use patch tokens

src/lerobot/policies/rlearn/eval_script.py

@@ -155,6 +155,7 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
 
     windows = []
     frame_positions = []  # Track which temporal position each frame should use
+    left_pad_counts = []  # Number of left-pad (OOB) frames per window
     
     for i in range(T):
         start = max(0, i - L + 1)
@@ -162,8 +163,10 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
         
         if window.shape[0] < L:
             pad_needed = L - window.shape[0]
-            pad = window[:1].expand(pad_needed, -1, -1, -1)  # repeat first frame
+            pad = window[:1].expand(pad_needed, -1, -1, -1)  # repeat first frame (clamp to frame 0)
             window = torch.cat([pad, window], dim=0)
+        else:
+            pad_needed = 0
         
         # IMPROVED FIX: Cycle through MLPs to get varied predictions throughout the episode
         # This ensures we use all 16 frame-specific MLPs and get varied outputs
@@ -172,6 +175,7 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
             
         windows.append(window)
         frame_positions.append(frame_pos)
+        left_pad_counts.append(pad_needed)
 
     preds = np.zeros(T, dtype=float)
 
@@ -185,6 +189,13 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
         # Model returns (B, L) predictions for each temporal position
         values = model.predict_rewards(batch)  # torch.Tensor (B, L)
 
+        # Apply eval-time padding rule: predictions for left-padded (OOB) frames are zero
+        if values.dim() == 2 and len(left_pad_counts) >= (e - s):
+            for b_idx in range(e - s):
+                pad_n = left_pad_counts[s + b_idx]
+                if pad_n > 0:
+                    values[b_idx, :pad_n] = 0.0
+
         # Debug output removed - issue was identified and fixed
 
         if values.dim() == 2: