diff --git a/lerobot/common/datasets/lerobot_dataset.py b/lerobot/common/datasets/lerobot_dataset.py
index d5a780ac..8afabd1f 100644
--- a/lerobot/common/datasets/lerobot_dataset.py
+++ b/lerobot/common/datasets/lerobot_dataset.py
@@ -707,9 +707,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         item = {}
         for vid_key, query_ts in query_timestamps.items():
             video_path = self.root / self.meta.get_video_file_path(ep_idx, vid_key)
-            frames = decode_video_frames_torchcodec(
-                video_path, query_ts, self.tolerance_s
-            )
+            frames = decode_video_frames_torchcodec(video_path, query_ts, self.tolerance_s)
             item[vid_key] = frames.squeeze(0)
 
         return item
diff --git a/lerobot/common/datasets/video_utils.py b/lerobot/common/datasets/video_utils.py
index 3090666e..587b11cb 100644
--- a/lerobot/common/datasets/video_utils.py
+++ b/lerobot/common/datasets/video_utils.py
@@ -29,6 +29,7 @@ from datasets.features.features import register_feature
 from PIL import Image
 from torchcodec.decoders import VideoDecoder
 
+
 def decode_video_frames_torchvision(
     video_path: Path | str,
     timestamps: list[float],
@@ -125,7 +126,8 @@ def decode_video_frames_torchvision(
 
     assert len(timestamps) == len(closest_frames)
     return closest_frames
-    
+
+
 def decode_video_frames_torchcodec(
     video_path: Path | str,
     timestamps: list[float],
@@ -142,26 +144,26 @@ def decode_video_frames_torchcodec(
     # get metadata for frame information
     metadata = decoder.metadata
     average_fps = metadata.average_fps
-    
+
     # convert timestamps to frame indices
     frame_indices = [round(ts * average_fps) for ts in timestamps]
-    
+
     # retrieve frames based on indices
     frames_batch = decoder.get_frames_at(indices=frame_indices)
-    
-    for frame, pts in zip(frames_batch.data, frames_batch.pts_seconds):
+
+    for frame, pts in zip(frames_batch.data, frames_batch.pts_seconds, strict=False):
         loaded_frames.append(frame)
         loaded_ts.append(pts.item())
         if log_loaded_timestamps:
             logging.info(f"Frame loaded at timestamp={pts:.4f}")
-    
+
     query_ts = torch.tensor(timestamps)
     loaded_ts = torch.tensor(loaded_ts)
-    
+
     # compute distances between each query timestamp and loaded timestamps
     dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1)
     min_, argmin_ = dist.min(1)
-    
+
     is_within_tol = min_ < tolerance_s
     assert is_within_tol.all(), (
         f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
@@ -172,20 +174,21 @@ def decode_video_frames_torchcodec(
         f"\nloaded timestamps: {loaded_ts}"
         f"\nvideo: {video_path}"
     )
-    
+
     # get closest frames to the query timestamps
     closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
     closest_ts = loaded_ts[argmin_]
-    
+
     if log_loaded_timestamps:
         logging.info(f"{closest_ts=}")
-    
+
     # convert to float32 in [0,1] range (channel first)
     closest_frames = closest_frames.type(torch.float32) / 255
-    
+
     assert len(timestamps) == len(closest_frames)
     return closest_frames
 
+
 def encode_video_frames(
     imgs_dir: Path | str,
     video_path: Path | str,