fix: nan in eval

src/lerobot/rewards/temporal_siglip_value_function/modeling_temporal_siglip_value_function.py

@@ -131,21 +131,42 @@ class TemporalSiglipVFRewardModel(DistributionalValueMixin, PreTrainedRewardMode
             frame_tokens + self.time_embedding(torch.arange(history_steps, device=frame_tokens.device))[None]
         )
 
-        causal_mask = torch.triu(
-            torch.ones(history_steps, history_steps, dtype=torch.bool, device=frame_tokens.device),
-            diagonal=1,
-        )
         frame_valid = torch.stack(masks).any(0)
+        attention_mask = self._make_temporal_attention_mask(frame_valid)
         hidden = self.temporal_transformer(
             frame_tokens,
-            mask=causal_mask,
-            src_key_padding_mask=~frame_valid,
-            is_causal=True,
+            mask=attention_mask,
         )
         # The history window is ordered oldest→current and the current frame is
         # always the final, non-padding element.
         return hidden[:, -1]
 
+    def _make_temporal_attention_mask(self, frame_valid: Tensor) -> Tensor:
+        """Combine causal and padding masks without fully masked padded queries.
+
+        A left-padded causal query has no valid past keys. PyTorch's optimized
+        eval path returns NaNs for such rows, which then contaminate later valid
+        tokens. Padded queries attend only to themselves; valid queries retain
+        causal attention and cannot attend to padded keys.
+        """
+        batch_size, history_steps = frame_valid.shape
+        causal_mask = torch.triu(
+            torch.ones(history_steps, history_steps, dtype=torch.bool, device=frame_valid.device),
+            diagonal=1,
+        )
+        attention_mask = causal_mask[None].expand(batch_size, -1, -1) | (~frame_valid)[:, None, :]
+        padded_queries = (~frame_valid).nonzero(as_tuple=False)
+        attention_mask[
+            padded_queries[:, 0],
+            padded_queries[:, 1],
+            padded_queries[:, 1],
+        ] = False
+        return (
+            attention_mask[:, None]
+            .expand(-1, self.config.num_heads, -1, -1)
+            .reshape(batch_size * self.config.num_heads, history_steps, history_steps)
+        )
+
     def _fit_state_dim(self, state: Tensor) -> Tensor:
         if state.shape[-1] > self.config.state_dim:
             return state[..., : self.config.state_dim]

tests/rewards/test_temporal_siglip_value_function.py

@@ -127,3 +127,9 @@ def test_temporal_model_forward(monkeypatch):
     loss, metrics = model(batch)
     assert torch.isfinite(loss)
     assert -1.0 <= metrics["predicted_value_mean"] <= 0.0
+
+    model.eval()
+    with torch.no_grad():
+        eval_loss, eval_metrics = model(batch)
+    assert torch.isfinite(eval_loss)
+    assert -1.0 <= eval_metrics["predicted_value_mean"] <= 0.0