smaller siglip2

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -39,8 +39,8 @@ class RLearNConfig(PreTrainedConfig):
     """
 
     # Encoders - Use SigLIP2 for both vision and text (shared checkpoint)
-    vision_model_name: str = "google/siglip2-base-patch16-512"
-    text_model_name: str = "google/siglip2-base-patch16-512"
+    vision_model_name: str = "google/siglip2-base-patch16-224"
+    text_model_name: str = "google/siglip2-base-patch16-224"
     freeze_backbones: bool = True
 
     # Sequence length, amount of past frames including current one to use in the temporal model

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -207,7 +207,7 @@ class RLearNPolicy(PreTrainedPolicy):
             # No per-patch tokens available, synthesize single patch from pooler
             patch_tokens_flat = vision_outputs.pooler_output[:, None, :]  # (BT, 1, D)
         else:
-            raise RuntimeError("DINOv3 outputs do not contain last_hidden_state or pooler_output")
+            raise RuntimeError("SigLIP2 vision outputs do not contain last_hidden_state or pooler_output")
         
         # Robustly reshape to (B, T, P, D): detect correct flatten order by maximizing temporal variance (on patch-mean)
         try:
@@ -224,7 +224,7 @@ class RLearNPolicy(PreTrainedPolicy):
             diff2 = mean_time_diff_4d(cand2)
             patch_features = cand1 if diff1 >= diff2 else cand2
             if self.training and torch.rand(1).item() < 0.05:
-                print(f"DINO reshape choice: {'(b t)->b t' if diff1 >= diff2 else '(t b)->b t'} | diff1={diff1.item():.6f}, diff2={diff2.item():.6f}")
+                print(f"SigLIP reshape choice: {'(b t)->b t' if diff1 >= diff2 else '(t b)->b t'} | diff1={diff1.item():.6f}, diff2={diff2.item():.6f}")
         except Exception:
             # Fallback to default
             P = patch_tokens_flat.shape[1]
@@ -233,7 +233,7 @@ class RLearNPolicy(PreTrainedPolicy):
         # DEBUG: Analyze vision feature variability (use per-frame pooled features for readability)
         if self.training and torch.rand(1).item() < 0.1:  # 10% of training steps for more frequent debugging
             with torch.no_grad():
-                print(f"\n🔍 DINOv3 VISION FEATURE DEBUG (B={B}, T={T}):")
+                print(f"\n🔍 SigLIP2 VISION FEATURE DEBUG (B={B}, T={T}):")
                 
                 # CRITICAL: Check if input frames are actually different
                 print(f"Raw frame tensor stats: mean={frames.mean():.6f}, std={frames.std():.6f}")