diff --git a/src/lerobot/policies/pi052/fit_fast_tokenizer.py b/src/lerobot/policies/pi052/fit_fast_tokenizer.py
index 37155d98d..14e4217ca 100644
--- a/src/lerobot/policies/pi052/fit_fast_tokenizer.py
+++ b/src/lerobot/policies/pi052/fit_fast_tokenizer.py
@@ -40,7 +40,6 @@ from __future__ import annotations
 import hashlib
 import logging
 from pathlib import Path
-from typing import Any
 
 import numpy as np
 
@@ -133,44 +132,49 @@ def fit_fast_tokenizer(
     # Stream a single episode's worth of action chunks at a time so
     # we don't blow memory on huge datasets. Random episode +
     # random start offset gives a reasonable spread.
+    #
+    # Actions are read straight from the underlying HF dataset's
+    # ``action`` *column* — never via ``ds[i]``. ``ds[i]`` builds a full
+    # training item (delta-timestamp expansion + video decode + image
+    # transforms); a single bad video frame would then throw and, since
+    # the failure was swallowed at debug level, silently starve the fit
+    # of every chunk. The action column carries no video, so reading it
+    # directly is both faster and immune to decode errors.
     rng = np.random.default_rng(seed)
     actions_buf: list[np.ndarray] = []
 
     # Load just the metadata first to know episode boundaries.
     ds_meta_only = LeRobotDataset(dataset_repo_id, episodes=[0])
     num_episodes = ds_meta_only.meta.total_episodes
+    if "action" not in ds_meta_only.features:
+        available = ", ".join(sorted(ds_meta_only.features)) or "<none>"
+        raise RuntimeError(
+            f"FAST fit: dataset {dataset_repo_id!r} has no ``action`` feature. "
+            f"Available features: {available}."
+        )
     del ds_meta_only
 
     samples_per_episode = max(1, n_samples // max(num_episodes, 1))
     collected = 0
     eps_visited = 0
+    short_episodes = 0
     for ep_idx in rng.permutation(num_episodes):
         if collected >= n_samples:
             break
         ep_idx = int(ep_idx)
         try:
             ds = LeRobotDataset(dataset_repo_id, episodes=[ep_idx])
+            ep_actions = np.asarray(ds.hf_dataset["action"], dtype=np.float32)
         except Exception as exc:  # noqa: BLE001
             logger.warning("FAST fit: skipping episode %d: %s", ep_idx, exc)
             continue
-        if len(ds) < chunk_size:
+        if ep_actions.ndim != 2 or ep_actions.shape[0] < chunk_size:
+            short_episodes += 1
             continue
-        # Sample ``samples_per_episode`` start indices uniformly within
-        # the episode.
-        starts = rng.integers(0, len(ds) - chunk_size + 1, size=samples_per_episode)
+        # Sample ``samples_per_episode`` contiguous chunks uniformly.
+        starts = rng.integers(0, ep_actions.shape[0] - chunk_size + 1, size=samples_per_episode)
         for s in starts:
-            try:
-                chunk_actions = np.stack(
-                    [
-                        np.asarray(ds[int(s) + j]["action"].cpu().numpy())
-                        for j in range(chunk_size)
-                    ],
-                    axis=0,
-                )
-            except Exception as exc:  # noqa: BLE001
-                logger.debug("FAST fit: chunk at ep=%d s=%d failed: %s", ep_idx, s, exc)
-                continue
-            actions_buf.append(chunk_actions)
+            actions_buf.append(ep_actions[int(s) : int(s) + chunk_size])
             collected += 1
             if collected >= n_samples:
                 break
@@ -178,9 +182,11 @@ def fit_fast_tokenizer(
 
     if not actions_buf:
         raise RuntimeError(
-            f"FAST fit collected zero action chunks from {dataset_repo_id!r}. "
-            "Check that the dataset has an ``action`` column and chunks of "
-            f"length ≥ {chunk_size}."
+            f"FAST fit collected zero action chunks from {dataset_repo_id!r}: "
+            f"all {num_episodes} episodes were shorter than chunk_size="
+            f"{chunk_size} ({short_episodes} too short) or had an unreadable "
+            "``action`` column. Lower ``chunk_size`` to match your episode "
+            "lengths."
         )
 
     actions = np.stack(actions_buf, axis=0)  # (N, H, D)