camera_key in manifest.video_keys:
+                cache.get_decoder(ep, camera_key)
+        decoder_s = time.perf_counter() - decoder_start
+        decode_s = _decode_all(cache, timestamps, decode_workers=decode_workers)
+    byte_count = _bytes_for(manifest, episodes)
+    return {
+        "fetch_s": fetch_s,
+        "fetch_mbps": byte_count / fetch_s / 1024**2,
+        "fetch_episodes_s": len(episodes) / fetch_s,
+        "parquet_s": parquet_s,
+        "decoder_ms_miss": decoder_s * 1000 / (len(episodes) * len(manifest.video_keys)),
+        "decode_samples_s": _samples_per_s(decode_s, episodes, frames_per_episode),
+    }
+
+
+def run_overlapped(
+    manifest: EpisodeVideoManifest,
+    data_root: str,
+    episodes: Sequence[int],
+    timestamps: dict[tuple[int, str], list[float]],
+    byte_budget: int,
+    workers: int,
+    decode_workers: int,
+    frames_per_episode: int,
+    prefetch_ahead: int,
+    parquet_reader: EpisodeParquetReader,
+    range_backend: str,
+) -> dict[str, float]:
+    with EpisodeByteCache(
+        manifest,
+        data_root,
+        byte_budget=byte_budget,
+        workers=workers,
+        range_backend=range_backend,
+        open_decoders=True,
+    ) as cache:
+        start = time.perf_counter()
+        video_wait_decode_s = 0.0
+        parquet_wait_s = 0.0
+        parquet_pool = ThreadPoolExecutor(max_workers=max(1, min(workers, len(episodes))))
+        parquet_futures = {
+            ep: parquet_reader.submit_read_episode(parquet_pool, ep) for ep in episodes[:prefetch_ahead]
+        }
+        for ep in episodes[:prefetch_ahead]:
+            cache.submit_prefetch(ep)
+        try:
+            for idx, ep in enumerate(episodes):
+                next_idx = idx + prefetch_ahead
+                if next_idx < len(episodes):
+                    next_ep = episodes[next_idx]
+                    cache.submit_prefetch(next_ep)
+                    parquet_futures[next_ep] = parquet_reader.submit_read_episode(parquet_pool, next_ep)
+
+                parquet_start = time.perf_counter()
+                parquet_futures.pop(ep).result()
+                parquet_wait_s += time.perf_counter() - parquet_start
+
+                video_start = time.perf_counter()
+                cache.ensure_ready(ep)
+                if decode_workers <= 1:
+                    for camera_key in manifest.video_keys:
+                        cache.get_frames(ep, camera_key, timestamps[(ep, camera_key)])
+                else:
+                    with ThreadPoolExecutor(max_workers=decode_workers) as pool:
+                        futures = [
+                            pool.submit(cache.get_frames, ep, camera_key, timestamps[(ep, camera_key)])
+                            for camera_key in manifest.video_keys
+                        ]
+                        for future in futures:
+                            future.result()
+                video_wait_decode_s += time.perf_counter() - video_start
+        finally:
+            parquet_pool.shutdown(wait=True)
+        elapsed = time.perf_counter() - start
+    return {
+        "samples_s": _samples_per_s(elapsed, episodes, frames_per_episode),
+        "video_samples_s": _samples_per_s(video_wait_decode_s, episodes, frames_per_episode),
+        "parquet_samples_s": _samples_per_s(parquet_wait_s, episodes, frames_per_episode),
+        "wall_s": elapsed,
+        "video_wait_decode_s": video_wait_decode_s,
+        "parquet_wait_s": parquet_wait_s,
+    }
+
+
+_remote_decoder_local = threading.local()
+
+
+def _remote_decoder_cache() -> VideoDecoderCache:
+    cache = getattr(_remote_decoder_local, "cache", None)
+    if cache is None:
+        cache = VideoDecoderCache(max_size=None)
+        _remote_decoder_local.cache = cache
+    return cache
+
+
+def _decode_remote_source(
+    meta: LeRobotDatasetMetadata,
+    data_root: str,
+    episode_index: int,
+    camera_key: str,
+    timestamps: list[float],
+):
+    video_path = _root_join(data_root, str(meta.get_video_file_path(episode_index, camera_key)))
+    return decode_video_frames_torchcodec(
+        video_path,
+        timestamps,
+        tolerance_s=1.0 / float(meta.fps),
+        decoder_cache=_remote_decoder_cache(),
+        return_uint8=True,
+    )
+
+
+def run_remote_decoder(
+    meta: LeRobotDatasetMetadata,
+    data_root: str,
+    episodes: Sequence[int],
+    timestamps: dict[tuple[int, str], list[float]],
+    *,
+    frames_per_episode: int,
+    decode_workers: int,
+    parquet_reader: EpisodeParquetReader,
+) -> dict[str, float]:
+    items = [
+        (ep, camera_key, timestamps[(ep, camera_key)]) for ep in episodes for camera_key in meta.video_keys
+    ]
+
+    start = time.perf_counter()
+    for ep, camera_key, ts in items:
+        if camera_key == meta.video_keys[0]:
+            parquet_reader.read_episode(ep)
+        _decode_remote_source(meta, data_root, ep, camera_key, ts)
+    sequential_s = time.perf_counter() - start
+
+    start = time.perf_counter()
+    if decode_workers <= 1:
+        for ep, camera_key, ts in items:
+            if camera_key == meta.video_keys[0]:
+                parquet_reader.read_episode(ep)
+            _decode_remote_source(meta, data_root, ep, camera_key, ts)
+    else:
+        with ThreadPoolExecutor(max_workers=decode_workers) as pool:
+            parquet_futures = [pool.submit(parquet_reader.read_episode, ep) for ep in episodes]
+            futures = [
+                pool.submit(_decode_remote_source, meta, data_root, ep, camera_key, ts)
+                for ep, camera_key, ts in items
+            ]
+            for future in parquet_futures:
+                future.result()
+            for future in futures:
+                future.result()
+    parallel_s = time.perf_counter() - start
+
+    return {
+        "sequential_samples_s": _samples_per_s(sequential_s, episodes, frames_per_episode),
+        "parallel_samples_s": _samples_per_s(parallel_s, episodes, frames_per_episode),
+    }
+
+
+def _print_range_timing_summary(fetch_pool: dict[str, float]) -> None:
+    range_jobs = fetch_pool.get("range_jobs", 0.0)
+    if range_jobs <= 0:
+        return
+
+    print()
+    print("| Range Read Stage | avg ms/range |")
+    print("|---|---:|")
+    for key, label in (
+        ("range_open_s", "fsspec handle open/lookup"),
+        ("range_seek_s", "fsspec seek"),
+        ("range_read_s", "fsspec read"),
+        ("range_resolve_s", "http URL resolve"),
+        ("range_header_s", "http response headers"),
+        ("range_first_byte_s", "http first body byte"),
+        ("range_body_s", "http body drain"),
+        ("range_retry_sleep_s", "http retry sleep"),
+    ):
+        value = fetch_pool.get(key)
+        if value is not None:
+            print(f"| {label} | {value * 1000 / range_jobs:.3f} |")
+    if "range_retry_attempts" in fetch_pool:
+        print(f"| http retries | {fetch_pool['range_retry_attempts'] / range_jobs:.3f} |")
+    if fetch_pool.get("range_failed_requests"):
+        print(f"| http failed requests | {fetch_pool['range_failed_requests']:.0f} |")
+    print(f"| range reads | {range_jobs:.0f} |")
+    print(f"| avg MiB/range | {fetch_pool.get('range_bytes', 0.0) / range_jobs / 1024**2:.1f} |")
+
+
+def run_indexed_strategy(
+    meta: LeRobotDatasetMetadata,
+    data_root: str,
+    args: argparse.Namespace,
+    parquet_reader: EpisodeParquetReader,
+    *,
+    range_backend: str = "fsspec",
+    label: str = "indexed",
+    sidecar_path: str | None = None,
+) -> None:
+    _log(f"starting_strategy: {label}")
+    memory_start = _memory_snapshot()
+    manifest_start = time.perf_counter()
+    dataset_episode_count = int(meta.total_episodes)
+    manifest_episode_count = args.manifest_episodes or dataset_episode_count
+    manifest_episode_count = min(manifest_episode_count, dataset_episode_count, args.num_episodes)
+    manifest = EpisodeVideoManifest.build(
+        meta,
+        data_root,
+        episode_indices=range(manifest_episode_count),
+        range_backend=range_backend,
+        workers=args.workers,
+        max_probe_bytes=args.max_probe_mb * 1024 * 1024,
+        sidecar_path=sidecar_path,
+    )
+    manifest_s = time.perf_counter() - manifest_start
+    _log(f"{label}: manifest_build_s={manifest_s:.2f}")
+
+    benchmark_episode_count = min(dataset_episode_count, args.num_episodes)
+    episodes = _episode_pool(dataset_episode_count, args.num_episodes, args.pool_size, args.seed)
+    byte_budget = int(args.byte_budget_gb * 1024**3)
+    byte_count = _bytes_for(manifest, episodes)
+    _log(
+        f"{label}: planned_video_fetch={byte_count / 1024**3:.2f} GiB per fetch track "
+        f"({byte_count / len(episodes) / 1024**2:.1f} MiB/episode)"
+    )
+
+    _log(f"{label}: filling episode byte cache with {args.workers} workers")
+    fetch_pool = run_fetch_pool(manifest, data_root, episodes, byte_budget, args.workers, range_backend, args)
+    estimated_dataset_s = dataset_episode_count / fetch_pool["fetch_episodes_s"]
+    estimated_benchmark_s = benchmark_episode_count / fetch_pool["fetch_episodes_s"]
+
+    print(f"manifest_build_s: {manifest_s:.2f}")
+    print(f"strategy: {label}")
+    print(f"range_backend: {range_backend}")
+    print(f"mp4_sidecar: {sidecar_path or 'none'}")
+    print(f"data_root: {data_root}")
+    print(f"dataset_episodes: {dataset_episode_count}")
+    print(f"benchmark_episodes: {benchmark_episode_count}")
+    print(f"pool_episodes: {len(episodes)}")
+    print(f"sampled_episodes: {episodes}")
+    print(f"cameras: {manifest.video_keys}")
+    print()
+    print(
+        "| Track | fetch MB/s | fetch eps/s | wall s | est benchmark | est full dataset | avg MB/camera | notes |"
+    )
+    print("|---|---:|---:|---:|---:|---:|---:|---|")
+    print(
+        f"| EPISODE POOL FETCH | {fetch_pool['fetch_mbps']:.1f} | "
+        f"{fetch_pool['fetch_episodes_s']:.2f} | {fetch_pool['fetch_s']:.2f} | "
+        f"{_format_duration(estimated_benchmark_s)} | {_format_duration(estimated_dataset_s)} | "
+        f"{fetch_pool['avg_mb_miss']:.1f} | {args.workers} workers, no decoder open/frame decode |"
+    )
+    print()
+    print("| Camera Job Stage | avg ms/job |")
+    print("|---|---:|")
+    print(f"| manifest lookup | {fetch_pool['lookup_ms']:.3f} |")
+    print(f"| remote byte-range fetch | {fetch_pool['range_fetch_ms']:.3f} |")
+    print(f"| synthesize mini-MP4 | {fetch_pool['synthesize_ms']:.3f} |")
+    print(f"| store in shared cache | {fetch_pool['store_ms']:.3f} |")
+    print(f"| camera jobs | {fetch_pool['jobs']:.0f} |")
+    _print_range_timing_summary(fetch_pool)
+    _print_memory_summary(memory_start, _memory_snapshot())
+
+    if args.include_decode:
+        timestamps = _timestamps(manifest, episodes, args.frames_per_episode, args.seed + 1)
+        _log(f"{label}: running parallel video fetch + decode-only")
+        parallel = run_parallel(
+            manifest,
+            data_root,
+            episodes,
+            timestamps,
+            byte_budget,
+            args.workers,
+            args.decode_workers,
+            args.frames_per_episode,
+            parquet_reader,
+            range_backend,
+        )
+        _log(f"{label}: running overlapped end-to-end")
+        overlapped = run_overlapped(
+            manifest,
+            data_root,
+            episodes,
+            timestamps,
+            byte_budget,
+            args.workers,
+            args.decode_workers,
+            args.frames_per_episode,
+            args.prefetch_ahead,
+            parquet_reader,
+            range_backend,
+        )
+        print(
+            f"| DECODE COMPARISON | {parallel['fetch_mbps']:.1f} | {parallel['fetch_episodes_s']:.2f} | "
+            f"{parallel['fetch_s']:.2f} | "
+            f"{_format_duration(benchmark_episode_count / parallel['fetch_episodes_s'])} | "
+            f"{_format_duration(dataset_episode_count / parallel['fetch_episodes_s'])} | "
+            f"{fetch_pool['avg_mb_miss']:.1f} | "
+            f"decoder open {parallel['decoder_ms_miss']:.1f} ms/miss, "
+            f"decode {parallel['decode_samples_s']:.1f} samples/s, parquet {parallel['parquet_s']:.2f}s |"
+        )
+        print(
+            f"| OVERLAPPED E2E | - | - | {overlapped['wall_s']:.2f} | - | - | "
+            f"{fetch_pool['avg_mb_miss']:.1f} | "
+            f"{overlapped['samples_s']:.1f} samples/s; video+decode "
+            f"{overlapped['video_wait_decode_s']:.2f}s, parquet {overlapped['parquet_wait_s']:.2f}s |"
+        )
+
+
+def run_remote_strategy(
+    meta: LeRobotDatasetMetadata,
+    data_root: str,
+    args: argparse.Namespace,
+    parquet_reader: EpisodeParquetReader,
+) -> None:
+    _log("starting_strategy: remote-decoder")
+    episodes = _episode_pool(int(meta.total_episodes), args.num_episodes, args.pool_size, args.seed)
+    timestamps = _timestamps_from_meta(meta, episodes, args.frames_per_episode, args.seed + 1)
+    _log("remote-decoder: running direct source MP4 decoder")
+    result = run_remote_decoder(
+        meta,
+        data_root,
+        episodes,
+        timestamps,
+        frames_per_episode=args.frames_per_episode,
+        decode_workers=args.decode_workers,
+        parquet_reader=parquet_reader,
+    )
+    print("strategy: remote-decoder")
+    print(f"data_root: {data_root}")
+    print(f"episodes: {episodes}")
+    print(f"cameras: {list(meta.video_keys)}")
+    print()
+    print("| Track | samples/s | notes |")
+    print("|---|---:|---|")
+    print(f"| REMOTE SEQUENTIAL | {result['sequential_samples_s']:.1f} | direct source MP4 decoder |")
+    print(
+        f"| REMOTE PARALLEL | {result['parallel_samples_s']:.1f} | "
+        f"direct source MP4 decoder, {args.decode_workers} workers |"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+    if args.strategy == "full":
+        args.strategy = "both"
+    if args.strategy == "native-http":
+        args.range_backend = "native-http"
+    data_root = args.data_root
+    if data_root.startswith("hf://") and not args.no_hub_branch_assert:
+        assert_hf_hub_range_cache_branch()
+
+    meta = LeRobotDatasetMetadata(args.repo_id, revision=args.revision)
+    meta.ensure_readable()
+    parquet_reader = EpisodeParquetReader(meta, data_root)
+    manifest_episode_count = args.manifest_episodes or int(meta.total_episodes)
+    manifest_episode_count = min(manifest_episode_count, int(meta.total_episodes), args.num_episodes)
+    sidecar_path = _find_or_download_sidecar(data_root, manifest_episode_count)
+
+    if sidecar_path is not None:
+        print(f"using_mp4_sidecar: {sidecar_path}")
+
+    if sidecar_path is not None and args.strategy == "both":
+        if args.include_decode:
+            run_remote_strategy(meta, data_root, args, parquet_reader)
+            print()
+        run_indexed_strategy(
+            meta,
+            data_root,
+            args,
+            parquet_reader,
+            range_backend=args.range_backend,
+            label=f"indexed-sidecar-{args.range_backend}",
+            sidecar_path=str(sidecar_path),
+        )
+        return
+    if sidecar_path is not None and args.strategy == "indexed":
+        run_indexed_strategy(
+            meta,
+            data_root,
+            args,
+            parquet_reader,
+            range_backend=args.range_backend,
+            label=f"indexed-sidecar-{args.range_backend}",
+            sidecar_path=str(sidecar_path),
+        )
+        return
+    if sidecar_path is not None and args.strategy == "native-http":
+        run_indexed_strategy(
+            meta,
+            data_root,
+            args,
+            parquet_reader,
+            range_backend="native-http",
+            label="indexed-sidecar-native-http",
+            sidecar_path=str(sidecar_path),
+        )
+        return
+    if args.strategy == "both":
+        expected_sidecar = SIDECAR_CACHE_DIR / FULL_SIDECAR_NAME
+        expected_remote = _root_join(data_root, f"meta/mp4-sidecars/{FULL_SIDECAR_NAME}")
+        print(f"mp4_sidecar_missing_local: {expected_sidecar}")
+        print(f"mp4_sidecar_missing_remote: {expected_remote}")
+        print(
+            "build_mp4_sidecar: "
+            "uv run --no-sync python scripts/build_mp4_sidecar.py "
+            f"--workers {args.workers} --range-backend native-http --output {expected_sidecar}"
+        )
+        print("running_without_mp4_sidecar: indexed variants will build MP4 indexes online")
+        print()
+
+    if args.strategy in ("both", "indexed"):
+        run_indexed_strategy(
+            meta,
+            data_root,
+            args,
+            parquet_reader,
+            range_backend="fsspec",
+            label="indexed",
+            sidecar_path=None,
+        )
+    if args.strategy == "both":
+        print()
+    if args.strategy == "remote-decoder" or (args.strategy == "both" and args.include_decode):
+        run_remote_strategy(meta, data_root, args, parquet_reader)
+    if args.strategy == "both" and args.include_decode:
+        print()
+    if args.strategy in ("both", "native-http"):
+        run_indexed_strategy(
+            meta,
+            data_root,
+            args,
+            parquet_reader,
+            range_backend="native-http",
+            label="indexed-native-http",
+            sidecar_path=None,
+        )
+
+
+if __name__ == "__main__":
+    main()

scripts/build_mp4_sidecar.py

@@ -0,0 +1,93 @@
+#!/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
+
+from __future__ import annotations
+
+import argparse
+import time
+from pathlib import Path
+
+import fsspec
+
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.episode_video_streaming import EpisodeVideoManifest, assert_hf_hub_range_cache_branch
+
+DEFAULT_REPO = "allenai/MolmoAct2-BimanualYAM-Dataset"
+DEFAULT_REVISION = "e9f21ae15074330839f2ac25ed4b49d76dfa1f9c"
+DEFAULT_DATA_ROOT = "hf://buckets/pepijn223/MolmoAct2-BimanualYAM-Dataset-bucket"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Build a reusable MP4 byte-index sidecar for streaming.")
+    parser.add_argument("--repo-id", default=DEFAULT_REPO)
+    parser.add_argument("--revision", default=DEFAULT_REVISION)
+    parser.add_argument("--data-root", default=DEFAULT_DATA_ROOT)
+    parser.add_argument("--output", required=True)
+    parser.add_argument("--episodes", type=int, default=None)
+    parser.add_argument("--workers", type=int, default=8)
+    parser.add_argument("--range-backend", choices=("fsspec", "native-http"), default="native-http")
+    parser.add_argument("--max-probe-mb", type=int, default=64)
+    parser.add_argument(
+        "--no-push", action="store_true", help="Do not upload the sidecar to data_root/meta/mp4-sidecars."
+    )
+    parser.add_argument("--no-hub-branch-assert", action="store_true")
+    return parser.parse_args()
+
+
+def push_sidecar(local_path: str, data_root: str) -> list[str]:
+    if not data_root.startswith("hf://"):
+        return []
+
+    local = Path(local_path)
+    fs = fsspec.filesystem("hf")
+    remote_dir = f"{data_root.rstrip('/')}/meta/mp4-sidecars"
+    remote_paths = [f"{remote_dir}/{local.name}"]
+
+    for remote in remote_paths:
+        fs.put(str(local), remote)
+    return remote_paths
+
+
+def main() -> None:
+    args = parse_args()
+    if args.data_root.startswith("hf://") and not args.no_hub_branch_assert:
+        assert_hf_hub_range_cache_branch()
+
+    meta = LeRobotDatasetMetadata(args.repo_id, revision=args.revision)
+    meta.ensure_readable()
+    total = (
+        int(meta.total_episodes) if args.episodes is None else min(args.episodes, int(meta.total_episodes))
+    )
+    rel_paths = sorted(
+        {str(meta.get_video_file_path(ep_idx, key)) for ep_idx in range(total) for key in meta.video_keys}
+    )
+
+    start = time.perf_counter()
+    EpisodeVideoManifest.write_file_sidecar(
+        args.output,
+        rel_paths,
+        args.data_root,
+        range_backend=args.range_backend,
+        workers=args.workers,
+        max_probe_bytes=args.max_probe_mb * 1024 * 1024,
+    )
+    elapsed = time.perf_counter() - start
+    print(f"wrote {args.output}")
+    print(f"episodes={total} files={len(rel_paths)} elapsed_s={elapsed:.2f}")
+    if args.no_push:
+        print("push_skipped: --no-push")
+    else:
+        pushed = push_sidecar(args.output, args.data_root)
+        for remote in pushed:
+            print(f"pushed {remote}")
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/common/wandb_utils.py

@@ -180,32 +180,24 @@ class WandBLogger:
                 self._wandb_custom_step_key.add(new_custom_key)
                 self._wandb.define_metric(new_custom_key, hidden=True)
 
-        batch_data = {}
         for k, v in d.items():
-            # Skip the custom step key here, it's added to the batch below.
-            if custom_step_key is not None and k == custom_step_key:
-                continue
-
-            if isinstance(v, list):
-                for i, elem in enumerate(v):
-                    if isinstance(elem, (int | float)):
-                        batch_data[f"{mode}/{k}_{i}"] = elem
-                continue
-
             if not isinstance(v, (int | float | str)):
                 logging.warning(
                     f'WandB logging of key "{k}" was ignored as its type "{type(v)}" is not handled by this wrapper.'
                 )
                 continue
 
-            batch_data[f"{mode}/{k}"] = v
+            # Do not log the custom step key itself.
+            if self._wandb_custom_step_key is not None and k in self._wandb_custom_step_key:
+                continue
 
-        if batch_data:
             if custom_step_key is not None:
-                batch_data[f"{mode}/{custom_step_key}"] = d[custom_step_key]
-                self._wandb.log(batch_data)
-            else:
-                self._wandb.log(data=batch_data, step=step)
+                value_custom_step = d[custom_step_key]
+                data = {f"{mode}/{k}": v, f"{mode}/{custom_step_key}": value_custom_step}
+                self._wandb.log(data)
+                continue
+
+            self._wandb.log(data={f"{mode}/{k}": v}, step=step)
 
     def log_video(self, video_path: str, step: int, mode: str = "train"):
         if mode not in {"train", "eval"}:

src/lerobot/configs/default.py

@@ -39,8 +39,6 @@ class DatasetConfig:
     # This reduces memory and speeds up DataLoader IPC. The training pipeline handles the conversion.
     return_uint8: bool = False
     streaming: bool = False
-    # Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
-    eval_split: float = 0.0
 
     def __post_init__(self) -> None:
         if self.episodes is not None:
@@ -75,8 +73,6 @@ class EvalConfig:
     # `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
-    # Whether to record eval rollouts as a LeRobot v3.0 dataset on disk.
-    recording: bool = False
 
     def __post_init__(self) -> None:
         if self.batch_size == 0:

src/lerobot/configs/policies.py

@@ -79,8 +79,6 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
     # Either the repo ID of a model hosted on the Hub or a path to a directory containing weights
     # saved using `Policy.save_pretrained`. If not provided, the policy is initialized from scratch.
     pretrained_path: Path | None = None
-    # Optional Hub revision (commit hash, branch, or tag) to pin the pretrained model version.
-    pretrained_revision: str | None = None
 
     def __post_init__(self) -> None:
         if not self.device or not is_torch_device_available(self.device):

src/lerobot/configs/rewards.py

@@ -56,8 +56,6 @@ class RewardModelConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):
     device: str | None = None
 
     pretrained_path: str | None = None
-    # Optional Hub revision (commit hash, branch, or tag) to pin the pretrained reward model version.
-    pretrained_revision: str | None = None
 
     push_to_hub: bool = False
     repo_id: str | None = None

src/lerobot/configs/train.py

@@ -100,13 +100,8 @@ class TrainPipelineConfig(HubMixin):
     prefetch_factor: int = 4
     persistent_workers: bool = True
     steps: int = 100_000
-    # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
-    env_eval_freq: int = 20_000
+    eval_freq: int = 20_000
     log_freq: int = 200
-    # Compute eval loss on held-out episodes every N steps (0 = disabled). Requires eval_split > 0.
-    eval_steps: int = 0
-    # Cap on total eval samples, split uniformly across tasks (0 = use all held-out data).
-    max_eval_samples: int = 0
     tolerance_s: float = 1e-4
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.

src/lerobot/datasets/__init__.py

@@ -35,7 +35,7 @@ from .dataset_tools import (
     remove_feature,
     split_dataset,
 )
-from .factory import make_dataset, make_train_eval_datasets, resolve_delta_timestamps
+from .factory import make_dataset, resolve_delta_timestamps
 from .image_writer import safe_stop_image_writer
 from .io_utils import load_episodes, write_stats
 from .language import (
@@ -89,7 +89,6 @@ __all__ = [
     "get_feature_stats",
     "load_episodes",
     "make_dataset",
-    "make_train_eval_datasets",
     "merge_datasets",
     "modify_features",
     "modify_tasks",

src/lerobot/datasets/episode_video_streaming.py

@@ -0,0 +1,890 @@
+# 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
+
+from __future__ import annotations
+
+import contextlib
+import io
+import json
+import threading
+import time
+from collections import OrderedDict
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from importlib import metadata
+from pathlib import Path
+from typing import Any
+from urllib.parse import quote, urljoin, urlparse
+
+import fsspec
+import httpx
+import numpy as np
+from huggingface_hub import HfApi, HfFileSystem, constants
+from huggingface_hub.utils import hf_raise_for_status
+
+from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+from lerobot.datasets.mp4 import Mp4Index, Mp4SampleSlice, fetch_mp4_index, synthesize_mp4
+
+
+@dataclass(frozen=True)
+class EpisodeVideoSpan:
+    file_id: int
+    mdat_offset: int
+    mdat_length: int
+    first_pts: float
+    last_pts: float
+    frame_count: int
+    sample_lo: int
+    sample_hi: int
+    source_start_pts: float
+
+
+@dataclass(frozen=True)
+class VideoFileRecord:
+    file_path: str
+    file_size: int
+    mp4: Mp4Index
+
+
+class ThreadLocalRangeFetcher:
+    """Range reader that gives each worker thread independent file handles."""
+
+    def __init__(self, data_root: str | Path, *, block_size: int = 2**20, cache_type: str = "none"):
+        self.data_root = str(data_root).rstrip("/")
+        protocol = "hf" if self.data_root.startswith("hf://") else "file"
+        self.fs = fsspec.filesystem(protocol)
+        self.block_size = block_size
+        self.cache_type = cache_type
+        self._local = threading.local()
+        self._timing_lock = threading.Lock()
+        self._timing_totals = {
+            "range_jobs": 0.0,
+            "range_bytes": 0.0,
+            "range_open_s": 0.0,
+            "range_seek_s": 0.0,
+            "range_read_s": 0.0,
+        }
+
+    def _url(self, relative_path: str) -> str:
+        if self.data_root.startswith("hf://"):
+            return f"{self.data_root}/{relative_path}"
+        return str(Path(self.data_root) / relative_path)
+
+    def _handle(self, relative_path: str):
+        handles = getattr(self._local, "handles", None)
+        if handles is None:
+            handles = {}
+            self._local.handles = handles
+        handle = handles.get(relative_path)
+        if handle is None or getattr(handle, "closed", False):
+            handle = self.fs.open(
+                self._url(relative_path), "rb", block_size=self.block_size, cache_type=self.cache_type
+            )
+            handles[relative_path] = handle
+        return handle
+
+    def info_size(self, relative_path: str) -> int:
+        return int(self.fs.info(self._url(relative_path))["size"])
+
+    def read_range(self, relative_path: str, offset: int, length: int) -> bytes:
+        open_start = time.perf_counter()
+        handle = self._handle(relative_path)
+        open_s = time.perf_counter() - open_start
+        seek_start = time.perf_counter()
+        handle.seek(offset)
+        seek_s = time.perf_counter() - seek_start
+        read_start = time.perf_counter()
+        data = handle.read(length)
+        read_s = time.perf_counter() - read_start
+        self._record_timing(
+            range_jobs=1.0,
+            range_bytes=float(len(data)),
+            range_open_s=open_s,
+            range_seek_s=seek_s,
+            range_read_s=read_s,
+        )
+        return data
+
+    def _record_timing(self, **kwargs: float) -> None:
+        with self._timing_lock:
+            for key, value in kwargs.items():
+                self._timing_totals[key] += value
+
+    def timing_summary(self) -> dict[str, float]:
+        with self._timing_lock:
+            return dict(self._timing_totals)
+
+    def close(self) -> None:
+        handles = getattr(self._local, "handles", None)
+        if handles is None:
+            return
+        for handle in handles.values():
+            with contextlib.suppress(Exception):
+                handle.close()
+        handles.clear()
+
+
+class NativeHTTPRangeFetcher:
+    """Direct pooled HTTP range reader for hf:// paths."""
+
+    _GLOBAL_SOURCE_URLS: dict[tuple[str, str], str] = {}
+    _GLOBAL_RESOLVED_URLS: dict[tuple[str, str], str] = {}
+    _GLOBAL_SIZES: dict[tuple[str, str], int] = {}
+    _GLOBAL_LOCK = threading.Lock()
+
+    _RETRYABLE_EXCEPTIONS = (
+        httpx.ConnectError,
+        httpx.ConnectTimeout,
+        httpx.ReadError,
+        httpx.ReadTimeout,
+        httpx.RemoteProtocolError,
+        httpx.PoolTimeout,
+    )
+
+    def __init__(
+        self,
+        data_root: str | Path,
+        *,
+        max_connections: int = 32,
+        timeout: float = 60.0,
+        max_retries: int = 4,
+    ):
+        self.data_root = str(data_root).rstrip("/")
+        if not self.data_root.startswith("hf://"):
+            raise ValueError("NativeHTTPRangeFetcher only supports hf:// roots")
+        self.max_retries = max_retries
+        self.api = HfApi()
+        self.fs: HfFileSystem | None = None
+        self._bucket_id: str | None = None
+        self._bucket_prefix = ""
+        if self.data_root.startswith("hf://buckets/"):
+            bucket_root = self.data_root.removeprefix("hf://buckets/")
+            parts = bucket_root.split("/", 2)
+            if len(parts) < 2:
+                raise ValueError(f"Invalid bucket root: {self.data_root}")
+            self._bucket_id = f"{parts[0]}/{parts[1]}"
+            self._bucket_prefix = parts[2].strip("/") if len(parts) == 3 else ""
+        else:
+            self.fs = HfFileSystem()
+        self.client = httpx.Client(
+            timeout=timeout,
+            limits=httpx.Limits(max_connections=max_connections, max_keepalive_connections=max_connections),
+            follow_redirects=False,
+        )
+        self._resolved_urls: dict[str, str] = {}
+        self._source_urls: dict[str, str] = {}
+        self._sizes: dict[str, int] = {}
+        self._lock = threading.Lock()
+        self._timing_lock = threading.Lock()
+        self._timing_totals = {
+            "range_jobs": 0.0,
+            "range_bytes": 0.0,
+            "range_resolve_s": 0.0,
+            "range_header_s": 0.0,
+            "range_first_byte_s": 0.0,
+            "range_body_s": 0.0,
+            "range_retry_attempts": 0.0,
+            "range_retry_sleep_s": 0.0,
+            "range_failed_requests": 0.0,
+        }
+
+    def _request(self, method: str, url: str, **kwargs) -> httpx.Response:
+        last_exc: Exception | None = None
+        for attempt in range(self.max_retries + 1):
+            try:
+                return self.client.request(method, url, **kwargs)
+            except self._RETRYABLE_EXCEPTIONS as exc:
+                last_exc = exc
+                if attempt >= self.max_retries:
+                    break
+                time.sleep(min(0.5 * 2**attempt, 5.0))
+        if last_exc is None:
+            raise RuntimeError("HTTP request failed without an exception")
+        raise last_exc
+
+    def _cache_key(self, relative_path: str) -> tuple[str, str]:
+        return self.data_root, relative_path
+
+    def _path(self, relative_path: str) -> str:
+        return f"{self.data_root}/{relative_path}"
+
+    def _bucket_path(self, relative_path: str) -> str:
+        if self._bucket_prefix:
+            return f"{self._bucket_prefix}/{relative_path}"
+        return relative_path
+
+    def _headers_for(self, request_url: str, source_url: str) -> dict[str, str]:
+        headers = self.api._build_hf_headers()
+        if urlparse(request_url).netloc != urlparse(source_url).netloc:
+            headers.pop("authorization", None)
+            headers.pop("Authorization", None)
+        return headers
+
+    def _source_url(self, relative_path: str) -> str:
+        with self._lock:
+            source = self._source_urls.get(relative_path)
+            if source is not None:
+                return source
+        key = self._cache_key(relative_path)
+        with self._GLOBAL_LOCK:
+            source = self._GLOBAL_SOURCE_URLS.get(key)
+        if source is None:
+            if self._bucket_id is not None:
+                source = (
+                    f"{constants.ENDPOINT}/buckets/{self._bucket_id}/resolve/"
+                    f"{quote(self._bucket_path(relative_path))}"
+                )
+            else:
+                if self.fs is None:
+                    raise RuntimeError("HfFileSystem fallback was not initialized")
+                source = self.fs.url(self._path(relative_path))
+            with self._GLOBAL_LOCK:
+                self._GLOBAL_SOURCE_URLS[key] = source
+        with self._lock:
+            self._source_urls[relative_path] = source
+            return source
+
+    def _resolve_url(self, relative_path: str, *, refresh: bool = False) -> str:
+        with self._lock:
+            if not refresh and relative_path in self._resolved_urls:
+                return self._resolved_urls[relative_path]
+        key = self._cache_key(relative_path)
+        if not refresh:
+            with self._GLOBAL_LOCK:
+                resolved = self._GLOBAL_RESOLVED_URLS.get(key)
+                size = self._GLOBAL_SIZES.get(key)
+            if resolved is not None:
+                with self._lock:
+                    self._resolved_urls[relative_path] = resolved
+                    if size is not None:
+                        self._sizes[relative_path] = size
+                return resolved
+
+        source = self._source_url(relative_path)
+        response = self._request("HEAD", source, headers=self.api._build_hf_headers(), follow_redirects=False)
+        try:
+            hf_raise_for_status(response)
+            location = response.headers.get("Location")
+            resolved = urljoin(source, location) if location else source
+            with self._lock:
+                self._resolved_urls[relative_path] = resolved
+                if "Content-Length" in response.headers:
+                    self._sizes[relative_path] = int(response.headers["Content-Length"])
+            with self._GLOBAL_LOCK:
+                self._GLOBAL_RESOLVED_URLS[key] = resolved
+                if "Content-Length" in response.headers:
+                    self._GLOBAL_SIZES[key] = int(response.headers["Content-Length"])
+            return resolved
+        finally:
+            response.close()
+
+    def info_size(self, relative_path: str) -> int:
+        with self._lock:
+            size = self._sizes.get(relative_path)
+            if size is not None:
+                return size
+        key = self._cache_key(relative_path)
+        with self._GLOBAL_LOCK:
+            size = self._GLOBAL_SIZES.get(key)
+        if size is not None:
+            with self._lock:
+                self._sizes[relative_path] = size
+            return size
+
+        resolved = self._resolve_url(relative_path)
+        source = self._source_url(relative_path)
+        response = self._request(
+            "HEAD", resolved, headers=self._headers_for(resolved, source), follow_redirects=True
+        )
+        try:
+            hf_raise_for_status(response)
+            size = int(response.headers["Content-Length"])
+            with self._lock:
+                self._sizes[relative_path] = size
+            with self._GLOBAL_LOCK:
+                self._GLOBAL_SIZES[key] = size
+            return size
+        finally:
+            response.close()
+
+    def read_range(self, relative_path: str, offset: int, length: int) -> bytes:
+        resolve_start = time.perf_counter()
+        resolved = self._resolve_url(relative_path)
+        source = self._source_url(relative_path)
+        resolve_s = time.perf_counter() - resolve_start
+        headers = self._headers_for(resolved, source)
+        headers["Range"] = f"bytes={offset}-{offset + length - 1}"
+        payload, status_code, timings = self._read_range_response(resolved, headers)
+        if status_code == 403:
+            refresh_start = time.perf_counter()
+            resolved = self._resolve_url(relative_path, refresh=True)
+            resolve_s += time.perf_counter() - refresh_start
+            headers = self._headers_for(resolved, source)
+            headers["Range"] = f"bytes={offset}-{offset + length - 1}"
+            payload, status_code, retry_timings = self._read_range_response(resolved, headers)
+            for key, value in retry_timings.items():
+                timings[key] += value
+        if status_code == 403:
+            raise PermissionError(f"HTTP range request returned 403 after URL refresh: {relative_path}")
+        self._record_timing(
+            range_jobs=1.0,
+            range_bytes=float(len(payload)),
+            range_resolve_s=resolve_s,
+            **timings,
+        )
+        return payload
+
+    def _read_range_response(self, url: str, headers: dict[str, str]) -> tuple[bytes, int, dict[str, float]]:
+        last_exc: Exception | None = None
+        retry_attempts = 0.0
+        retry_sleep_s = 0.0
+        for attempt in range(self.max_retries + 1):
+            try:
+                payload, status_code, timings = self._read_range_response_once(url, headers)
+                timings["range_retry_attempts"] = retry_attempts
+                timings["range_retry_sleep_s"] = retry_sleep_s
+                return payload, status_code, timings
+            except self._RETRYABLE_EXCEPTIONS as exc:
+                last_exc = exc
+                if attempt >= self.max_retries:
+                    break
+                retry_attempts += 1.0
+                sleep_s = min(0.5 * 2**attempt, 5.0)
+                retry_sleep_s += sleep_s
+                time.sleep(sleep_s)
+        self._record_timing(
+            range_failed_requests=1.0,
+            range_retry_attempts=retry_attempts,
+            range_retry_sleep_s=retry_sleep_s,
+        )
+        if last_exc is None:
+            raise RuntimeError("HTTP range request failed without an exception")
+        raise last_exc
+
+    def _read_range_response_once(
+        self, url: str, headers: dict[str, str]
+    ) -> tuple[bytes, int, dict[str, float]]:
+        header_start = time.perf_counter()
+        with self.client.stream("GET", url, headers=headers) as response:
+            header_s = time.perf_counter() - header_start
+            if response.status_code == 403:
+                return (
+                    b"",
+                    response.status_code,
+                    {
+                        "range_header_s": header_s,
+                        "range_first_byte_s": 0.0,
+                        "range_body_s": 0.0,
+                    },
+                )
+            hf_raise_for_status(response)
+            chunks = []
+            first_byte_s = 0.0
+            first_chunk = True
+            body_start = time.perf_counter()
+            for chunk in response.iter_bytes():
+                if first_chunk:
+                    first_byte_s = time.perf_counter() - body_start
+                    first_chunk = False
+                chunks.append(chunk)
+            body_s = time.perf_counter() - body_start
+            return (
+                b"".join(chunks),
+                response.status_code,
+                {
+                    "range_header_s": header_s,
+                    "range_first_byte_s": first_byte_s,
+                    "range_body_s": body_s,
+                },
+            )
+
+    def _record_timing(self, **kwargs: float) -> None:
+        with self._timing_lock:
+            for key, value in kwargs.items():
+                self._timing_totals[key] += value
+
+    def timing_summary(self) -> dict[str, float]:
+        with self._timing_lock:
+            return dict(self._timing_totals)
+
+    def close(self) -> None:
+        self.client.close()
+
+
+def make_range_fetcher(
+    data_root: str | Path,
+    *,
+    range_backend: str,
+    workers: int,
+    native_http_connections: int | None = None,
+    native_http_timeout: float = 60.0,
+    native_http_retries: int = 4,
+):
+    if range_backend == "fsspec":
+        return ThreadLocalRangeFetcher(data_root)
+    if range_backend == "native-http":
+        max_connections = native_http_connections or max(8, workers)
+        return NativeHTTPRangeFetcher(
+            data_root,
+            max_connections=max_connections,
+            timeout=native_http_timeout,
+            max_retries=native_http_retries,
+        )
+    raise ValueError(f"Unknown range backend: {range_backend}")
+
+
+class EpisodeVideoManifest:
+    _FILE_SIDECAR_CACHE: dict[str, dict[str, VideoFileRecord]] = {}
+    _FILE_SIDECAR_CACHE_LOCK = threading.Lock()
+
+    def __init__(
+        self,
+        *,
+        video_keys: list[str],
+        files: list[VideoFileRecord],
+        spans: dict[str, np.ndarray],
+    ):
+        self.video_keys = list(video_keys)
+        self._camera_to_id = {key: idx for idx, key in enumerate(self.video_keys)}
+        self.files = files
+        self.spans = spans
+
+    @classmethod
+    def build(
+        cls,
+        meta: LeRobotDatasetMetadata,
+        data_root: str | Path,
+        *,
+        episode_indices: list[int] | range | None = None,
+        range_backend: str = "fsspec",
+        workers: int = 8,
+        header_probe_bytes: int = 4 * 1024 * 1024,
+        max_probe_bytes: int = 64 * 1024 * 1024,
+        keyframe_pad_s: float = 0.1,
+        keyframe_pad_fraction: float = 0.05,
+        sidecar_path: str | Path | None = None,
+    ) -> EpisodeVideoManifest:
+        meta.ensure_readable()
+        video_keys = list(meta.video_keys)
+        if episode_indices is None:
+            episode_indices = range(int(meta.total_episodes))
+        rel_paths = sorted(
+            {str(meta.get_video_file_path(ep_idx, key)) for ep_idx in episode_indices for key in video_keys}
+        )
+        path_to_id = {path: idx for idx, path in enumerate(rel_paths)}
+        if sidecar_path is None:
+            files = cls._build_file_records(
+                rel_paths,
+                data_root,
+                range_backend=range_backend,
+                workers=workers,
+                header_probe_bytes=header_probe_bytes,
+                max_probe_bytes=max_probe_bytes,
+            )
+        else:
+            records = cls.load_file_sidecar(sidecar_path)
+            missing = [path for path in rel_paths if path not in records]
+            if missing:
+                raise ValueError(
+                    f"Sidecar {sidecar_path} is missing {len(missing)} files, first: {missing[0]}"
+                )
+            files = [records[path] for path in rel_paths]
+
+        total = int(meta.total_episodes)
+        num_cameras = len(video_keys)
+        spans: dict[str, np.ndarray] = {
+            "file_id": np.zeros((total, num_cameras), dtype=np.int32),
+            "mdat_offset": np.zeros((total, num_cameras), dtype=np.int64),
+            "mdat_length": np.zeros((total, num_cameras), dtype=np.int64),
+            "first_pts": np.zeros((total, num_cameras), dtype=np.float64),
+            "last_pts": np.zeros((total, num_cameras), dtype=np.float64),
+            "frame_count": np.zeros((total, num_cameras), dtype=np.int32),
+            "sample_lo": np.zeros((total, num_cameras), dtype=np.int32),
+            "sample_hi": np.zeros((total, num_cameras), dtype=np.int32),
+            "source_start_pts": np.zeros((total, num_cameras), dtype=np.float64),
+        }
+
+        for ep_idx in episode_indices:
+            ep = meta.episodes[ep_idx]
+            for cam_idx, key in enumerate(video_keys):
+                rel_path = str(meta.get_video_file_path(ep_idx, key))
+                file_id = path_to_id[rel_path]
+                mp4 = files[file_id].mp4
+                from_ts = float(ep[f"videos/{key}/from_timestamp"])
+                to_ts = float(ep[f"videos/{key}/to_timestamp"])
+                sample_slice = mp4.sample_slice(
+                    from_ts,
+                    to_ts,
+                    keyframe_pad_s=keyframe_pad_s,
+                    keyframe_pad_fraction=keyframe_pad_fraction,
+                    file_size=files[file_id].file_size,
+                )
+                spans["file_id"][ep_idx, cam_idx] = file_id
+                spans["mdat_offset"][ep_idx, cam_idx] = sample_slice.byte_offset
+                spans["mdat_length"][ep_idx, cam_idx] = sample_slice.byte_length
+                spans["first_pts"][ep_idx, cam_idx] = from_ts
+                spans["last_pts"][ep_idx, cam_idx] = to_ts
+                spans["frame_count"][ep_idx, cam_idx] = sample_slice.sample_hi - sample_slice.sample_lo + 1
+                spans["sample_lo"][ep_idx, cam_idx] = sample_slice.sample_lo
+                spans["sample_hi"][ep_idx, cam_idx] = sample_slice.sample_hi
+                spans["source_start_pts"][ep_idx, cam_idx] = sample_slice.source_start_pts
+
+        return cls(video_keys=video_keys, files=files, spans=spans)
+
+    @staticmethod
+    def _build_file_records(
+        rel_paths: list[str],
+        data_root: str | Path,
+        *,
+        range_backend: str,
+        workers: int,
+        header_probe_bytes: int,
+        max_probe_bytes: int,
+    ) -> list[VideoFileRecord]:
+        fetcher = make_range_fetcher(data_root, range_backend=range_backend, workers=workers)
+
+        def build_file(path: str) -> VideoFileRecord:
+            file_size = fetcher.info_size(path)
+            mp4 = fetch_mp4_index(
+                path,
+                fetcher.read_range,
+                file_size=file_size,
+                header_probe_bytes=header_probe_bytes,
+                max_probe_bytes=max_probe_bytes,
+            )
+            return VideoFileRecord(path, file_size, mp4)
+
+        try:
+            with ThreadPoolExecutor(max_workers=workers) as pool:
+                return list(pool.map(build_file, rel_paths))
+        finally:
+            fetcher.close()
+
+    @classmethod
+    def write_file_sidecar(
+        cls,
+        sidecar_path: str | Path,
+        rel_paths: list[str],
+        data_root: str | Path,
+        *,
+        range_backend: str = "native-http",
+        workers: int = 8,
+        header_probe_bytes: int = 4 * 1024 * 1024,
+        max_probe_bytes: int = 64 * 1024 * 1024,
+    ) -> None:
+        records = cls._build_file_records(
+            sorted(set(rel_paths)),
+            data_root,
+            range_backend=range_backend,
+            workers=workers,
+            header_probe_bytes=header_probe_bytes,
+            max_probe_bytes=max_probe_bytes,
+        )
+        cls.save_file_sidecar(sidecar_path, records)
+
+    @staticmethod
+    def save_file_sidecar(sidecar_path: str | Path, records: list[VideoFileRecord]) -> None:
+        sidecar_path = Path(sidecar_path)
+        sidecar_path.parent.mkdir(parents=True, exist_ok=True)
+        payload = {
+            "version": 1,
+            "files": [
+                {"file_path": record.file_path, "file_size": record.file_size, "mp4": record.mp4.to_dict()}
+                for record in records
+            ],
+        }
+        arrays = {}
+        for file_idx, record in enumerate(records):
+            arrays[f"{file_idx}/sample_pts"] = record.mp4.sample_pts
+            arrays[f"{file_idx}/sample_durations"] = record.mp4.sample_durations
+            arrays[f"{file_idx}/sample_sizes"] = record.mp4.sample_sizes
+            arrays[f"{file_idx}/sample_offsets"] = record.mp4.sample_offsets
+            arrays[f"{file_idx}/sync_samples"] = record.mp4.sync_samples
+        np.savez_compressed(sidecar_path, manifest_json=json.dumps(payload).encode("utf-8"), **arrays)
+
+    @staticmethod
+    def load_file_sidecar(sidecar_path: str | Path) -> dict[str, VideoFileRecord]:
+        cache_key = str(Path(sidecar_path).expanduser())
+        with EpisodeVideoManifest._FILE_SIDECAR_CACHE_LOCK:
+            cached = EpisodeVideoManifest._FILE_SIDECAR_CACHE.get(cache_key)
+        if cached is not None:
+            return cached
+
+        with np.load(sidecar_path, allow_pickle=False) as data:
+            payload = json.loads(bytes(data["manifest_json"]).decode("utf-8"))
+            records = {}
+            for file_idx, item in enumerate(payload["files"]):
+                arrays = {
+                    name: data[f"{file_idx}/{name}"]
+                    for name in [
+                        "sample_pts",
+                        "sample_durations",
+                        "sample_sizes",
+                        "sample_offsets",
+                        "sync_samples",
+                    ]
+                }
+                mp4 = Mp4Index.from_dict(item["mp4"], arrays)
+                records[item["file_path"]] = VideoFileRecord(item["file_path"], int(item["file_size"]), mp4)
+        with EpisodeVideoManifest._FILE_SIDECAR_CACHE_LOCK:
+            EpisodeVideoManifest._FILE_SIDECAR_CACHE[cache_key] = records
+        return records
+
+    def camera_id(self, camera_key: str) -> int:
+        return self._camera_to_id[camera_key]
+
+    def lookup(self, episode_index: int, camera_key: str) -> EpisodeVideoSpan:
+        cam = self.camera_id(camera_key)
+        return EpisodeVideoSpan(
+            file_id=int(self.spans["file_id"][episode_index, cam]),
+            mdat_offset=int(self.spans["mdat_offset"][episode_index, cam]),
+            mdat_length=int(self.spans["mdat_length"][episode_index, cam]),
+            first_pts=float(self.spans["first_pts"][episode_index, cam]),
+            last_pts=float(self.spans["last_pts"][episode_index, cam]),
+            frame_count=int(self.spans["frame_count"][episode_index, cam]),
+            sample_lo=int(self.spans["sample_lo"][episode_index, cam]),
+            sample_hi=int(self.spans["sample_hi"][episode_index, cam]),
+            source_start_pts=float(self.spans["source_start_pts"][episode_index, cam]),
+        )
+
+    def file_lookup(self, file_id: int) -> VideoFileRecord:
+        return self.files[file_id]
+
+    def mp4_index(self, episode_index: int, camera_key: str) -> Mp4Index:
+        return self.files[self.lookup(episode_index, camera_key).file_id].mp4
+
+    def sample_slice(self, episode_index: int, camera_key: str) -> Mp4SampleSlice:
+        span = self.lookup(episode_index, camera_key)
+        return Mp4SampleSlice(
+            sample_lo=span.sample_lo,
+            sample_hi=span.sample_hi,
+            byte_offset=span.mdat_offset,
+            byte_length=span.mdat_length,
+            source_start_pts=span.source_start_pts,
+        )
+
+
+class EpisodeByteCache:
+    def __init__(
+        self,
+        manifest: EpisodeVideoManifest,
+        data_root: str | Path,
+        *,
+        byte_budget: int = 80 * 1024**3,
+        workers: int = 8,
+        range_backend: str = "fsspec",
+        native_http_connections: int | None = None,
+        native_http_timeout: float = 60.0,
+        native_http_retries: int = 4,
+        open_decoders: bool = True,
+    ):
+        self.manifest = manifest
+        self.fetcher = make_range_fetcher(
+            data_root,
+            range_backend=range_backend,
+            workers=workers,
+            native_http_connections=native_http_connections,
+            native_http_timeout=native_http_timeout,
+            native_http_retries=native_http_retries,
+        )
+        self.byte_budget = byte_budget
+        self.open_decoders = open_decoders
+        self._pool = ThreadPoolExecutor(max_workers=workers)
+        self._cache: OrderedDict[tuple[int, str], dict[str, Any]] = OrderedDict()
+        self._futures: dict[tuple[int, str], Future[dict[str, Any]]] = {}
+        self._bytes = 0
+        self._lock = threading.Lock()
+        self._timing_totals = {
+            "lookup_s": 0.0,
+            "fetch_s": 0.0,
+            "synthesize_s": 0.0,
+            "store_s": 0.0,
+            "jobs": 0.0,
+        }
+
+    def close(self) -> None:
+        self._pool.shutdown(wait=True)
+        with self._lock:
+            self._cache.clear()
+            self._futures.clear()
+            self._bytes = 0
+        self.fetcher.close()
+
+    def __enter__(self) -> EpisodeByteCache:
+        return self
+
+    def __exit__(self, *_exc) -> None:
+        self.close()
+
+    def submit_prefetch(self, episode_index: int) -> None:
+        for camera_key in self.manifest.video_keys:
+            self._submit(episode_index, camera_key)
+
+    def ensure_ready(self, episode_index: int) -> None:
+        for camera_key in self.manifest.video_keys:
+            self.get_bytes(episode_index, camera_key)
+
+    def get_bytes(self, episode_index: int, camera_key: str) -> bytes:
+        return self._get_entry(episode_index, camera_key)["bytes"]
+
+    def get_decoder(self, episode_index: int, camera_key: str):
+        entry = self._get_entry(episode_index, camera_key)
+        decoder = entry.get("decoder")
+        if decoder is None:
+            decoder = open_video_decoder(io.BytesIO(entry["bytes"]))
+            entry["decoder"] = decoder
+        return decoder
+
+    def get_frames(self, episode_index: int, camera_key: str, timestamps: list[float]):
+        span = self.manifest.lookup(episode_index, camera_key)
+        local_ts = [ts - span.source_start_pts for ts in timestamps]
+        decoder = self.get_decoder(episode_index, camera_key)
+        if hasattr(decoder, "get_frames_played_at"):
+            return decoder.get_frames_played_at(local_ts).data
+        metadata = decoder.metadata
+        fps = getattr(metadata, "average_fps", None)
+        if fps is None:
+            duration = max(getattr(metadata, "end_stream_seconds", 0.0), 1e-9)
+            fps = metadata.num_frames / duration
+        return decoder.get_frames_at(indices=[round(ts * fps) for ts in local_ts]).data
+
+    def timing_summary(self) -> dict[str, float]:
+        with self._lock:
+            summary = dict(self._timing_totals)
+        fetcher_summary = getattr(self.fetcher, "timing_summary", None)
+        if fetcher_summary is not None:
+            summary.update(fetcher_summary())
+        return summary
+
+    def _submit(self, episode_index: int, camera_key: str) -> Future[dict[str, Any]]:
+        key = (episode_index, camera_key)
+        with self._lock:
+            if key in self._cache:
+                future: Future[dict[str, Any]] = Future()
+                future.set_result(self._cache[key])
+                return future
+            future = self._futures.get(key)
+            if future is None:
+                future = self._pool.submit(self._fetch_and_synthesize, episode_index, camera_key)
+                self._futures[key] = future
+            return future
+
+    def _get_entry(self, episode_index: int, camera_key: str) -> dict[str, Any]:
+        key = (episode_index, camera_key)
+        with self._lock:
+            entry = self._cache.get(key)
+            if entry is not None:
+                self._cache.move_to_end(key)
+                return entry
+        future = self._submit(episode_index, camera_key)
+        entry = future.result()
+        store_start = time.perf_counter()
+        with self._lock:
+            self._futures.pop(key, None)
+            existing = self._cache.get(key)
+            if existing is not None:
+                self._cache.move_to_end(key)
+                return existing
+            self._cache[key] = entry
+            self._bytes += len(entry["bytes"])
+            self._evict_locked()
+            timings = entry.pop("_timings", None)
+            if timings is not None:
+                self._timing_totals["lookup_s"] += timings["lookup_s"]
+                self._timing_totals["fetch_s"] += timings["fetch_s"]
+                self._timing_totals["synthesize_s"] += timings["synthesize_s"]
+                self._timing_totals["store_s"] += time.perf_counter() - store_start
+                self._timing_totals["jobs"] += 1
+            return entry
+
+    def _evict_locked(self) -> None:
+        while self._bytes > self.byte_budget and self._cache:
+            _key, entry = self._cache.popitem(last=False)
+            self._bytes -= len(entry["bytes"])
+
+    def _fetch_and_synthesize(self, episode_index: int, camera_key: str) -> dict[str, Any]:
+        lookup_start = time.perf_counter()
+        span = self.manifest.lookup(episode_index, camera_key)
+        file_record = self.manifest.file_lookup(span.file_id)
+        sample_slice = Mp4SampleSlice(
+            sample_lo=span.sample_lo,
+            sample_hi=span.sample_hi,
+            byte_offset=span.mdat_offset,
+            byte_length=span.mdat_length,
+            source_start_pts=span.source_start_pts,
+        )
+        lookup_s = time.perf_counter() - lookup_start
+        fetch_start = time.perf_counter()
+        payload = self.fetcher.read_range(file_record.file_path, span.mdat_offset, span.mdat_length)
+        fetch_s = time.perf_counter() - fetch_start
+        if len(payload) != span.mdat_length:
+            raise OSError(
+                f"Short read for {file_record.file_path}: expected {span.mdat_length}, got {len(payload)}"
+            )
+        synthesize_start = time.perf_counter()
+        mp4_bytes = synthesize_mp4(file_record.mp4, sample_slice, payload)
+        synthesize_s = time.perf_counter() - synthesize_start
+        entry: dict[str, Any] = {
+            "bytes": mp4_bytes,
+            "decoder": None,
+            "_timings": {
+                "lookup_s": lookup_s,
+                "fetch_s": fetch_s,
+                "synthesize_s": synthesize_s,
+            },
+        }
+        if self.open_decoders:
+            entry["decoder"] = open_video_decoder(io.BytesIO(mp4_bytes))
+        return entry
+
+
+def open_video_decoder(file_like_or_bytesio, frame_mappings=None):
+    if frame_mappings is not None:
+        raise ValueError("Synthesized episode videos use a local timeline; pass frame_mappings=None.")
+    from torchcodec.decoders import VideoDecoder
+
+    return VideoDecoder(file_like_or_bytesio, seek_mode="approximate")
+
+
+def assert_hf_hub_range_cache_branch() -> None:
+    """Fail unless huggingface_hub was installed from the required range-cache branch."""
+
+    try:
+        dist = metadata.distribution("huggingface_hub")
+    except metadata.PackageNotFoundError as exc:
+        raise AssertionError("huggingface_hub is not installed") from exc
+
+    candidates = []
+    direct_url = dist.read_text("direct_url.json")
+    if direct_url:
+        candidates.append(direct_url)
+        with contextlib.suppress(json.JSONDecodeError):
+            parsed = json.loads(direct_url)
+            candidates.append(str(parsed.get("url", "")))
+            candidates.append(str(parsed.get("vcs_info", {}).get("requested_revision", "")))
+            candidates.append(str(parsed.get("vcs_info", {}).get("commit_id", "")))
+
+    text = "\n".join(candidates)
+    if "feat/hffs-cache-cdn-range-reads" not in text:
+        raise AssertionError(
+            "huggingface_hub must be installed from "
+            "git+https://github.com/huggingface/huggingface_hub.git@feat/hffs-cache-cdn-range-reads"
+        )
+
+
+@dataclass
+class StageTimer:
+    fetch_ms: float = 0.0
+    decode_ms: float = 0.0
+    bytes_read: int = 0
+    misses: int = 0
+
+    def record_fetch(self, start: float, byte_count: int) -> None:
+        self.fetch_ms += (time.perf_counter() - start) * 1000
+        self.bytes_read += byte_count
+        self.misses += 1

src/lerobot/datasets/factory.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-import math
 from pprint import pformat
 
 import torch
@@ -131,81 +130,3 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 
     return dataset
-
-
-def make_train_eval_datasets(
-    cfg: TrainPipelineConfig,
-) -> tuple[LeRobotDataset | MultiLeRobotDataset, LeRobotDataset | None]:
-    """Create train and optional eval datasets by splitting episodes based on eval_split.
-
-    The last ceil(n_episodes * eval_split) episodes per task are held out for evaluation.
-    If eval_split == 0.0, returns (full_dataset, None).
-    """
-    full_dataset = make_dataset(cfg)
-
-    if cfg.dataset.eval_split == 0.0:
-        return full_dataset, None
-
-    base_episodes = (
-        full_dataset.episodes if full_dataset.episodes is not None else list(range(full_dataset.num_episodes))
-    )
-
-    episode_tasks = full_dataset.meta.episodes["tasks"]
-    task_to_episodes: dict[str, list[int]] = {}
-    for ep_idx in base_episodes:
-        task_key = episode_tasks[ep_idx][0] if episode_tasks[ep_idx] else ""
-        task_to_episodes.setdefault(task_key, []).append(ep_idx)
-
-    train_episodes, eval_episodes = [], []
-    for eps in task_to_episodes.values():
-        n_eval = math.ceil(len(eps) * cfg.dataset.eval_split)
-        train_episodes.extend(eps[: len(eps) - n_eval])
-        eval_episodes.extend(eps[len(eps) - n_eval :])
-
-    if not train_episodes:
-        raise ValueError(
-            f"eval_split={cfg.dataset.eval_split} leaves 0 training episodes from {len(base_episodes)} total."
-        )
-
-    logging.info(
-        f"Train/eval split: {len(train_episodes)} train, {len(eval_episodes)} eval "
-        f"(eval_split={cfg.dataset.eval_split}, {len(task_to_episodes)} tasks)"
-    )
-
-    delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, full_dataset.meta)
-
-    train_image_transforms = (
-        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
-    )
-
-    train_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=train_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=train_image_transforms,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    eval_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=eval_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=None,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    if cfg.dataset.use_imagenet_stats:
-        for ds in (train_dataset, eval_dataset):
-            for key in ds.meta.camera_keys:
-                for stats_type, stats in IMAGENET_STATS.items():
-                    ds.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
-
-    return train_dataset, eval_dataset

src/lerobot/datasets/io_utils.py

@@ -153,7 +153,7 @@ def cast_stats_to_numpy(stats: dict) -> dict[str, dict[str, np.ndarray]]:
     Returns:
         dict: The statistics dictionary with values cast to numpy arrays.
     """
-    stats = {key: np.atleast_1d(np.array(value)) for key, value in flatten_dict(stats).items()}
+    stats = {key: np.array(value) for key, value in flatten_dict(stats).items()}
     return unflatten_dict(stats)
 
 

src/lerobot/datasets/lerobot_dataset.py

@@ -474,8 +474,6 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if reader.hf_dataset is None:
             # One-shot load after finalize()
             reader.load_and_activate()
-        if reader._absolute_to_relative_idx is not None and idx in reader._absolute_to_relative_idx:
-            idx = reader._absolute_to_relative_idx[idx]
         return reader.get_item(idx)
 
     def select_columns(self, column_names: str | list[str]):

src/lerobot/datasets/mp4.py

@@ -0,0 +1,666 @@
+# 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
+
+from __future__ import annotations
+
+import struct
+from collections.abc import Callable, Iterable
+from dataclasses import dataclass
+
+import numpy as np
+
+
+@dataclass(frozen=True)
+class Box:
+    type: bytes
+    start: int
+    header_size: int
+    end: int
+
+    @property
+    def payload_start(self) -> int:
+        return self.start + self.header_size
+
+    @property
+    def size(self) -> int:
+        return self.end - self.start
+
+
+@dataclass(frozen=True)
+class Mp4SampleSlice:
+    sample_lo: int
+    sample_hi: int
+    byte_offset: int
+    byte_length: int
+    source_start_pts: float
+
+
+@dataclass(frozen=True)
+class Mp4Index:
+    file_path: str
+    file_size: int
+    ftyp: bytes
+    moov_offset: int
+    mdat_offset: int
+    mdat_payload_offset: int
+    mdat_payload_size: int
+    faststart: bool
+    codec: str
+    timescale: int
+    duration: int
+    track_id: int
+    width: int
+    height: int
+    stsd_body: bytes
+    sample_pts: np.ndarray
+    sample_durations: np.ndarray
+    sample_sizes: np.ndarray
+    sample_offsets: np.ndarray
+    sync_samples: np.ndarray
+
+    def sample_slice(
+        self,
+        from_ts: float,
+        to_ts: float,
+        *,
+        keyframe_pad_s: float = 0.1,
+        keyframe_pad_fraction: float = 0.05,
+        file_size: int | None = None,
+    ) -> Mp4SampleSlice:
+        if to_ts < from_ts:
+            raise ValueError(f"Invalid timestamp span: {from_ts=} {to_ts=}")
+        if len(self.sample_pts) == 0:
+            raise ValueError(f"{self.file_path} contains no indexed samples")
+
+        pad = max(keyframe_pad_s, (to_ts - from_ts) * keyframe_pad_fraction)
+        lo_ts = max(0.0, from_ts - pad)
+        hi_ts = to_ts + pad
+        lo = int(np.searchsorted(self.sample_pts, lo_ts, side="left"))
+        hi = int(np.searchsorted(self.sample_pts, hi_ts, side="right")) - 1
+        lo = min(max(lo, 0), len(self.sample_pts) - 1)
+        hi = min(max(hi, lo), len(self.sample_pts) - 1)
+
+        if len(self.sync_samples):
+            prev_sync = self.sync_samples[self.sync_samples <= lo]
+            if len(prev_sync):
+                lo = int(prev_sync[-1])
+            else:
+                lo = int(self.sync_samples[0])
+                if lo > hi:
+                    hi = lo
+
+        offsets = self.sample_offsets[lo : hi + 1]
+        sizes = self.sample_sizes[lo : hi + 1]
+        slice_lo = int(offsets.min())
+        slice_hi = int((offsets + sizes).max())
+        if file_size is not None:
+            slice_hi = min(slice_hi, int(file_size))
+        return Mp4SampleSlice(
+            sample_lo=lo,
+            sample_hi=hi,
+            byte_offset=slice_lo,
+            byte_length=slice_hi - slice_lo,
+            source_start_pts=float(self.sample_pts[lo]),
+        )
+
+    def to_dict(self) -> dict:
+        return {
+            "file_path": self.file_path,
+            "file_size": self.file_size,
+            "ftyp": self.ftyp.hex(),
+            "moov_offset": self.moov_offset,
+            "mdat_offset": self.mdat_offset,
+            "mdat_payload_offset": self.mdat_payload_offset,
+            "mdat_payload_size": self.mdat_payload_size,
+            "faststart": self.faststart,
+            "codec": self.codec,
+            "timescale": self.timescale,
+            "duration": self.duration,
+            "track_id": self.track_id,
+            "width": self.width,
+            "height": self.height,
+            "stsd_body": self.stsd_body.hex(),
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict, arrays: dict[str, np.ndarray]) -> Mp4Index:
+        return cls(
+            file_path=data["file_path"],
+            file_size=int(data["file_size"]),
+            ftyp=bytes.fromhex(data["ftyp"]),
+            moov_offset=int(data["moov_offset"]),
+            mdat_offset=int(data["mdat_offset"]),
+            mdat_payload_offset=int(data["mdat_payload_offset"]),
+            mdat_payload_size=int(data["mdat_payload_size"]),
+            faststart=bool(data["faststart"]),
+            codec=data["codec"],
+            timescale=int(data["timescale"]),
+            duration=int(data["duration"]),
+            track_id=int(data["track_id"]),
+            width=int(data["width"]),
+            height=int(data["height"]),
+            stsd_body=bytes.fromhex(data["stsd_body"]),
+            sample_pts=arrays["sample_pts"],
+            sample_durations=arrays["sample_durations"],
+            sample_sizes=arrays["sample_sizes"],
+            sample_offsets=arrays["sample_offsets"],
+            sync_samples=arrays["sync_samples"],
+        )
+
+
+def fetch_mp4_index(
+    path: str,
+    read_range: Callable[[str, int, int], bytes],
+    *,
+    file_size: int,
+    header_probe_bytes: int = 4 * 1024 * 1024,
+    max_probe_bytes: int = 64 * 1024 * 1024,
+) -> Mp4Index:
+    probe_size = min(header_probe_bytes, file_size)
+    while True:
+        data = read_range(path, 0, probe_size)
+        top = list(iter_boxes(data, 0, len(data), absolute_base=0, allow_truncated=True))
+        has_mdat = any(box.type == b"mdat" for box in top)
+        has_moov = any(box.type == b"moov" and box.end <= len(data) for box in top)
+        if has_mdat and has_moov:
+            return parse_mp4_index(path, data, file_size=file_size)
+        if probe_size >= min(max_probe_bytes, file_size):
+            if has_mdat and not has_moov:
+                tail_index = _fetch_tail_moov_index(path, read_range, data, top, file_size, max_probe_bytes)
+                if tail_index is not None:
+                    return tail_index
+            missing = []
+            if not has_mdat:
+                missing.append("mdat")
+            if not has_moov:
+                missing.append("moov")
+            raise ValueError(
+                f"Could not find complete {'/'.join(missing)} in first {probe_size} bytes of {path}"
+            )
+        probe_size = min(probe_size * 2, max_probe_bytes, file_size)
+
+
+def _fetch_tail_moov_index(
+    path: str,
+    read_range: Callable[[str, int, int], bytes],
+    prefix: bytes,
+    top_boxes: list[Box],
+    file_size: int,
+    max_probe_bytes: int,
+) -> Mp4Index | None:
+    mdat_box = _one(top_boxes, b"mdat")
+    if mdat_box is None or mdat_box.end >= file_size:
+        return None
+    tail_offset = mdat_box.end
+    tail_length = min(max_probe_bytes, file_size - tail_offset)
+    tail = read_range(path, tail_offset, tail_length)
+    tail_boxes = list(iter_boxes(tail, 0, len(tail), absolute_base=tail_offset, allow_truncated=True))
+    moov_box = next(
+        (box for box in tail_boxes if box.type == b"moov" and box.end <= tail_offset + len(tail)), None
+    )
+    if moov_box is None:
+        return None
+    ftyp_box = _one(top_boxes, b"ftyp", required=False)
+    ftyp = (
+        prefix[ftyp_box.start : ftyp_box.end]
+        if ftyp_box is not None
+        else _box(b"ftyp", b"isom\0\0\2\0isomiso2mp41")
+    )
+    moov_start = moov_box.payload_start - tail_offset
+    moov_end = moov_box.end - tail_offset
+    return _parse_mp4_index_from_layout(
+        path,
+        file_size=file_size,
+        ftyp=ftyp,
+        moov_offset=moov_box.start,
+        moov=tail[moov_start:moov_end],
+        mdat_box=mdat_box,
+    )
+
+
+def parse_mp4_index(path: str, data: bytes, *, file_size: int | None = None) -> Mp4Index:
+    if file_size is None:
+        file_size = len(data)
+    top = list(iter_boxes(data, 0, len(data), absolute_base=0, allow_truncated=True))
+    ftyp_box = _one(top, b"ftyp", required=False)
+    moov_box = _one(top, b"moov")
+    mdat_box = _one(top, b"mdat")
+    if moov_box.end > len(data):
+        raise ValueError(f"{path}: moov box is truncated")
+
+    moov = data[moov_box.payload_start : moov_box.end]
+    ftyp = (
+        data[ftyp_box.start : ftyp_box.end]
+        if ftyp_box is not None
+        else _box(b"ftyp", b"isom\0\0\2\0isomiso2mp41")
+    )
+    return _parse_mp4_index_from_layout(
+        path,
+        file_size=file_size,
+        ftyp=ftyp,
+        moov_offset=moov_box.start,
+        moov=moov,
+        mdat_box=mdat_box,
+    )
+
+
+def _parse_mp4_index_from_layout(
+    path: str,
+    *,
+    file_size: int,
+    ftyp: bytes,
+    moov_offset: int,
+    moov: bytes,
+    mdat_box: Box,
+) -> Mp4Index:
+    mvhd_timescale, mvhd_duration = _parse_mvhd(_find_descendant(moov, [b"mvhd"]))
+    trak_box, trak_payload = _find_video_trak(moov)
+    _ = trak_box
+    tkhd = _parse_tkhd(_find_descendant(trak_payload, [b"tkhd"]))
+    mdhd_timescale, mdhd_duration = _parse_mdhd(_find_descendant(trak_payload, [b"mdia", b"mdhd"]))
+    stbl = _find_descendant(trak_payload, [b"mdia", b"minf", b"stbl"])
+
+    stsd = _find_child(stbl, b"stsd")
+    stsd_body = stbl[stsd.payload_start : stsd.end]
+    codec = _parse_stsd_codec(stsd_body)
+    stts = _parse_stts(_payload(stbl, b"stts"))
+    sample_sizes = _parse_stsz(_payload(stbl, b"stsz"))
+    stsc = _parse_stsc(_payload(stbl, b"stsc"))
+    chunk_offsets = _parse_chunk_offsets(stbl)
+    sync_samples = _parse_stss(stbl, len(sample_sizes))
+
+    sample_durations = _expand_stts(stts, len(sample_sizes))
+    sample_pts_units = np.empty(len(sample_durations), dtype=np.int64)
+    if len(sample_durations):
+        sample_pts_units[0] = 0
+        if len(sample_durations) > 1:
+            sample_pts_units[1:] = np.cumsum(sample_durations[:-1], dtype=np.int64)
+    sample_pts = sample_pts_units.astype(np.float64) / float(mdhd_timescale)
+    sample_offsets = _sample_offsets(stsc, chunk_offsets, sample_sizes)
+
+    return Mp4Index(
+        file_path=path,
+        file_size=file_size,
+        ftyp=ftyp,
+        moov_offset=moov_offset,
+        mdat_offset=mdat_box.start,
+        mdat_payload_offset=mdat_box.payload_start,
+        mdat_payload_size=mdat_box.end - mdat_box.payload_start
+        if mdat_box.end <= file_size
+        else file_size - mdat_box.payload_start,
+        faststart=moov_offset < mdat_box.start,
+        codec=codec,
+        timescale=mdhd_timescale,
+        duration=mdhd_duration or mvhd_duration,
+        track_id=tkhd["track_id"],
+        width=tkhd["width"],
+        height=tkhd["height"],
+        stsd_body=stsd_body,
+        sample_pts=sample_pts,
+        sample_durations=sample_durations,
+        sample_sizes=sample_sizes,
+        sample_offsets=sample_offsets,
+        sync_samples=sync_samples,
+    )
+
+
+def synthesize_mp4(index: Mp4Index, sample_slice: Mp4SampleSlice, mdat_payload: bytes) -> bytes:
+    lo = sample_slice.sample_lo
+    hi = sample_slice.sample_hi + 1
+    if lo < 0 or hi > len(index.sample_sizes) or lo >= hi:
+        raise ValueError(f"Invalid sample range [{lo}, {hi}) for {index.file_path}")
+
+    offsets = index.sample_offsets[lo:hi]
+    sizes = index.sample_sizes[lo:hi]
+    rel_offsets = offsets - sample_slice.byte_offset
+    if int(rel_offsets.min()) != 0:
+        raise ValueError("Sample slice must start at the minimum referenced sample offset")
+    if int((rel_offsets + sizes).max()) > len(mdat_payload):
+        raise ValueError("Sample slice does not cover all referenced samples")
+
+    durations = index.sample_durations[lo:hi]
+    sync = index.sync_samples[(index.sync_samples >= lo) & (index.sync_samples < hi)] - lo + 1
+    moov = _make_moov(index, durations, sizes, rel_offsets, sync, mdat_data_offset=0)
+    header_size = len(index.ftyp) + len(moov)
+    moov = _make_moov(index, durations, sizes, rel_offsets, sync, mdat_data_offset=header_size + 8)
+    return index.ftyp + moov + _box(b"mdat", mdat_payload)
+
+
+def iter_boxes(
+    data: bytes,
+    start: int,
+    end: int,
+    *,
+    absolute_base: int = 0,
+    allow_truncated: bool = False,
+) -> Iterable[Box]:
+    pos = start
+    while pos + 8 <= end:
+        size = struct.unpack_from(">I", data, pos)[0]
+        typ = data[pos + 4 : pos + 8]
+        header_size = 8
+        if size == 1:
+            if pos + 16 > end:
+                break
+            size = struct.unpack_from(">Q", data, pos + 8)[0]
+            header_size = 16
+        elif size == 0:
+            size = end - pos
+        if size < header_size:
+            break
+        box_end = pos + size
+        if box_end > end and not allow_truncated:
+            break
+        yield Box(typ, absolute_base + pos, header_size, absolute_base + box_end)
+        pos = box_end
+
+
+def _find_video_trak(moov: bytes) -> tuple[Box, bytes]:
+    for trak in _children(moov, 0, len(moov)):
+        if trak.type != b"trak":
+            continue
+        payload = moov[trak.payload_start : trak.end]
+        hdlr = _find_descendant(payload, [b"mdia", b"hdlr"])
+        if hdlr[8:12] == b"vide":
+            return trak, payload
+    raise ValueError("No video track found")
+
+
+def _find_descendant(data: bytes, path: list[bytes]) -> bytes:
+    current = data
+    for typ in path:
+        box = _find_child(current, typ)
+        current = current[box.payload_start : box.end]
+    return current
+
+
+def _find_child(data: bytes, typ: bytes) -> Box:
+    for box in _children(data, 0, len(data)):
+        if box.type == typ:
+            return box
+    raise ValueError(f"Missing MP4 box {typ.decode('latin1')}")
+
+
+def _children(data: bytes, start: int, end: int) -> Iterable[Box]:
+    return iter_boxes(data, start, end, absolute_base=0)
+
+
+def _one(boxes: list[Box], typ: bytes, *, required: bool = True) -> Box | None:
+    matches = [box for box in boxes if box.type == typ]
+    if not matches and required:
+        raise ValueError(f"Missing MP4 box {typ.decode('latin1')}")
+    return matches[0] if matches else None
+
+
+def _payload(parent: bytes, typ: bytes) -> bytes:
+    box = _find_child(parent, typ)
+    return parent[box.payload_start : box.end]
+
+
+def _parse_mvhd(payload: bytes) -> tuple[int, int]:
+    version = payload[0]
+    if version == 1:
+        return struct.unpack_from(">IQ", payload, 20)
+    return struct.unpack_from(">II", payload, 12)
+
+
+def _parse_mdhd(payload: bytes) -> tuple[int, int]:
+    version = payload[0]
+    if version == 1:
+        return struct.unpack_from(">IQ", payload, 20)
+    return struct.unpack_from(">II", payload, 12)
+
+
+def _parse_tkhd(payload: bytes) -> dict[str, int]:
+    version = payload[0]
+    if version == 1:
+        track_id = struct.unpack_from(">I", payload, 20)[0]
+        duration = struct.unpack_from(">Q", payload, 28)[0]
+        width, height = struct.unpack_from(">II", payload, 88)
+    else:
+        track_id = struct.unpack_from(">I", payload, 12)[0]
+        duration = struct.unpack_from(">I", payload, 20)[0]
+        width, height = struct.unpack_from(">II", payload, 76)
+    return {"track_id": track_id, "duration": duration, "width": width >> 16, "height": height >> 16}
+
+
+def _parse_stsd_codec(stsd_body: bytes) -> str:
+    if len(stsd_body) < 16:
+        return "unknown"
+    return stsd_body[12:16].decode("latin1")
+
+
+def _parse_stts(payload: bytes) -> list[tuple[int, int]]:
+    count = struct.unpack_from(">I", payload, 4)[0]
+    out = []
+    offset = 8
+    for _ in range(count):
+        out.append(struct.unpack_from(">II", payload, offset))
+        offset += 8
+    return out
+
+
+def _expand_stts(entries: list[tuple[int, int]], sample_count: int) -> np.ndarray:
+    values = np.empty(sample_count, dtype=np.int64)
+    pos = 0
+    for count, delta in entries:
+        values[pos : pos + count] = delta
+        pos += count
+    if pos != sample_count:
+        raise ValueError(f"stts describes {pos} samples, stsz describes {sample_count}")
+    return values
+
+
+def _parse_stsz(payload: bytes) -> np.ndarray:
+    sample_size, sample_count = struct.unpack_from(">II", payload, 4)
+    if sample_size:
+        return np.full(sample_count, sample_size, dtype=np.int64)
+    offset = 12
+    values = np.empty(sample_count, dtype=np.int64)
+    for idx in range(sample_count):
+        values[idx] = struct.unpack_from(">I", payload, offset)[0]
+        offset += 4
+    return values
+
+
+def _parse_stsc(payload: bytes) -> list[tuple[int, int, int]]:
+    count = struct.unpack_from(">I", payload, 4)[0]
+    out = []
+    offset = 8
+    for _ in range(count):
+        out.append(struct.unpack_from(">III", payload, offset))
+        offset += 12
+    return out
+
+
+def _parse_chunk_offsets(stbl: bytes) -> np.ndarray:
+    with_stco = None
+    with_co64 = None
+    for box in _children(stbl, 0, len(stbl)):
+        if box.type == b"stco":
+            with_stco = stbl[box.payload_start : box.end]
+        elif box.type == b"co64":
+            with_co64 = stbl[box.payload_start : box.end]
+    if with_co64 is not None:
+        count = struct.unpack_from(">I", with_co64, 4)[0]
+        return np.array(
+            [struct.unpack_from(">Q", with_co64, 8 + idx * 8)[0] for idx in range(count)], dtype=np.int64
+        )
+    if with_stco is None:
+        raise ValueError("Missing stco/co64 chunk offsets")
+    count = struct.unpack_from(">I", with_stco, 4)[0]
+    return np.array(
+        [struct.unpack_from(">I", with_stco, 8 + idx * 4)[0] for idx in range(count)], dtype=np.int64
+    )
+
+
+def _parse_stss(stbl: bytes, sample_count: int) -> np.ndarray:
+    for box in _children(stbl, 0, len(stbl)):
+        if box.type == b"stss":
+            payload = stbl[box.payload_start : box.end]
+            count = struct.unpack_from(">I", payload, 4)[0]
+            return np.array(
+                [struct.unpack_from(">I", payload, 8 + idx * 4)[0] - 1 for idx in range(count)],
+                dtype=np.int64,
+            )
+    return np.arange(sample_count, dtype=np.int64)
+
+
+def _sample_offsets(
+    stsc: list[tuple[int, int, int]], chunk_offsets: np.ndarray, sample_sizes: np.ndarray
+) -> np.ndarray:
+    if not stsc:
+        raise ValueError("stsc is empty")
+    offsets = np.empty(len(sample_sizes), dtype=np.int64)
+    sample_idx = 0
+    for entry_idx, (first_chunk, samples_per_chunk, _desc_idx) in enumerate(stsc):
+        next_first = stsc[entry_idx + 1][0] if entry_idx + 1 < len(stsc) else len(chunk_offsets) + 1
+        for chunk_number in range(first_chunk, next_first):
+            if chunk_number < 1 or chunk_number > len(chunk_offsets):
+                raise ValueError("stsc references a chunk outside stco/co64")
+            chunk_pos = int(chunk_offsets[chunk_number - 1])
+            for _ in range(samples_per_chunk):
+                if sample_idx >= len(sample_sizes):
+                    return offsets
+                offsets[sample_idx] = chunk_pos
+                chunk_pos += int(sample_sizes[sample_idx])
+                sample_idx += 1
+    if sample_idx != len(sample_sizes):
+        raise ValueError(f"stsc describes {sample_idx} samples, stsz describes {len(sample_sizes)}")
+    return offsets
+
+
+def _make_moov(
+    index: Mp4Index,
+    durations: np.ndarray,
+    sizes: np.ndarray,
+    rel_offsets: np.ndarray,
+    sync_samples: np.ndarray,
+    *,
+    mdat_data_offset: int,
+) -> bytes:
+    duration = int(durations.sum())
+    stco_values = [int(mdat_data_offset + value) for value in rel_offsets]
+    if any(value > 0xFFFFFFFF for value in stco_values):
+        offset_box = _co64(stco_values)
+    else:
+        offset_box = _stco(stco_values)
+    stbl = _box(
+        b"stbl",
+        _box(b"stsd", index.stsd_body)
+        + _stts(durations)
+        + _stsc_one_sample_per_chunk(len(sizes))
+        + _stsz(sizes)
+        + offset_box
+        + (_stss(sync_samples) if len(sync_samples) else b""),
+    )
+    minf = _box(b"minf", _vmhd() + _dinf() + stbl)
+    mdia = _box(b"mdia", _mdhd(index.timescale, duration) + _hdlr() + minf)
+    trak = _box(b"trak", _tkhd(index.track_id, duration, index.width, index.height) + mdia)
+    return _box(b"moov", _mvhd(index.timescale, duration, index.track_id + 1) + trak)
+
+
+def _full_box(typ: bytes, version: int, flags: int, payload: bytes = b"") -> bytes:
+    return _box(typ, bytes([version]) + flags.to_bytes(3, "big") + payload)
+
+
+def _box(typ: bytes, payload: bytes) -> bytes:
+    size = len(payload) + 8
+    if size <= 0xFFFFFFFF:
+        return struct.pack(">I4s", size, typ) + payload
+    return struct.pack(">I4sQ", 1, typ, size + 8) + payload
+
+
+def _mvhd(timescale: int, duration: int, next_track_id: int) -> bytes:
+    matrix = struct.pack(">9I", 0x00010000, 0, 0, 0, 0x00010000, 0, 0, 0, 0x40000000)
+    payload = (
+        struct.pack(">IIII", 0, 0, timescale, duration)
+        + struct.pack(">IHH", 0x00010000, 0x0100, 0)
+        + b"\0" * 8
+        + matrix
+        + b"\0" * 24
+        + struct.pack(">I", next_track_id)
+    )
+    return _full_box(b"mvhd", 0, 0, payload)
+
+
+def _tkhd(track_id: int, duration: int, width: int, height: int) -> bytes:
+    matrix = struct.pack(">9I", 0x00010000, 0, 0, 0, 0x00010000, 0, 0, 0, 0x40000000)
+    payload = (
+        struct.pack(">IIIII", 0, 0, track_id, 0, duration)
+        + b"\0" * 8
+        + struct.pack(">hhhh", 0, 0, 0, 0)
+        + matrix
+        + struct.pack(">II", width << 16, height << 16)
+    )
+    return _full_box(b"tkhd", 0, 7, payload)
+
+
+def _mdhd(timescale: int, duration: int) -> bytes:
+    return _full_box(b"mdhd", 0, 0, struct.pack(">IIIIH", 0, 0, timescale, duration, 0x55C4) + b"\0\0")
+
+
+def _hdlr() -> bytes:
+    return _full_box(b"hdlr", 0, 0, b"\0" * 4 + b"vide" + b"\0" * 12 + b"VideoHandler\0")
+
+
+def _vmhd() -> bytes:
+    return _full_box(b"vmhd", 0, 1, struct.pack(">HHHH", 0, 0, 0, 0))
+
+
+def _dinf() -> bytes:
+    url = _full_box(b"url ", 0, 1)
+    dref = _full_box(b"dref", 0, 0, struct.pack(">I", 1) + url)
+    return _box(b"dinf", dref)
+
+
+def _stts(durations: np.ndarray) -> bytes:
+    runs = []
+    for duration in durations.tolist():
+        if runs and runs[-1][1] == int(duration):
+            runs[-1][0] += 1
+        else:
+            runs.append([1, int(duration)])
+    payload = struct.pack(">I", len(runs)) + b"".join(
+        struct.pack(">II", count, delta) for count, delta in runs
+    )
+    return _full_box(b"stts", 0, 0, payload)
+
+
+def _stsc_one_sample_per_chunk(sample_count: int) -> bytes:
+    return _full_box(b"stsc", 0, 0, struct.pack(">IIII", 1, 1, 1, 1))
+
+
+def _stsz(sizes: np.ndarray) -> bytes:
+    return _full_box(
+        b"stsz",
+        0,
+        0,
+        struct.pack(">II", 0, len(sizes)) + b"".join(struct.pack(">I", int(size)) for size in sizes.tolist()),
+    )
+
+
+def _stco(values: list[int]) -> bytes:
+    return _full_box(
+        b"stco", 0, 0, struct.pack(">I", len(values)) + b"".join(struct.pack(">I", v) for v in values)
+    )
+
+
+def _co64(values: list[int]) -> bytes:
+    return _full_box(
+        b"co64", 0, 0, struct.pack(">I", len(values)) + b"".join(struct.pack(">Q", v) for v in values)
+    )
+
+
+def _stss(values: np.ndarray) -> bytes:
+    return _full_box(
+        b"stss",
+        0,
+        0,
+        struct.pack(">I", len(values)) + b"".join(struct.pack(">I", int(value)) for value in values.tolist()),
+    )

src/lerobot/datasets/pipeline_features.py

@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
     initial_features: dict[PipelineFeatureType, dict[str, Any]],
     *,
     use_videos: bool = True,
+    exclude_images: bool = False,
     patterns: Sequence[str] | None = None,
 ) -> dict[str, dict]:
     """
     Aggregates and filters pipeline features to create a dataset-ready features dictionary.
 
     This function transforms initial features using the pipeline, categorizes them as action or observations
-    (image or state), filters them based on `use_videos` and `patterns`, and finally
+    (image or state), filters them based on `exclude_images` and `patterns`, and finally
     formats them for use with a Hugging Face LeRobot Dataset.
 
     Args:
         pipeline: The DataProcessorPipeline to apply.
         initial_features: A dictionary of raw feature specs for actions and observations.
-        use_videos: If False, image features are excluded.
+        use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
+        exclude_images: If True, image features are dropped entirely from the output.
         patterns: A sequence of regex patterns to filter action and state features.
                   Image features are not affected by this filter.
 
@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
             )
 
             # 2. Apply filtering rules.
-            if is_image and not use_videos:
+            if is_image and exclude_images:
                 continue
             if not is_image and not should_keep(key, compiled_patterns):
                 continue

src/lerobot/envs/utils.py

@@ -126,26 +126,6 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
     if "camera_obs" in observations:
         return_observations[f"{OBS_STR}.camera_obs"] = observations["camera_obs"]
 
-    # Pass through any remaining ndarray/tensor keys not already handled above,
-    # so env plugins can expose extra observation keys via get_env_processors().
-    _handled = {"pixels", "environment_state", "agent_pos", "robot_state", "policy", "camera_obs"}
-    for key, value in observations.items():
-        if key in _handled:
-            continue
-        target = f"{OBS_STR}.{key}"
-        if target in return_observations:
-            continue
-        if isinstance(value, np.ndarray):
-            val = torch.from_numpy(value).float()
-            if val.dim() == 1:
-                val = val.unsqueeze(0)
-            return_observations[target] = val
-        elif isinstance(value, Tensor):
-            val = value.float()
-            if val.dim() == 1:
-                val = val.unsqueeze(0)
-            return_observations[target] = val
-
     return return_observations
 
 

src/lerobot/policies/act/modeling_act.py

@@ -148,7 +148,7 @@ class ACTPolicy(PreTrainedPolicy):
         l1_loss = (abs_err * valid_mask).sum() / num_valid.clamp_min(1)
 
         loss_dict = {"l1_loss": l1_loss.item()}
-        if self.config.use_vae and log_sigma_x2_hat is not None:
+        if self.config.use_vae:
             # Calculate Dₖₗ(latent_pdf || standard_normal). Note: After computing the KL-divergence for
             # each dimension independently, we sum over the latent dimension to get the total
             # KL-divergence per batch element, then take the mean over the batch.

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -101,23 +101,11 @@ class DiffusionPolicy(PreTrainedPolicy):
 
     @torch.no_grad()
     def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
-        """Predict a chunk of actions given environment observations.
-
-        Supports two modes:
-        - Online (queues populated via select_action): stacks observations from internal queues.
-        - Offline (empty queues, e.g. dataloader batch): uses the batch directly.
-        """
-        queues_populated = any(len(q) > 0 for q in self._queues.values())
-        if queues_populated:
-            batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
-        else:
-            batch = dict(batch)
-            if self.config.image_features:
-                for key in self.config.image_features:
-                    if batch[key].ndim == 4:
-                        batch[key] = batch[key].unsqueeze(1)
-                batch[OBS_IMAGES] = torch.stack([batch[key] for key in self.config.image_features], dim=-4)
+        """Predict a chunk of actions given environment observations."""
+        # stack n latest observations from the queue
+        batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
         actions = self.diffusion.generate_actions(batch, noise=noise)
+
         return actions
 
     @torch.no_grad()

src/lerobot/policies/factory.py

@@ -252,7 +252,6 @@ class ProcessorConfigKwargs(TypedDict, total=False):
 def make_pre_post_processors(
     policy_cfg: PreTrainedConfig,
     pretrained_path: str | None = None,
-    pretrained_revision: str | None = None,
     **kwargs: Unpack[ProcessorConfigKwargs],
 ) -> tuple[
     PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
@@ -310,7 +309,6 @@ def make_pre_post_processors(
             overrides=kwargs.get("preprocessor_overrides", {}),
             to_transition=batch_to_transition,
             to_output=transition_to_batch,
-            revision=pretrained_revision,
         )
         postprocessor = PolicyProcessorPipeline.from_pretrained(
             pretrained_model_name_or_path=pretrained_path,
@@ -320,7 +318,6 @@ def make_pre_post_processors(
             overrides=kwargs.get("postprocessor_overrides", {}),
             to_transition=policy_action_to_transition,
             to_output=transition_to_policy_action,
-            revision=pretrained_revision,
         )
         _reconnect_relative_absolute_steps(preprocessor, postprocessor)
         return preprocessor, postprocessor
@@ -560,7 +557,6 @@ def make_policy(
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
         # hyperparameters that we want to vary).
         kwargs["pretrained_name_or_path"] = cfg.pretrained_path
-        kwargs["revision"] = cfg.pretrained_revision
         policy = policy_cls.from_pretrained(**kwargs)
     elif cfg.pretrained_path and cfg.use_peft:
         # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo

src/lerobot/rewards/factory.py

@@ -124,7 +124,6 @@ def make_reward_model(cfg: RewardModelConfig, **kwargs) -> PreTrainedRewardModel
 
     if cfg.pretrained_path:
         kwargs["pretrained_name_or_path"] = cfg.pretrained_path
-        kwargs["revision"] = cfg.pretrained_revision
         reward_model = reward_cls.from_pretrained(**kwargs)
     else:
         reward_model = reward_cls(**kwargs)

src/lerobot/scripts/lerobot_eval.py

@@ -72,9 +72,8 @@ from termcolor import colored
 from torch import Tensor, nn
 from tqdm import trange
 
-from lerobot.configs import FeatureType, parser
+from lerobot.configs import parser
 from lerobot.configs.eval import EvalPipelineConfig
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.envs import (
     check_env_attributes_and_types,
     close_envs,
@@ -85,7 +84,7 @@ from lerobot.envs import (
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
-from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
+from lerobot.utils.constants import ACTION, DONE, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.io_utils import write_video
@@ -96,81 +95,6 @@ from lerobot.utils.utils import (
 )
 
 
-def _env_features_to_dataset_features(env_features: dict, raw_obs: dict | None = None) -> dict:
-    """Convert EnvConfig.features (PolicyFeature objects) to the plain dict format for LeRobotDataset.create().
-
-    If raw_obs is provided, visual feature shapes are inferred from the actual observation
-    to avoid mismatches between the env config and the real observation resolution.
-    """
-    features = {}
-    for key, ft in env_features.items():
-        if ft.type is FeatureType.VISUAL:
-            shape = tuple(ft.shape)
-            if raw_obs is not None and key in raw_obs and isinstance(raw_obs[key], np.ndarray):
-                shape = raw_obs[key].shape[1:]  # strip batch dim
-            elif raw_obs is not None and "pixels" in raw_obs:
-                pixels = raw_obs["pixels"]
-                if isinstance(pixels, dict):
-                    for cam_name, img in pixels.items():
-                        if key == f"{OBS_IMAGES}.{cam_name}" or key == cam_name:
-                            shape = img.shape[1:]  # strip batch dim
-                elif key in ("pixels", OBS_IMAGE):
-                    shape = pixels.shape[1:]  # strip batch dim
-            features[key] = {"dtype": "video", "shape": shape, "names": ["height", "width", "channel"]}
-        else:
-            shape = tuple(ft.shape)
-            if raw_obs is not None and key in raw_obs and isinstance(raw_obs[key], np.ndarray):
-                shape = raw_obs[key].shape[1:]  # strip batch dim
-            features[key] = {"dtype": "float32", "shape": shape, "names": None}
-    features["next.reward"] = {"dtype": "float32", "shape": (1,), "names": None}
-    features["next.success"] = {"dtype": "bool", "shape": (1,), "names": None}
-    features["next.done"] = {"dtype": "bool", "shape": (1,), "names": None}
-    return features
-
-
-def _build_raw_frame(
-    raw_obs: dict,
-    env_idx: int,
-    action: np.ndarray,
-    reward: float,
-    success: bool,
-    done: bool,
-    task: str,
-    env_features: dict,
-) -> dict:
-    """Build a dataset frame from raw env observations for one env index.
-
-    Keys in the frame match the keys in env_features so they align with the
-    dataset schema created by _env_features_to_dataset_features().
-    """
-    frame: dict[str, Any] = {}
-    for key in env_features:
-        if key == ACTION:
-            continue
-        if "pixels" in raw_obs and isinstance(raw_obs["pixels"], dict):
-            for cam_name, img in raw_obs["pixels"].items():
-                candidate = f"{OBS_IMAGES}.{cam_name}"
-                if candidate == key:
-                    frame[key] = img[env_idx]
-            if key in frame:
-                continue
-        if "pixels" in raw_obs and not isinstance(raw_obs["pixels"], dict) and key in ("pixels", OBS_IMAGE):
-            frame[key] = raw_obs["pixels"][env_idx]
-            continue
-        raw_key = key
-        if raw_key in raw_obs and isinstance(raw_obs[raw_key], np.ndarray):
-            val = raw_obs[raw_key][env_idx]
-            if val.dtype == np.float64:
-                val = val.astype(np.float32)
-            frame[key] = val
-    frame[ACTION] = action
-    frame["next.reward"] = np.atleast_1d(np.float32(reward))
-    frame["next.success"] = np.atleast_1d(np.bool_(success))
-    frame["next.done"] = np.atleast_1d(np.bool_(done))
-    frame["task"] = task
-    return frame
-
-
 def rollout(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,
@@ -181,7 +105,6 @@ def rollout(
     seeds: list[int] | None = None,
     return_observations: bool = False,
     render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,
-    recording_dataset: Any | None = None,
 ) -> dict:
     """Run a batched policy rollout once through a batch of environments.
 
@@ -222,14 +145,6 @@ def rollout(
     if render_callback is not None:
         render_callback(env)
 
-    raw_observation = deepcopy(observation) if recording_dataset is not None else None
-    task_desc = ""
-    if recording_dataset is not None:
-        try:
-            task_desc = list(env.call("task_description"))[0]
-        except (AttributeError, NotImplementedError):
-            task_desc = ""
-
     all_observations = []
     all_actions = []
     all_rewards = []
@@ -302,26 +217,6 @@ def rollout(
         else:
             successes = [False] * env.num_envs
 
-        if recording_dataset is not None and raw_observation is not None:
-            prev_done = done.copy()
-            for env_idx in range(env.num_envs):
-                if prev_done[env_idx]:
-                    continue
-                frame = _build_raw_frame(
-                    raw_observation,
-                    env_idx,
-                    action_numpy[env_idx],
-                    reward[env_idx],
-                    successes[env_idx],
-                    bool(terminated[env_idx] | truncated[env_idx]),
-                    task_desc,
-                    recording_dataset.features,
-                )
-                recording_dataset.add_frame(frame)
-                if terminated[env_idx] or truncated[env_idx]:
-                    recording_dataset.save_episode()
-            raw_observation = deepcopy(observation)
-
         # Keep track of which environments are done so far.
         # Mark the episode as done if we reach the maximum step limit.
         # This ensures that the rollout always terminates cleanly at `max_steps`,
@@ -378,7 +273,6 @@ def eval_policy(
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
-    recording_dataset: Any | None = None,
 ) -> dict:
     """
     Args:
@@ -467,7 +361,6 @@ def eval_policy(
             seeds=list(seeds) if seeds else None,
             return_observations=return_episode_data,
             render_callback=render_frame if max_episodes_rendered > 0 else None,
-            recording_dataset=recording_dataset,
         )
 
         # Figure out where in each rollout sequence the first done condition was encountered (results after
@@ -670,10 +563,6 @@ def eval_main(cfg: EvalPipelineConfig):
     # Create environment-specific preprocessor and postprocessor (e.g., for LIBERO environments)
     env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env, policy_cfg=cfg.policy)
 
-    recording_dir = Path(cfg.output_dir) / "recordings" if cfg.eval.recording else None
-    max_episodes_rendered = 0 if cfg.eval.recording else 10
-    videos_dir = None if cfg.eval.recording else Path(cfg.output_dir) / "videos"
-
     with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy_all(
             envs=envs,
@@ -683,13 +572,10 @@ def eval_main(cfg: EvalPipelineConfig):
             preprocessor=preprocessor,
             postprocessor=postprocessor,
             n_episodes=cfg.eval.n_episodes,
-            max_episodes_rendered=max_episodes_rendered,
-            videos_dir=videos_dir,
-            return_episode_data=False,
+            max_episodes_rendered=10,
+            videos_dir=Path(cfg.output_dir) / "videos",
             start_seed=cfg.seed,
             max_parallel_tasks=cfg.env.max_parallel_tasks,
-            recording_dir=recording_dir,
-            env_features=cfg.env.features if cfg.eval.recording else None,
         )
         print("Overall Aggregated Metrics:")
         print(info["overall"])
@@ -732,7 +618,6 @@ def eval_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
-    recording_dataset: Any | None = None,
 ) -> TaskMetrics:
     """Evaluates one task_id of one suite using the provided vec env."""
 
@@ -750,7 +635,6 @@ def eval_one(
         videos_dir=task_videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
-        recording_dataset=recording_dataset,
     )
 
     per_episode = task_result["per_episode"]
@@ -777,8 +661,6 @@ def run_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
-    recording_dir: Path | None = None,
-    env_features: dict | None = None,
 ):
     """
     Run eval_one for a single (task_group, task_id, env).
@@ -790,39 +672,21 @@ def run_one(
         task_videos_dir = videos_dir / f"{task_group}_{task_id}"
         task_videos_dir.mkdir(parents=True, exist_ok=True)
 
-    recording_dataset = None
-    if recording_dir is not None and env_features is not None:
-        task_recording_dir = recording_dir / f"{task_group}_{task_id}"
-        fps = env.unwrapped.metadata.get("render_fps", 30)
-        sample_obs, _ = env.reset()
-        features = _env_features_to_dataset_features(env_features, raw_obs=sample_obs)
-        recording_dataset = LeRobotDataset.create(
-            repo_id=f"eval_{task_group}_{task_id}",
-            fps=fps,
-            features=features,
-            root=str(task_recording_dir),
-            use_videos=True,
-        )
-
-    try:
-        metrics = eval_one(
-            env,
-            policy=policy,
-            env_preprocessor=env_preprocessor,
-            env_postprocessor=env_postprocessor,
-            preprocessor=preprocessor,
-            postprocessor=postprocessor,
-            n_episodes=n_episodes,
-            max_episodes_rendered=max_episodes_rendered,
-            videos_dir=task_videos_dir,
-            return_episode_data=return_episode_data,
-            start_seed=start_seed,
-            recording_dataset=recording_dataset,
-        )
-    finally:
-        if recording_dataset is not None:
-            recording_dataset.finalize()
-
+    # Call the existing eval_one (assumed to return TaskMetrics-like dict)
+    metrics = eval_one(
+        env,
+        policy=policy,
+        env_preprocessor=env_preprocessor,
+        env_postprocessor=env_postprocessor,
+        preprocessor=preprocessor,
+        postprocessor=postprocessor,
+        n_episodes=n_episodes,
+        max_episodes_rendered=max_episodes_rendered,
+        videos_dir=task_videos_dir,
+        return_episode_data=return_episode_data,
+        start_seed=start_seed,
+    )
+    # ensure we always provide video_paths key to simplify accumulation
     if max_episodes_rendered > 0:
         metrics.setdefault("video_paths", [])
     return task_group, task_id, metrics
@@ -838,8 +702,6 @@ def eval_policy_all(
     n_episodes: int,
     *,
     max_episodes_rendered: int = 0,
-    recording_dir: Path | None = None,
-    env_features: dict | None = None,
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
@@ -899,8 +761,6 @@ def eval_policy_all(
         videos_dir=videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
-        recording_dir=recording_dir,
-        env_features=env_features,
     )
 
     if max_parallel_tasks <= 1:

src/lerobot/scripts/lerobot_train.py

@@ -45,8 +45,7 @@ from lerobot.common.train_utils import (
 from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state
-from lerobot.datasets.factory import make_train_eval_datasets
+from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state, make_dataset
 from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
@@ -245,19 +244,19 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     # LeRobotDataset skips its snapshot_download when try_load() succeeds, so no rank re-downloads.
     if is_main_process:
         logging.info("Creating dataset")
-        dataset, eval_dataset = make_train_eval_datasets(cfg)
+        dataset = make_dataset(cfg)
 
     accelerator.wait_for_everyone()
 
     # Other ranks read from the shared copy populated by the main process.
     if not is_main_process:
-        dataset, eval_dataset = make_train_eval_datasets(cfg)
+        dataset = make_dataset(cfg)
 
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
     # using the eval.py instead, with gym_dora environment and dora-rs.
     eval_env = None
-    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
+    if cfg.eval_freq > 0 and cfg.env is not None and is_main_process:
         logging.info("Creating env")
         eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
 
@@ -346,7 +345,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         preprocessor, postprocessor = make_pre_post_processors(
             policy_cfg=cfg.policy,
             pretrained_path=processor_pretrained_path,
-            pretrained_revision=getattr(cfg.policy, "pretrained_revision", None),
             **processor_kwargs,
         )
 
@@ -457,31 +455,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
     )
 
-    # Build eval dataloader if a held-out split exists
-    eval_dataloader = None
-    if eval_dataset is not None:
-        eval_ds = eval_dataset
-        if cfg.max_eval_samples > 0 and hasattr(eval_dataset, "hf_dataset"):
-            task_indices = eval_dataset.hf_dataset["task_index"]
-            unique_tasks = sorted(set(task_indices))
-            per_task = max(1, cfg.max_eval_samples // len(unique_tasks))
-            selected: list[int] = []
-            for t in unique_tasks:
-                frames = [i for i, ti in enumerate(task_indices) if ti == t][:per_task]
-                selected.extend(frames)
-            eval_ds = torch.utils.data.Subset(eval_dataset, selected)
-
-        eval_collate_fn = lerobot_collate_fn if dataset.meta.has_language_columns else None
-        eval_dataloader = torch.utils.data.DataLoader(
-            eval_ds,
-            batch_size=cfg.batch_size,
-            shuffle=False,
-            num_workers=cfg.num_workers,
-            pin_memory=device.type == "cuda",
-            drop_last=False,
-            collate_fn=eval_collate_fn,
-        )
-
     # Prepare everything with accelerator
     accelerator.wait_for_everyone()
     policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
@@ -561,8 +534,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
-        is_env_eval_step = cfg.env_eval_freq > 0 and step % cfg.env_eval_freq == 0
-        is_eval_step = cfg.eval_steps > 0 and eval_dataloader is not None and step % cfg.eval_steps == 0
+        is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
 
         if is_log_step:
             # Collective reduce must run on every rank, before the main-process gate below.
@@ -585,27 +557,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     wandb_logger.log_dict(wandb_log_dict, step)
             train_tracker.reset_averages()
 
-        if is_eval_step:
-            policy.eval()
-            eval_loss_sum = 0.0
-            n_eval_batches = 0
-            with torch.no_grad(), accelerator.autocast():
-                for eval_batch in eval_dataloader:
-                    for cam_key in dataset.meta.camera_keys:
-                        if cam_key in eval_batch and eval_batch[cam_key].dtype == torch.uint8:
-                            eval_batch[cam_key] = eval_batch[cam_key].to(dtype=torch.float32) / 255.0
-                    eval_batch = preprocessor(eval_batch)
-                    loss, _ = policy.forward(eval_batch)
-                    eval_loss_sum += loss.item()
-                    n_eval_batches += 1
-            eval_loss = eval_loss_sum / max(n_eval_batches, 1)
-            policy.train()
-
-            if is_main_process:
-                logging.info(f"step {step}: eval_loss={eval_loss:.4f}")
-                if wandb_logger:
-                    wandb_logger.log_dict({"eval_loss": eval_loss}, step=step, mode="eval")
-
         if cfg.save_checkpoint and is_saving_step:
             if is_main_process:
                 logging.info(f"Checkpoint policy after step {step}")
@@ -628,7 +579,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
 
             accelerator.wait_for_everyone()
 
-        if cfg.env and is_env_eval_step:
+        if cfg.env and is_eval_step:
             if is_main_process:
                 step_id = get_step_identifier(step, cfg.steps)
                 logging.info(f"Eval policy at step {step}")

src/lerobot/utils/import_utils.py

@@ -216,15 +216,9 @@ def register_third_party_plugins() -> None:
 
     This function uses `importlib.metadata` to find packages installed in the environment
     (including editable installs) starting with 'lerobot_robot_', 'lerobot_camera_',
-    'lerobot_teleoperator_', 'lerobot_policy_', or 'lerobot_env_' and imports them.
+    'lerobot_teleoperator_', or 'lerobot_policy_' and imports them.
     """
-    prefixes = (
-        "lerobot_robot_",
-        "lerobot_camera_",
-        "lerobot_teleoperator_",
-        "lerobot_policy_",
-        "lerobot_env_",
-    )
+    prefixes = ("lerobot_robot_", "lerobot_camera_", "lerobot_teleoperator_", "lerobot_policy_")
     imported: list[str] = []
     failed: list[str] = []
 

tests/datasets/test_episode_video_streaming.py

@@ -0,0 +1,121 @@
+#!/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
+
+import json
+import struct
+
+import numpy as np
+import pytest
+
+from lerobot.datasets.episode_video_streaming import assert_hf_hub_range_cache_branch
+from lerobot.datasets.mp4 import (
+    _box,
+    _co64,
+    _dinf,
+    _hdlr,
+    _mdhd,
+    _mvhd,
+    _stco,
+    _stsc_one_sample_per_chunk,
+    _stss,
+    _stsz,
+    _stts,
+    _tkhd,
+    _vmhd,
+    parse_mp4_index,
+    synthesize_mp4,
+)
+
+
+def _minimal_mp4(sample_offsets: list[int], *, use_co64: bool = False) -> bytes:
+    ftyp = _box(b"ftyp", b"isom\0\0\2\0isomiso2mp41")
+    sizes = np.array([10, 10, 10], dtype=np.int64)
+    durations = np.array([1000, 1000, 1000], dtype=np.int64)
+    stsd_body = struct.pack(">II", 0, 1) + struct.pack(">I4s", 16, b"avc1") + b"\0" * 8
+    offsets = _co64(sample_offsets) if use_co64 else _stco(sample_offsets)
+    stbl = _box(
+        b"stbl",
+        _box(b"stsd", stsd_body)
+        + _stts(durations)
+        + _stsc_one_sample_per_chunk(len(sizes))
+        + _stsz(sizes)
+        + offsets
+        + _stss(np.array([1], dtype=np.int64)),
+    )
+    minf = _box(b"minf", _vmhd() + _dinf() + stbl)
+    mdia = _box(b"mdia", _mdhd(1000, 3000) + _hdlr() + minf)
+    trak = _box(b"trak", _tkhd(1, 3000, 64, 48) + mdia)
+    moov = _box(b"moov", _mvhd(1000, 3000, 2) + trak)
+    mdat_payload_start = 10_000
+    free_size = mdat_payload_start - 8 - len(ftyp) - len(moov)
+    assert free_size >= 8
+    free = _box(b"free", b"\0" * (free_size - 8))
+    return ftyp + moov + free + _box(b"mdat", b"x" * 128)
+
+
+def test_episode_slice_uses_min_max_sample_offsets_for_reordered_chunks():
+    mp4 = parse_mp4_index("test.mp4", _minimal_mp4([10_000, 10_050, 10_025]))
+
+    sample_slice = mp4.sample_slice(0.0, 2.0, keyframe_pad_s=0, keyframe_pad_fraction=0)
+
+    assert sample_slice.byte_offset == 10_000
+    assert sample_slice.byte_length == 60
+    assert sample_slice.sample_lo == 0
+    assert sample_slice.sample_hi == 2
+
+
+def test_synthesized_mp4_rebases_one_chunk_per_sample_offsets():
+    mp4 = parse_mp4_index("test.mp4", _minimal_mp4([10_000, 10_050, 10_025]))
+    sample_slice = mp4.sample_slice(0.0, 2.0, keyframe_pad_s=0, keyframe_pad_fraction=0)
+
+    mini = synthesize_mp4(mp4, sample_slice, b"x" * sample_slice.byte_length)
+    mini_index = parse_mp4_index("mini.mp4", mini)
+
+    expected = np.array([0, 50, 25], dtype=np.int64) + mini_index.mdat_payload_offset
+    np.testing.assert_array_equal(mini_index.sample_offsets, expected)
+    np.testing.assert_array_equal(mini_index.sample_sizes, np.array([10, 10, 10]))
+
+
+def test_parser_accepts_co64_chunk_offsets():
+    mp4 = parse_mp4_index("test.mp4", _minimal_mp4([10_000, 10_050, 10_025], use_co64=True))
+
+    np.testing.assert_array_equal(mp4.sample_offsets, np.array([10_000, 10_050, 10_025]))
+
+
+def test_hf_hub_branch_assertion_accepts_requested_revision(monkeypatch):
+    class FakeDist:
+        def read_text(self, name):
+            assert name == "direct_url.json"
+            return json.dumps(
+                {
+                    "url": "https://github.com/huggingface/huggingface_hub.git",
+                    "vcs_info": {"requested_revision": "feat/hffs-cache-cdn-range-reads"},
+                }
+            )
+
+    monkeypatch.setattr(
+        "lerobot.datasets.episode_video_streaming.metadata.distribution", lambda _: FakeDist()
+    )
+
+    assert_hf_hub_range_cache_branch()
+
+
+def test_hf_hub_branch_assertion_rejects_plain_install(monkeypatch):
+    class FakeDist:
+        def read_text(self, name):
+            assert name == "direct_url.json"
+            return json.dumps({"url": "https://github.com/huggingface/huggingface_hub.git"})
+
+    monkeypatch.setattr(
+        "lerobot.datasets.episode_video_streaming.metadata.distribution", lambda _: FakeDist()
+    )
+
+    with pytest.raises(AssertionError):
+        assert_hf_hub_range_cache_branch()

tests/processor/test_pipeline.py

@@ -2370,14 +2370,32 @@ def test_aggregate_images_when_use_videos_false():
     out = aggregate_pipeline_dataset_features(
         pipeline=rp,
         initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
-        use_videos=False,  # expect "image" dtype
+        use_videos=False,  # images kept, stored as "image" dtype
         patterns=None,
     )
 
     key = f"{OBS_IMAGES}.back"
     key_front = f"{OBS_IMAGES}.front"
-    assert key not in out
-    assert key_front not in out
+    assert key in out
+    assert key_front in out
+    assert out[key]["dtype"] == "image"
+    assert out[key_front]["dtype"] == "image"
+    assert out[key]["shape"] == initial["back"]
+
+
+def test_aggregate_images_excluded():
+    rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
+    initial = {"back": (480, 640, 3)}
+
+    out = aggregate_pipeline_dataset_features(
+        pipeline=rp,
+        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
+        exclude_images=True,
+        patterns=None,
+    )
+
+    assert f"{OBS_IMAGES}.back" not in out
+    assert f"{OBS_IMAGES}.front" not in out
 
 
 def test_aggregate_images_when_use_videos_true():

tests/training/test_multi_gpu.py

@@ -134,7 +134,7 @@ class TestMultiGPUTraining:
                 f"--output_dir={output_dir}",
                 "--batch_size=4",
                 "--steps=10",
-                "--env_eval_freq=-1",
+                "--eval_freq=-1",
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",
@@ -177,7 +177,7 @@ class TestMultiGPUTraining:
                 f"--output_dir={output_dir}",
                 "--batch_size=4",
                 "--steps=20",
-                "--env_eval_freq=-1",
+                "--eval_freq=-1",
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "(python_full_version >= '3.15' and platform_machine == 'AMD64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'linux')",
@@ -1089,8 +1089,8 @@ wheels = [
 
 [[package]]
 name = "datasets"
-version = "4.8.5"
-source = { registry = "https://pypi.org/simple" }
+version = "5.0.1.dev0"
+source = { git = "https://github.com/huggingface/datasets.git?branch=main#06fcc085fcdd22fc5cc741954f6187dd879543b6" }
 dependencies = [
     { name = "dill" },
     { name = "filelock" },
@@ -1107,10 +1107,6 @@ dependencies = [
     { name = "tqdm" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/66/34/14cd8e76f907f7d4dca2334cfeec9f81d30fd15c25a015f99aaea694eaed/datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772", size = 605649, upload-time = "2026-04-27T15:43:57.384Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/65/99/00f3196036501b53032c4b1ab8337a0b978dee832ed276dae3815df4e8b5/datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff", size = 528973, upload-time = "2026-04-27T15:43:53.702Z" },
-]
 
 [[package]]
 name = "debugpy"
@@ -1147,7 +1143,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "numpy", marker = "(platform_machine != 'arm64' and platform_machine != 's390x' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -2050,8 +2046,8 @@ wheels = [
 
 [[package]]
 name = "huggingface-hub"
-version = "1.19.0"
-source = { registry = "https://pypi.org/simple" }
+version = "1.20.0.dev0"
+source = { git = "https://github.com/huggingface/huggingface_hub.git?branch=feat%2Fhffs-cache-cdn-range-reads#5319b287faa73239bb40df16d69c39e5d6daf0f7" }
 dependencies = [
     { name = "click" },
     { name = "filelock" },
@@ -2064,10 +2060,6 @@ dependencies = [
     { name = "typer" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/88/27/629cfe58c582f92ded066c4a07d1a057ff617118ab7973200f770bd853cb/huggingface_hub-1.19.0.tar.gz", hash = "sha256:fd771622182d40977272a923953ee3b1b13538f9f8a7f5d78398f10af0f1c0bd", size = 824721, upload-time = "2026-06-11T12:33:18.665Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b2/a5/558da89f66464d8d0229ff497e8b8666977de2d8cf48c28a2862ecf1250f/huggingface_hub-1.19.0-py3-none-any.whl", hash = "sha256:1dc72e1f6b4d6df6b30eb72e57d00514ef453d660f04af2b87f0e67267f31ee0", size = 693398, upload-time = "2026-06-11T12:33:16.695Z" },
-]
 
 [[package]]
 name = "hydra-core"
@@ -3187,7 +3179,7 @@ requires-dist = [
     { name = "av", marker = "extra == 'av-dep'", specifier = ">=15.0.0,<16.0.0" },
     { name = "cmake", specifier = ">=3.29.0.1,<4.2.0" },
     { name = "contourpy", marker = "extra == 'matplotlib-dep'", specifier = ">=1.3.0,<2.0.0" },
-    { name = "datasets", marker = "extra == 'dataset'", specifier = ">=4.7.0,<5.0.0" },
+    { name = "datasets", marker = "extra == 'dataset'", git = "https://github.com/huggingface/datasets.git?branch=main" },
     { name = "debugpy", marker = "extra == 'dev'", specifier = ">=1.8.1,<1.9.0" },
     { name = "decord", marker = "(platform_machine == 'AMD64' and extra == 'groot') or (platform_machine == 'x86_64' and extra == 'groot')", specifier = ">=0.6.0,<1.0.0" },
     { name = "deepdiff", marker = "extra == 'deepdiff-dep'", specifier = ">=7.0.1,<9.0.0" },
@@ -3210,7 +3202,7 @@ requires-dist = [
     { name = "hebi-py", marker = "extra == 'phone'", specifier = ">=2.8.0,<2.12.0" },
     { name = "hf-libero", marker = "sys_platform == 'linux' and extra == 'libero'", specifier = ">=0.1.4,<0.2.0" },
     { name = "hidapi", marker = "extra == 'gamepad'", specifier = ">=0.14.0,<0.15.0" },
-    { name = "huggingface-hub", specifier = ">=1.0.0,<2.0.0" },
+    { name = "huggingface-hub", git = "https://github.com/huggingface/huggingface_hub.git?branch=feat%2Fhffs-cache-cdn-range-reads" },
     { name = "ipykernel", marker = "extra == 'notebook'", specifier = ">=6.0.0,<7.0.0" },
     { name = "jsonlines", marker = "extra == 'dataset'", specifier = ">=4.0.0,<5.0.0" },
     { name = "jupyter", marker = "extra == 'notebook'", specifier = ">=1.0.0,<2.0.0" },