fix

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -186,7 +186,7 @@ class RLearNPolicy(PreTrainedPolicy):
                 min_value=config.reward_min_value,
                 max_value=config.reward_max_value,
                 num_bins=config.reward_hl_gauss_loss_num_bins,
-            ) if config.use_hl_gauss_loss else None
+            )  # Always provide config, HLGaussLayer needs it even for regression mode
         )
         
         # Simple frame dropout probability
@@ -559,10 +559,13 @@ class RLearNPolicy(PreTrainedPolicy):
         # DEBUG: Print targets and predictions occasionally during training
         if self.training and torch.rand(1).item() < 0.02:  # ~2% chance to debug print
             with torch.no_grad():
+                # Get raw MLP outputs before HLGauss
+                raw_outputs = video_frame_embeds
                 preds = self.hl_gauss_layer(video_frame_embeds).squeeze(-1)
                 print(f"\n=== DEBUG TRAINING ===")
                 print(f"Target range: [{target.min():.3f}, {target.max():.3f}]")
                 print(f"Target mean: {target.mean():.3f}")
+                print(f"Raw MLP range: [{raw_outputs.min():.3f}, {raw_outputs.max():.3f}]")
                 print(f"Pred range: [{preds.min():.3f}, {preds.max():.3f}]") 
                 print(f"Pred mean: {preds.mean():.3f}")
                 print(f"Loss: {loss:.4f}")