change config

src/lerobot/policies/rlearn/configuration_rlearn.py

@@ -45,7 +45,7 @@ class RLearNConfig(PreTrainedConfig):
 
     # Sequence length, amount of past frames including current one to use in the temporal model
     max_seq_len: int = 16
-    # Temporal sampling stride (2 = skip every other frame for wider temporal coverage)
+    # Temporal sampling stride
     temporal_sampling_stride: int = 3 # Open x mostly has fps 10, and rewind has seq len 16, ours is 30fps so 30/10 = 3 stride lenght to have same timeframe!
 
     # Model dimensions and transformer
@@ -53,8 +53,7 @@ class RLearNConfig(PreTrainedConfig):
     num_layers: int = 4
     num_heads: int = 8
     ff_mult: int = 4  # Feed-forward multiplier, hidden = dim_model * ff_mult
-    dropout: float = 0.10
+    dropout: float = 0.05
-    num_register_tokens: int = 4
 
     # --- reward head options ---
     use_categorical_rewards: bool = False      # classification over bins
@@ -69,7 +68,7 @@ class RLearNConfig(PreTrainedConfig):
     frame_dropout_p: float = 0.10
 
     # Training
-    learning_rate: float = 1e-3
+    learning_rate: float = 5e-4
     weight_decay: float = 0.01
     head_lr_multiplier: float = 5.0
     logit_eps: float = 1e-4
@@ -79,9 +78,9 @@ class RLearNConfig(PreTrainedConfig):
     compile_model: bool = True
 
     # ReWiND augmentation
-    rewind_prob: float = 0.8
+    rewind_prob: float = 0.3 #0.8
-    rewind_last3_prob: float = 0.3
+    rewind_last3_prob: float = 0.0 #0.3
-    mismatch_prob: float = 0.2
+    mismatch_prob: float = 0.0# 0.2
     
     # Normalization presets
     normalization_mapping: dict[str, NormalizationMode] = field(

src/lerobot/policies/rlearn/eval_script.py

@@ -139,7 +139,7 @@ def extract_episode_frames_and_gt(dataset, episode_idx):
 
 
 @torch.no_grad()
-def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=64, device="cuda"):
+def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=64, device="cuda", temporal_stride: int | None = None):
     """
     Sliding-window prediction: for each frame i, create a window [max(0, i-L+1) .. i],
     left-pad by repeating the first frame to length L (<= 16), and take the prediction 
@@ -147,8 +147,13 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
     Returns np.ndarray of shape (T,).
     """
     T = frames.shape[0]
-    L = int(getattr(getattr(model, "config", object()), "max_seq_len", max_seq_len))
+    cfg = getattr(model, "config", object())
+    L = int(getattr(cfg, "max_seq_len", max_seq_len))
     L = min(L, max_seq_len)  # hard-cap at 16
+    # Use the same temporal stride as training (skip s-1 frames, take 1)
+    if temporal_stride is None:
+        temporal_stride = int(getattr(cfg, "temporal_sampling_stride", 1))
+    temporal_stride = max(1, int(temporal_stride))
 
     # Preprocessed tensor on device
     frames = frames.to(device)
@@ -158,20 +163,14 @@ def predict_rewards_sliding(model, frames, language, max_seq_len=16, batch_size=
     left_pad_counts = []  # Number of left-pad (OOB) frames per window
     
     for i in range(T):
-        start = max(0, i - L + 1)
+        # Build indices with stride s: [..., i-3, i] etc., left-padded by clamping to 0
-        window = frames[start : i + 1]  # (len<=L, C, H, W)
+        idxs = [i - (L - 1 - j) * temporal_stride for j in range(L)]
+        pad_needed = sum(1 for k in idxs if k < 0)
+        clamped = [0 if k < 0 else (T - 1 if k >= T else k) for k in idxs]
+        window = frames[clamped]  # (L, C, H, W)
 
-        if window.shape[0] < L:
+        # Use the last temporal position (current frame) for reading model output
-            pad_needed = L - window.shape[0]
+        frame_pos = L - 1
-            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
-        # Frames 0-15 use MLPs 0-15, frames 16-31 use MLPs 0-15 again, etc.
-        frame_pos = i % L  # Cycle through [0, 1, 2, ..., 15, 0, 1, 2, ..., 15, ...]
 
         windows.append(window)
         frame_positions.append(frame_pos)

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -509,7 +509,7 @@ class RLearNPolicy(PreTrainedPolicy):
                     L_mismatch = mismatch_loss_per_sample[mismatch_tensor].mean()
 
         # Total loss
-        total_loss = total_loss + L_mismatch + 0.3 * L_rank + 0.05 * L_flat
+        total_loss = total_loss + L_mismatch
         loss_time = time.perf_counter() - loss_start
         
         # DEBUG: Clean logit regression monitoring with full array printing
@@ -571,8 +571,7 @@ class RLearNPolicy(PreTrainedPolicy):
             "loss": float(total_loss.detach().item()),
             "loss_main": float(loss.detach().item() if isinstance(loss, torch.Tensor) else 0.0),
             "loss_mismatch": float(L_mismatch.detach().item()),
-            "loss_rank": float(L_rank.detach().item()),
+            
-            "loss_flat": float(L_flat.detach().item()),
             "t_eff": float(T_eff),
             "lang_len_mean": float(mask.sum().float().mean().item()), # Use mask to get actual lengths
             # Target statistics for monitoring