diff --git a/src/lerobot/policies/smolvla2/chat_processor_smolvla2.py b/src/lerobot/policies/smolvla2/chat_processor_smolvla2.py
index ddb08645e..e370a88e9 100644
--- a/src/lerobot/policies/smolvla2/chat_processor_smolvla2.py
+++ b/src/lerobot/policies/smolvla2/chat_processor_smolvla2.py
@@ -122,19 +122,8 @@ class SmolVLA2ChatTokenizerStep(ProcessorStep):
         # flows through into ``COMPLEMENTARY_DATA`` unchanged. When
         # absent (e.g. inference) we fall back to 0 which is harmless
         # because the dropout probs are also 0 at inference time.
-        sample_idx_raw = comp.get("index")
-        if hasattr(sample_idx_raw, "item"):
-            try:
-                sample_idx_raw = sample_idx_raw.item()
-            except Exception:  # noqa: BLE001
-                pass
-
         if _is_batched_messages(messages):
-            indices_iter = (
-                sample_idx_raw
-                if isinstance(sample_idx_raw, (list, tuple))
-                else [sample_idx_raw] * len(messages)
-            )
+            indices_iter = _sample_indices(comp.get("index"), len(messages))
             encoded = [
                 self._encode_messages(
                     tokenizer,
@@ -152,13 +141,14 @@ class SmolVLA2ChatTokenizerStep(ProcessorStep):
                 )
             ]
         else:
+            sample_idx = _sample_indices(comp.get("index"), 1)[0]
             encoded = [
                 self._encode_messages(
                     tokenizer,
                     messages,
                     list(comp.get("message_streams") or []),
                     sorted(int(i) for i in (comp.get("target_message_indices") or [])),
-                    sample_idx=int(sample_idx_raw) if sample_idx_raw is not None else None,
+                    sample_idx=sample_idx,
                 )
             ]
 
@@ -396,6 +386,21 @@ def _is_batched_messages(messages: Any) -> bool:
     return isinstance(messages, list) and bool(messages) and isinstance(messages[0], list)
 
 
+def _sample_indices(value: Any, batch_size: int) -> list[int | None]:
+    if value is None:
+        return [None] * batch_size
+    if isinstance(value, torch.Tensor):
+        if value.numel() == 1:
+            return [int(value.item())] * batch_size
+        values = value.reshape(-1).tolist()
+        return [int(v) for v in values[:batch_size]]
+    if isinstance(value, (list, tuple)):
+        if len(value) == 1:
+            return _sample_indices(value[0], batch_size)
+        return [int(v.item() if hasattr(v, "item") else v) for v in value[:batch_size]]
+    return [int(value)] * batch_size
+
+
 def _classify_message_for_dropout(message: dict[str, Any]) -> str | None:
     """Best-effort classification of which recipe binding contributed
     to this message, used for per-component dropout.