diff --git a/src/lerobot/datasets/lerobot_dataset.py b/src/lerobot/datasets/lerobot_dataset.py
index 72c277b67..295f67b48 100644
--- a/src/lerobot/datasets/lerobot_dataset.py
+++ b/src/lerobot/datasets/lerobot_dataset.py
@@ -80,31 +80,48 @@ from lerobot.utils.constants import HF_LEROBOT_HOME
 CODEBASE_VERSION = "v3.0"
 
 
-def hf_transform_to_torch(items_dict: dict[str, Any]) -> dict[str, torch.Tensor | str]:
+def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
     """
-    Converts a single item (row) from a Hugging Face dataset to torch tensors.
-    This item-level transform allows `datasets` to build an efficient, pre-processed cache.
+    Converts a batch from a Hugging Face dataset to torch tensors.
     """
-    for key in items_dict:
-        item = items_dict[key]
 
-        if item is None:
+    # Create a single ToTensor transform instance to reuse
+    to_tensor = transforms.ToTensor()
+
+    for key in items_dict:
+        items_list = items_dict[key]
+
+        # Check if the list is non-empty
+        if not items_list:
             continue
 
-        if isinstance(item, PILImage.Image):
-            # Correctly transform PIL images to tensors
-            items_dict[key] = transforms.ToTensor()(item)
-        elif isinstance(item, (str, bytes)):
-            # Let strings (like 'task') pass through untouched
+        first_item = items_list[0]
+
+        if isinstance(first_item, PILImage.Image):
+            # This is the (slow) CPU-bound part.
+            # We convert every image in the batch list to a tensor.
+            items_dict[key] = [to_tensor(img) for img in items_list]
+
+        elif isinstance(first_item, (str, bytes)):
+            # List of strings (e.g., 'task'), do nothing
             pass
+
+        elif first_item is None:
+            # List of Nones, do nothing
+            pass
+
         else:
-            # Convert all other numeric types (int, float, list, np.ndarray) to tensors
+            # List of other things (int, float, list, np.ndarray)
             try:
-                items_dict[key] = torch.tensor(item)
+                # Convert each item in the list to a tensor
+                items_dict[key] = [torch.tensor(item) for item in items_list]
             except Exception as e:
-                # Catch errors like the one you saw
-                print(f"Error converting item['{key}'] to tensor. Value: {item}, Type: {type(item)}")
+                # This catch is what was missing from the original v3.0 code
+                print(
+                    f"Error converting batch['{key}'] to tensor. First item: {first_item}, Type: {type(first_item)}"
+                )
                 raise e
+
     return items_dict