nit

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -59,7 +59,7 @@ class RLearNConfig(PreTrainedConfig):
     use_tanh_head: bool = False  # when True, bound outputs in [-1, 1]
 
     # Training
-    learning_rate: float = 5e-5  # Reduced for stability
+    learning_rate: float = 1e-4
     weight_decay: float = 0.01
     loss_type: str = "composite"  # Always use composite loss with spatial awareness
     ranking_margin: float = 0.1

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -374,7 +374,7 @@ class RLearNPolicy(PreTrainedPolicy):
         # Align target with sampled timesteps
         if target.dim() == 1:
             target = target.unsqueeze(1)  # (B, 1)
-        
+
         # Handle target padding to match frame sequence if needed
         if target.shape[1] < self.config.max_seq_len:
             # Pad targets by repeating the first value (assuming it's the earliest)
@@ -382,10 +382,13 @@ class RLearNPolicy(PreTrainedPolicy):
             first_target = target[:, :1]  # (B, 1)
             padding = first_target.expand(target.shape[0], padding_needed)
             target = torch.cat([padding, target], dim=1)  # Prepend padding
-            
+
             import logging
-            logging.debug(f"Padded targets from {target.shape[1] - padding_needed} to {self.config.max_seq_len}")
-        
+
+            logging.debug(
+                f"Padded targets from {target.shape[1] - padding_needed} to {self.config.max_seq_len}"
+            )
+
         # Now safely index with idx
         target = target[:, idx]
 
@@ -617,12 +620,12 @@ def generate_causal_mask(T: int, device=None) -> Tensor:
 
 def extract_visual_sequence(batch: dict[str, Tensor], target_seq_len: int = None) -> Tensor:
     """Extract visual sequence from batch and ensure it has the expected temporal length.
-    
+
     Args:
         batch: Input batch containing image data
         target_seq_len: Expected sequence length. If provided and the actual sequence is shorter,
                        it will be padded by repeating the first frame.
-    
+
     Returns:
         Tensor of shape (B, T, C, H, W)
     """
@@ -670,7 +673,7 @@ def extract_visual_sequence(batch: dict[str, Tensor], target_seq_len: int = None
             f"Available keys with 'image': {image_like_keys}. "
             f"All keys: {available_keys}"
         )
-    
+
     # Pad sequence if needed
     if target_seq_len is not None:
         B, T, C, H, W = frames.shape
@@ -680,10 +683,11 @@ def extract_visual_sequence(batch: dict[str, Tensor], target_seq_len: int = None
             first_frame = frames[:, :1]  # (B, 1, C, H, W)
             padding = first_frame.expand(B, padding_needed, C, H, W)
             frames = torch.cat([padding, frames], dim=1)  # Prepend padding
-            
+
             import logging
+
             logging.debug(f"Padded sequence from {T} to {target_seq_len} frames by repeating first frame")
-    
+
     return frames
 
 

src/lerobot/policies/rlearn/rlearn_plan.md

@@ -128,11 +128,11 @@ Default weights: $\lambda_{\text{prog}}=1.0$, $\lambda_{\text{spatial-nce}}=0.5$
   - Only vlc loss then eval []
   - Vlc + rewind loss then eval []
 - Convert 1% of bc-z []
-- Cleanup code [] 
+- Cleanup code []
 - Try DINO v3 as encoder Base 86 M: https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m with HuggingFaceTB/SmolLM2-135M-Instruct ? []
 - Add more artificial text to dataset generated by vlm (google gemini) []
   - See google gemini vlm caption [] https://gemini.google.com/app/7e332ffaf32580f2
-  - Multiple captions per video, creat method to generate as much data as possible etc [] https://arxiv.org/abs/2508.13446
+  - Multiple captions per video, creat method to generate as much data as possible etc [] https://arxiv.org/abs/2508.13446, https://arxiv.org/pdf/2412.04453
 - How can we improve spatial aware learning? co generating captions for each frame with language decoder?
 - Add droid []
 - Extend evaluation []