diff --git a/notebooks/rlearn_evaluation.ipynb b/notebooks/rlearn_evaluation.ipynb
index d361083d8..ec89f4899 100644
--- a/notebooks/rlearn_evaluation.ipynb
+++ b/notebooks/rlearn_evaluation.ipynb
@@ -103,17 +103,37 @@
      "output_type": "stream",
      "text": [
       "Using device: mps\n",
-      "Loading dataset...\n",
-      "Dataset loaded: 10 episodes, 1175 frames\n",
-      "Features: ['observation.images.image', 'observation.state', 'action', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index', 'next.reward']\n",
-      "FPS: 10\n"
+      "Loading dataset...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70313939b804493298a5577d4235b73c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset loaded: 10 episodes, 1581 frames\n",
+      "Features: ['action', 'observation.state', 'observation.images.front', 'timestamp', 'frame_index', 'episode_index', 'index', 'task_index']\n",
+      "FPS: 30\n"
      ]
     }
    ],
    "source": [
     "# Configuration\n",
-    "DATASET_REPO = \"pepijn223/rewards_bc_z3\"  # Change to your dataset\n",
-    "MODEL_PATH = \"pepijn223/rlearn_rewards_bcz_5000\"  # Change to your model checkpoint\n",
+    "DATASET_REPO = \"pepijn223/phone_pipeline_pickup1\"  # Change to your dataset\n",
+    "MODEL_PATH = \"pepijn223/rlearn_11\"  # Change to your model checkpoint\n",
     "DEVICE = \"cuda\" if torch.cuda.is_available() else \"mps\"\n",
     "NUM_EVAL_EPISODES = 10  # Number of episodes for evaluation\n",
     "\n",
@@ -142,13 +162,13 @@
      "output_type": "stream",
      "text": [
       "Setting up model...\n",
-      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_rewards_bcz_5000\n"
+      "Loading trained model from Hugging Face Hub: pepijn223/rlearn_11\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6993bb028af48d08bb59ec90979a37d",
+       "model_id": "5f42e50ec1da43a1813bd6e4e8757709",
        "version_major": 2,
        "version_minor": 0
       },
@@ -164,18 +184,27 @@
      "output_type": "stream",
      "text": [
       "WARNING:root:Device 'cuda' is not available. Switching to 'mps'.\n",
-      "WARNING:root:Device 'cuda' is not available. Switching to 'mps'.\n"
+      "WARNING:root:Automatic Mixed Precision (amp) is not available on device 'mps'. Deactivating AMP.\n",
+      "WARNING:root:Device 'cuda' is not available. Switching to 'mps'.\n",
+      "WARNING:root:Automatic Mixed Precision (amp) is not available on device 'mps'. Deactivating AMP.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Applied torch.compile to encoders and transformer\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5279f36eb0ca4685932c9e44d888050b",
+       "model_id": "f7b9e19062314e8aa8e0a6e51bbe404b",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]"
+       "model.safetensors:   0%|          | 0.00/3.08G [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -186,8 +215,8 @@
      "output_type": "stream",
      "text": [
       "✓ Model ready on mps\n",
-      "  Parameters: 139,442,560\n",
-      "  Trainable: 19,502,080\n"
+      "  Parameters: 770,074,628\n",
+      "  Trainable: 19,698,688\n"
      ]
     }
    ],
@@ -217,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -232,9 +261,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing VOC-S:   0%|          | 0/10 [00:00<?, ?it/s]objc[83562]: Class AVFFrameReceiver is implemented in both /Users/pepijnkooijmans/miniconda3/envs/lerobot/lib/python3.10/site-packages/av/.dylibs/libavdevice.61.3.100.dylib (0x15fac03a8) and /Users/pepijnkooijmans/miniconda3/envs/lerobot/lib/libavdevice.61.3.100.dylib (0x3088bc848). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
-      "objc[83562]: Class AVFAudioReceiver is implemented in both /Users/pepijnkooijmans/miniconda3/envs/lerobot/lib/python3.10/site-packages/av/.dylibs/libavdevice.61.3.100.dylib (0x15fac03f8) and /Users/pepijnkooijmans/miniconda3/envs/lerobot/lib/libavdevice.61.3.100.dylib (0x3088bc898). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
-      "Computing VOC-S:   0%|          | 0/10 [00:26<?, ?it/s]\n"
+      "Computing VOC-S:   0%|          | 0/10 [00:00<?, ?it/s]W0830 22:53:32.884000 40736 site-packages/torch/_dynamo/variables/builtin.py:861] [0/0] incorrect arg count <bound method BuiltinVariable.call_next of BuiltinVariable(next)> too many positional arguments and no constant handler\n",
+      "W0830 22:53:34.869000 40736 site-packages/torch/_inductor/utils.py:1250] [3/0_1] Not enough SMs to use max_autotune_gemm mode\n",
+      "Computing VOC-S:   0%|          | 0/10 [00:21<?, ?it/s]\n"
      ]
     },
     {
@@ -244,32 +273,29 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[5], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Run VOC-S evaluation\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning VOC-S evaluation...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m voc_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_voc_s\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EVAL_EPISODES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_interquartile_mean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVOC-S RESULTS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Run VOC-S evaluation\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRunning VOC-S evaluation...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m voc_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_voc_s\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mNUM_EVAL_EPISODES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_interquartile_mean\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVOC-S RESULTS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:437\u001b[0m, in \u001b[0;36mRLearnEvaluator.evaluate_voc_s\u001b[0;34m(self, dataset, num_episodes, use_interquartile_mean)\u001b[0m\n\u001b[1;32m    434\u001b[0m     frames_tensor \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mstack(frames)  \u001b[38;5;66;03m# (T, C, H, W)\u001b[39;00m\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;66;03m# Predict rewards\u001b[39;00m\n\u001b[0;32m--> 437\u001b[0m     episode_rewards \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_episode_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlanguage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    438\u001b[0m     predicted_rewards\u001b[38;5;241m.\u001b[39mappend(episode_rewards)\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/evaluation.py:300\u001b[0m, in \u001b[0;36mRLearnEvaluator.predict_episode_rewards\u001b[0;34m(self, frames, language, batch_size)\u001b[0m\n\u001b[1;32m    294\u001b[0m batch \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    295\u001b[0m     OBS_IMAGES: batch_sequences,  \u001b[38;5;66;03m# (B, T, C, H, W) format expected by model\u001b[39;00m\n\u001b[1;32m    296\u001b[0m     OBS_LANGUAGE: [language] \u001b[38;5;241m*\u001b[39m batch_sequences\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    297\u001b[0m }\n\u001b[1;32m    299\u001b[0m \u001b[38;5;66;03m# Predict rewards - model returns (B, T') but we want the last timestep for each sequence\u001b[39;00m\n\u001b[0;32m--> 300\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_rewards\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# (B, T')\u001b[39;00m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;66;03m# Take the last timestep prediction for each sequence (represents current frame reward)\u001b[39;00m\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m values\u001b[38;5;241m.\u001b[39mdim() \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:240\u001b[0m, in \u001b[0;36mRLearNPolicy.predict_rewards\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    237\u001b[0m frames \u001b[38;5;241m=\u001b[39m frames\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    239\u001b[0m \u001b[38;5;66;03m# Process video frames\u001b[39;00m\n\u001b[0;32m--> 240\u001b[0m video_embeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_encode_video_frames\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# (B, T, D_vision)\u001b[39;00m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;66;03m# Language embeddings\u001b[39;00m\n\u001b[1;32m    243\u001b[0m lang_embeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtext_encoder\u001b[38;5;241m.\u001b[39mencode(\n\u001b[1;32m    244\u001b[0m     commands,\n\u001b[1;32m    245\u001b[0m     output_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken_embeddings\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    246\u001b[0m     convert_to_tensor\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    247\u001b[0m     device\u001b[38;5;241m=\u001b[39mdevice\n\u001b[1;32m    248\u001b[0m )\n",
-      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:318\u001b[0m, in \u001b[0;36mRLearNPolicy._encode_video_frames\u001b[0;34m(self, frames)\u001b[0m\n\u001b[1;32m    316\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvision_processor(images\u001b[38;5;241m=\u001b[39mimages_list, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    317\u001b[0m pixel_values \u001b[38;5;241m=\u001b[39m processed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvision_encoder\u001b[38;5;241m.\u001b[39mparameters())\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 318\u001b[0m vision_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[38;5;66;03m# Extract CLS tokens\u001b[39;00m\n\u001b[1;32m    321\u001b[0m cls_tokens \u001b[38;5;241m=\u001b[39m vision_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# (BT, D_vision)\u001b[39;00m\n",
+      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:263\u001b[0m, in \u001b[0;36mRLearNPolicy.predict_rewards\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    260\u001b[0m frames \u001b[38;5;241m=\u001b[39m frames\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    262\u001b[0m \u001b[38;5;66;03m# Process video frames\u001b[39;00m\n\u001b[0;32m--> 263\u001b[0m video_embeds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_encode_video_frames\u001b[49m\u001b[43m(\u001b[49m\u001b[43mframes\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mto(device)  \u001b[38;5;66;03m# (B, T, D_vision)\u001b[39;00m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# Language embeddings + mask\u001b[39;00m\n\u001b[1;32m    266\u001b[0m lang_embeds, mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_encode_language_tokens(commands, device)\n",
+      "File \u001b[0;32m~/Documents/GitHub/lerobot/src/lerobot/policies/rlearn/modeling_rlearn.py:343\u001b[0m, in \u001b[0;36mRLearNPolicy._encode_video_frames\u001b[0;34m(self, frames)\u001b[0m\n\u001b[1;32m    340\u001b[0m     pixel_values \u001b[38;5;241m=\u001b[39m processed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpixel_values\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    342\u001b[0m \u001b[38;5;66;03m# Process in batch through vision model\u001b[39;00m\n\u001b[0;32m--> 343\u001b[0m vision_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvision_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpixel_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpixel_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m cls_tokens \u001b[38;5;241m=\u001b[39m vision_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state[:, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m rearrange(cls_tokens, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(b t) d -> b t d\u001b[39m\u001b[38;5;124m'\u001b[39m, b\u001b[38;5;241m=\u001b[39mB, t\u001b[38;5;241m=\u001b[39mT)\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/dinov2/modeling_dinov2.py:594\u001b[0m, in \u001b[0;36mDinov2Model.forward\u001b[0;34m(self, pixel_values, bool_masked_pos, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    590\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[1;32m    592\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values, bool_masked_pos\u001b[38;5;241m=\u001b[39mbool_masked_pos)\n\u001b[0;32m--> 594\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    600\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    601\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    602\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayernorm(sequence_output)\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:959\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dict_passed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    958\u001b[0m     return_dict \u001b[38;5;241m=\u001b[39m return_dict_passed\n\u001b[0;32m--> 959\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    961\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:763\u001b[0m, in \u001b[0;36mSiglipVisionTransformer.forward\u001b[0;34m(self, pixel_values, output_attentions, output_hidden_states, interpolate_pos_encoding)\u001b[0m\n\u001b[1;32m    757\u001b[0m output_hidden_states \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    758\u001b[0m     output_hidden_states \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39moutput_hidden_states\n\u001b[1;32m    759\u001b[0m )\n\u001b[1;32m    761\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(pixel_values, interpolate_pos_encoding\u001b[38;5;241m=\u001b[39minterpolate_pos_encoding)\n\u001b[0;32m--> 763\u001b[0m encoder_outputs: BaseModelOutput \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    764\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    765\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    766\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    767\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    769\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m encoder_outputs\u001b[38;5;241m.\u001b[39mlast_hidden_state\n\u001b[1;32m    770\u001b[0m last_hidden_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpost_layernorm(last_hidden_state)\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/dinov2/modeling_dinov2.py:473\u001b[0m, in \u001b[0;36mDinov2Encoder.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    469\u001b[0m     all_hidden_states \u001b[38;5;241m=\u001b[39m all_hidden_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[1;32m    471\u001b[0m layer_head_mask \u001b[38;5;241m=\u001b[39m head_mask[i] \u001b[38;5;28;01mif\u001b[39;00m head_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 473\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    475\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    477\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/utils/generic.py:959\u001b[0m, in \u001b[0;36mcan_return_tuple.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_dict_passed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    958\u001b[0m     return_dict \u001b[38;5;241m=\u001b[39m return_dict_passed\n\u001b[0;32m--> 959\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    961\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39mto_tuple()\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:594\u001b[0m, in \u001b[0;36mSiglipEncoder.forward\u001b[0;34m(self, inputs_embeds, attention_mask, output_attentions, output_hidden_states)\u001b[0m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states:\n\u001b[1;32m    592\u001b[0m     encoder_states \u001b[38;5;241m=\u001b[39m encoder_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[0;32m--> 594\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mencoder_layer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    595\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    596\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    597\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    598\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    600\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    602\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/modeling_layers.py:93\u001b[0m, in \u001b[0;36mGradientCheckpointingLayer.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     90\u001b[0m         logger\u001b[38;5;241m.\u001b[39mwarning(message)\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(partial(\u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs), \u001b[38;5;241m*\u001b[39margs)\n\u001b[0;32m---> 93\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/dinov2/modeling_dinov2.py:422\u001b[0m, in \u001b[0;36mDinov2Layer.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    417\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    418\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[1;32m    419\u001b[0m     head_mask: Optional[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    420\u001b[0m     output_attentions: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    421\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor], \u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[0;32m--> 422\u001b[0m     self_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    423\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnorm1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# in Dinov2, layernorm is applied before self-attention\u001b[39;49;00m\n\u001b[1;32m    424\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    425\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    426\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    427\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    429\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer_scale1(attention_output)\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/siglip/modeling_siglip.py:458\u001b[0m, in \u001b[0;36mSiglipEncoderLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m    455\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n\u001b[1;32m    457\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[0;32m--> 458\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    459\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmlp(hidden_states)\n\u001b[1;32m    460\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m hidden_states\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/dinov2/modeling_dinov2.py:308\u001b[0m, in \u001b[0;36mDinov2Attention.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    303\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    304\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[1;32m    305\u001b[0m     head_mask: Optional[torch\u001b[38;5;241m.\u001b[39mTensor] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    306\u001b[0m     output_attentions: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    307\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor], \u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor]]:\n\u001b[0;32m--> 308\u001b[0m     self_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    310\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(self_outputs[\u001b[38;5;241m0\u001b[39m], hidden_states)\n\u001b[1;32m    312\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (attention_output,) \u001b[38;5;241m+\u001b[39m self_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/transformers/models/dinov2/modeling_dinov2.py:218\u001b[0m, in \u001b[0;36mDinov2SelfAttention.forward\u001b[0;34m(self, hidden_states, head_mask, output_attentions)\u001b[0m\n\u001b[1;32m    211\u001b[0m batch_size, seq_length, _ \u001b[38;5;241m=\u001b[39m hidden_states\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m    212\u001b[0m key_layer \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkey(hidden_states)\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;241m.\u001b[39mview(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattention_head_size)\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    216\u001b[0m )\n\u001b[1;32m    217\u001b[0m value_layer \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 218\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;241m.\u001b[39mview(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattention_head_size)\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    221\u001b[0m )\n\u001b[1;32m    222\u001b[0m query_layer \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    223\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery(hidden_states)\n\u001b[1;32m    224\u001b[0m     \u001b[38;5;241m.\u001b[39mview(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_attention_heads, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattention_head_size)\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m    226\u001b[0m )\n\u001b[1;32m    228\u001b[0m attention_interface: Callable \u001b[38;5;241m=\u001b[39m eager_attention_forward\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1751\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1749\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1750\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1751\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py:1762\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1757\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1758\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1760\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1761\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1762\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1764\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1765\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
-      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/linear.py:125\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/normalization.py:217\u001b[0m, in \u001b[0;36mLayerNorm.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 217\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    218\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\n\u001b[1;32m    219\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/functional.py:2910\u001b[0m, in \u001b[0;36mlayer_norm\u001b[0;34m(input, normalized_shape, weight, bias, eps)\u001b[0m\n\u001b[1;32m   2900\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, weight, bias):\n\u001b[1;32m   2901\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m   2902\u001b[0m         layer_norm,\n\u001b[1;32m   2903\u001b[0m         (\u001b[38;5;28minput\u001b[39m, weight, bias),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2908\u001b[0m         eps\u001b[38;5;241m=\u001b[39meps,\n\u001b[1;32m   2909\u001b[0m     )\n\u001b[0;32m-> 2910\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2911\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnormalized_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\n\u001b[1;32m   2912\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
@@ -303,19 +329,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Visualizing first 2 episodes...\n"
+      "Visualizing first 4 episodes...\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x1200 with 4 Axes>"
       ]
@@ -331,32 +357,32 @@
       "Detailed Episode Statistics:\n",
       "================================================================================\n",
       "Episode 0:\n",
-      "  Language: place the white sponge in the ceramic bowl\n",
-      "  Frames: 113\n",
-      "  VOC-S (Spearman ρ): 0.3486 (p=0.0002)\n",
-      "  Reward range: [0.078, 0.266]\n",
-      "  Trend: ↗ Increasing\n",
+      "  Language: Pickup the blue block\n",
+      "  Frames: 128\n",
+      "  VOC-S (Spearman ρ): 0.0849 (p=0.3407)\n",
+      "  Reward range: [0.806, 0.907]\n",
+      "  Trend: → Flat\n",
       "--------------------------------------------------------------------------------\n",
       "Episode 1:\n",
-      "  Language: move the arm in a circular motion\n",
+      "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): 0.6721 (p=0.0000)\n",
-      "  Reward range: [0.085, 0.858]\n",
-      "  Trend: ↗ Increasing\n",
+      "  VOC-S (Spearman ρ): -0.0830 (p=0.3519)\n",
+      "  Reward range: [0.814, 0.902]\n",
+      "  Trend: → Flat\n",
       "--------------------------------------------------------------------------------\n",
       "Episode 2:\n",
-      "  Language: place the bottle in the ceramic bowl\n",
+      "  Language: Pickup the blue block\n",
       "  Frames: 128\n",
-      "  VOC-S (Spearman ρ): 0.6220 (p=0.0000)\n",
-      "  Reward range: [0.085, 0.827]\n",
-      "  Trend: ↗ Increasing\n",
+      "  VOC-S (Spearman ρ): 0.0751 (p=0.3997)\n",
+      "  Reward range: [0.815, 0.914]\n",
+      "  Trend: → Flat\n",
       "--------------------------------------------------------------------------------\n",
-      "Episode 3:\n",
-      "  Language: drag the ceramic bowl in a circle\n",
-      "  Frames: 2\n",
-      "  VOC-S (Spearman ρ): 1.0000 (p=nan)\n",
-      "  Reward range: [0.081, 0.081]\n",
-      "  Trend: ↗ Increasing\n",
+      "Episode 4:\n",
+      "  Language: Pickup the blue block\n",
+      "  Frames: 128\n",
+      "  VOC-S (Spearman ρ): -0.0371 (p=0.6778)\n",
+      "  Reward range: [0.862, 0.908]\n",
+      "  Trend: → Flat\n",
       "--------------------------------------------------------------------------------\n"
      ]
     }
@@ -592,7 +618,7 @@
     "\n",
     "# Visualize multiple episodes at once\n",
     "print(\"Visualizing first 4 episodes...\")\n",
-    "visualize_multiple_episodes([0, 1, 2, 3], max_frames=128)"
+    "visualize_multiple_episodes([0, 1, 2, 4], max_frames=128)"
    ]
   },
   {
diff --git a/src/lerobot/policies/rlearn/configuration_rlearn.py b/src/lerobot/policies/rlearn/configuration_rlearn.py
index 64dc974e0..48f3db656 100644
--- a/src/lerobot/policies/rlearn/configuration_rlearn.py
+++ b/src/lerobot/policies/rlearn/configuration_rlearn.py
@@ -61,7 +61,7 @@ class RLearNConfig(PreTrainedConfig):
     use_tanh_head: bool = False  # when True, bound outputs in [-1, 1]
 
     # Training
-    learning_rate: float = 1e-4
+    learning_rate: float = 1e-3
     weight_decay: float = 0.01
     
     # Performance optimizations
@@ -98,6 +98,12 @@ class RLearNConfig(PreTrainedConfig):
     reward_max_value: float = 1.0
     reward_hl_gauss_loss_num_bins: int = 20
     
+    # Evaluation visualization parameters
+    enable_eval_visualizations: bool = False  # Enable reward evaluation visualizations during training
+    eval_visualization_freq: int = 1000  # Steps between evaluation visualizations
+    eval_holdout_episodes: int = 9  # Number of episodes to hold out for evaluation
+    eval_max_frames: int = 128  # Maximum frames per episode for evaluation
+    eval_visualization_seed: int = 42  # Seed for reproducible episode selection
 
     # Optional: path to episodes.jsonl to build full-episode indices automatically
     # Default to common dataset layout: <dataset_root>/meta/episodes.jsonl
diff --git a/src/lerobot/policies/rlearn/eval_visualizer.py b/src/lerobot/policies/rlearn/eval_visualizer.py
new file mode 100644
index 000000000..b539af160
--- /dev/null
+++ b/src/lerobot/policies/rlearn/eval_visualizer.py
@@ -0,0 +1,511 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Visualization utilities for RLearN evaluation during training.
+
+Creates and saves reward prediction visualizations for held-out episodes.
+"""
+
+from __future__ import annotations
+
+import warnings
+from pathlib import Path
+from typing import Any
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from matplotlib import rcParams
+from scipy.stats import spearmanr
+from torch import Tensor
+
+from lerobot.constants import OBS_IMAGES, OBS_LANGUAGE
+
+# Set matplotlib backend to avoid GUI issues during training
+rcParams['backend'] = 'Agg'
+
+
+class RLearNEvalVisualizer:
+    """
+    Creates visualization plots for RLearN model evaluation during training.
+    
+    Generates reward prediction plots similar to the evaluation notebook but saves
+    them as images for monitoring training progress.
+    """
+    
+    def __init__(self, model, dataset, device: str = "cuda"):
+        """
+        Args:
+            model: RLearN model instance
+            dataset: LeRobot dataset instance
+            device: Device to run evaluation on
+        """
+        self.model = model
+        self.dataset = dataset
+        self.device = device
+        
+    def get_episode_data(self, episode_idx: int, max_frames: int = 64) -> tuple[Tensor | None, str | None, np.ndarray | None, int | None]:
+        """Extract frames, language, and predict rewards for an episode."""
+        try:
+            # Get episode data
+            ep_start = self.dataset.episode_data_index["from"][episode_idx].item()
+            ep_end = self.dataset.episode_data_index["to"][episode_idx].item()
+            episode_length = min(ep_end - ep_start, max_frames)
+
+            # Collect frames and get language
+            frames = []
+            language = None
+
+            for frame_idx in range(episode_length):
+                global_idx = ep_start + frame_idx
+                frame_data = self.dataset[global_idx]
+
+                # Extract image
+                if OBS_IMAGES in frame_data:
+                    img = frame_data[OBS_IMAGES]
+                else:
+                    img_keys = [k for k in frame_data.keys() if "image" in k.lower()]
+                    if img_keys:
+                        img = frame_data[img_keys[0]]
+                    else:
+                        continue
+
+                if isinstance(img, np.ndarray):
+                    img = torch.from_numpy(img)
+
+                # Ensure CHW format
+                if len(img.shape) == 3 and img.shape[-1] in [1, 3, 4]:
+                    img = img.permute(2, 0, 1)
+
+                # Resize to expected input size (224x224 for SigLIP2)
+                if img.shape[-2:] != (224, 224):
+                    import torch.nn.functional as F
+                    img = F.interpolate(
+                        img.unsqueeze(0), size=(224, 224), mode="bilinear", align_corners=False
+                    ).squeeze(0)
+
+                # Normalize to [0, 1] if needed
+                if img.dtype == torch.uint8:
+                    img = img.float() / 255.0
+
+                frames.append(img)
+
+                # Get language
+                if language is None:
+                    if OBS_LANGUAGE in frame_data:
+                        language = frame_data[OBS_LANGUAGE]
+                        if isinstance(language, list):
+                            language = language[0]
+                    elif "task" in frame_data:
+                        language = frame_data["task"]
+                    else:
+                        language = "No language provided"
+
+            if not frames:
+                return None, None, None, None
+
+            frames_tensor = torch.stack(frames)
+            
+            # Predict rewards using the model's evaluation method
+            with torch.no_grad():
+                self.model.eval()
+                rewards = self._predict_episode_rewards(frames_tensor, language)
+
+            return frames_tensor, language, rewards, episode_length
+
+        except Exception as e:
+            warnings.warn(f"Error processing episode {episode_idx}: {e}")
+            return None, None, None, None
+
+    @torch.no_grad()
+    def _predict_episode_rewards(self, frames: Tensor, language: str, batch_size: int = 16) -> np.ndarray:
+        """
+        Predict rewards for a single episode using proper temporal sequences.
+        
+        Args:
+            frames: Video frames tensor of shape (T, C, H, W)
+            language: Language instruction string
+            batch_size: Maximum number of temporal sequences to process at once
+
+        Returns:
+            Predicted progress/rewards array of shape (T,)
+        """
+        T = frames.shape[0]
+        max_seq_len = self.model.config.max_seq_len
+
+        # Create temporal sequences for each frame
+        temporal_sequences = []
+
+        for i in range(T):
+            # Create sequence ending at frame i
+            seq_frames = []
+            for j in range(max(0, i - max_seq_len + 1), i + 1):
+                # Use frame j if available, otherwise repeat the first available frame
+                frame_idx = max(0, min(j, T - 1))
+                seq_frames.append(frames[frame_idx])
+
+            # Pad sequence to max_seq_len by repeating the first frame if needed
+            while len(seq_frames) < max_seq_len:
+                seq_frames.insert(0, seq_frames[0])  # Prepend first frame
+
+            # Take only the last max_seq_len frames if we have too many
+            seq_frames = seq_frames[-max_seq_len:]
+            temporal_sequences.append(torch.stack(seq_frames))  # (max_seq_len, C, H, W)
+
+        # Stack all temporal sequences: (T, max_seq_len, C, H, W)
+        all_sequences = torch.stack(temporal_sequences)
+
+        # Process in batches
+        rewards = []
+        for i in range(0, T, batch_size):
+            end_idx = min(i + batch_size, T)
+            batch_sequences = all_sequences[i:end_idx].to(self.device)  # (B, max_seq_len, C, H, W)
+
+            # Create batch for model
+            batch = {
+                OBS_IMAGES: batch_sequences,  # (B, T, C, H, W) format expected by model
+                OBS_LANGUAGE: [language] * batch_sequences.shape[0],
+            }
+
+            # Predict rewards - model returns (B, T') but we want the last timestep for each sequence
+            values = self.model.predict_rewards(batch)  # (B, T')
+
+            # Take the last timestep prediction for each sequence (represents current frame reward)
+            if values.dim() == 2:
+                batch_rewards = values[:, -1].cpu().numpy()  # (B,) - last timestep
+            else:
+                batch_rewards = values.cpu().numpy()  # (B,) - already single timestep
+
+            rewards.extend(batch_rewards)
+
+        return np.array(rewards[:T])  # Ensure exact length
+
+    def create_episode_grid_visualization(
+        self, 
+        episode_indices: list[int], 
+        save_path: Path, 
+        step: int | None = None,
+        max_frames: int = 64
+    ) -> dict[str, Any]:
+        """
+        Create a 3x3 grid visualization of episode reward predictions.
+        
+        Args:
+            episode_indices: List of 9 episode indices to visualize
+            save_path: Path to save the visualization image
+            step: Training step (for title)
+            max_frames: Maximum frames per episode to process
+            
+        Returns:
+            Dictionary with evaluation metrics
+        """
+        if len(episode_indices) != 9:
+            raise ValueError("Expected exactly 9 episode indices for 3x3 grid")
+            
+        # Create figure with 3x3 subplots
+        fig, axes = plt.subplots(3, 3, figsize=(20, 16))
+        axes = axes.flatten()
+        
+        eval_metrics = {
+            "voc_s_scores": [],
+            "episode_lengths": [],
+            "reward_ranges": [],
+            "languages": []
+        }
+        
+        for i, episode_idx in enumerate(episode_indices):
+            ax = axes[i]
+            
+            frames, language, rewards, episode_length = self.get_episode_data(episode_idx, max_frames)
+            
+            if rewards is None:
+                ax.text(
+                    0.5, 0.5, f"Episode {episode_idx}\nNo data available",
+                    ha="center", va="center", transform=ax.transAxes,
+                    fontsize=12, bbox=dict(boxstyle="round,pad=0.3", facecolor="lightcoral", alpha=0.7)
+                )
+                ax.set_title(f"Episode {episode_idx} - Error", fontsize=12, pad=10)
+                continue
+                
+            # Plot predicted rewards
+            time_steps = range(len(rewards))
+            ax.plot(
+                time_steps, rewards, "b-", linewidth=2.5, marker="o", markersize=5, 
+                label="Predicted Reward", alpha=0.8
+            )
+            
+            # Add expected progress line (ground truth for ReWiND)
+            expected_progress = np.linspace(0, 1, len(rewards))
+            ax.plot(
+                time_steps, expected_progress, "orange", linestyle="--", linewidth=2.5, 
+                label="Expected Progress (0→1)", alpha=0.8
+            )
+            
+            # Compute VOC-S (Value-Order Correlation for Success)
+            frame_indices = np.arange(1, len(rewards) + 1)
+            correlation, p_value = spearmanr(frame_indices, rewards)
+            if np.isnan(correlation):
+                correlation = 0.0
+                
+            eval_metrics["voc_s_scores"].append(correlation)
+            eval_metrics["episode_lengths"].append(len(rewards))
+            eval_metrics["reward_ranges"].append((rewards.min(), rewards.max()))
+            eval_metrics["languages"].append(language)
+            
+            # Format title with language (truncated) and VOC-S
+            title_lang = language[:35] + "..." if len(language) > 35 else language
+            title = f'Episode {episode_idx}\n"{title_lang}"\nVOC-S: {correlation:.3f}'
+            ax.set_title(title, fontsize=10, pad=15)
+            
+            ax.set_xlabel("Frame Index", fontsize=10)
+            ax.set_ylabel("Reward", fontsize=10)
+            ax.legend(fontsize=8, loc='upper left')
+            ax.grid(True, alpha=0.3)
+            
+            # Color-coded trend indicator
+            if correlation > 0.3:
+                trend_text = "↗ Strong+"
+                trend_color = "darkgreen"
+            elif correlation > 0.1:
+                trend_text = "↗ Weak+"
+                trend_color = "green"
+            elif correlation < -0.3:
+                trend_text = "↘ Strong-"
+                trend_color = "darkred"
+            elif correlation < -0.1:
+                trend_text = "↘ Weak-"
+                trend_color = "red"
+            else:
+                trend_text = "→ Flat"
+                trend_color = "gray"
+                
+            ax.text(
+                0.02, 0.98, trend_text, transform=ax.transAxes,
+                verticalalignment="top", fontsize=9, fontweight="bold",
+                bbox=dict(boxstyle="round,pad=0.3", facecolor=trend_color, alpha=0.2),
+                color=trend_color
+            )
+            
+            # Add reward range info
+            ax.text(
+                0.98, 0.02, f"Range: [{rewards.min():.3f}, {rewards.max():.3f}]",
+                transform=ax.transAxes, ha="right", va="bottom", fontsize=8,
+                bbox=dict(boxstyle="round,pad=0.2", facecolor="lightblue", alpha=0.5)
+            )
+        
+        # Add overall title
+        step_text = f" - Step {step}" if step is not None else ""
+        fig.suptitle(
+            f"RLearN Reward Evaluation{step_text}\n"
+            f"Mean VOC-S: {np.mean(eval_metrics['voc_s_scores']):.3f} | "
+            f"Episodes: {len([s for s in eval_metrics['voc_s_scores'] if s != 0])}/9",
+            fontsize=16, y=0.95
+        )
+        
+        plt.tight_layout()
+        plt.subplots_adjust(top=0.90)  # Make room for suptitle
+        
+        # Save the figure
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(save_path, dpi=150, bbox_inches='tight', facecolor='white')
+        plt.close()  # Close to free memory
+        
+        # Calculate summary metrics
+        valid_scores = [s for s in eval_metrics["voc_s_scores"] if s != 0]
+        summary = {
+            "mean_voc_s": np.mean(valid_scores) if valid_scores else 0.0,
+            "std_voc_s": np.std(valid_scores) if valid_scores else 0.0,
+            "num_valid_episodes": len(valid_scores),
+            "total_episodes": len(episode_indices),
+            "mean_episode_length": np.mean(eval_metrics["episode_lengths"]) if eval_metrics["episode_lengths"] else 0,
+            "individual_scores": eval_metrics["voc_s_scores"],
+            "episode_languages": eval_metrics["languages"]
+        }
+        
+        return summary
+        
+    def create_comparison_visualization(
+        self,
+        episode_indices: list[int],
+        save_path: Path,
+        step: int | None = None,
+        max_frames: int = 64,
+        mismatch_templates: list[str] | None = None
+    ) -> dict[str, Any]:
+        """
+        Create correct vs incorrect language comparison visualization.
+        
+        Args:
+            episode_indices: List of episode indices to compare (up to 6)
+            save_path: Path to save the visualization image  
+            step: Training step (for title)
+            max_frames: Maximum frames per episode to process
+            mismatch_templates: Custom mismatch templates
+            
+        Returns:
+            Dictionary with detection metrics
+        """
+        if mismatch_templates is None:
+            mismatch_templates = [
+                "kick the ball", "clean the sink", "dance in place", 
+                "wave your hand", "jump up and down", "do nothing"
+            ]
+            
+        # Limit to 6 episodes for 2x3 grid
+        episode_indices = episode_indices[:6]
+        n_episodes = len(episode_indices)
+        
+        # Create figure with 2x3 subplots
+        fig, axes = plt.subplots(2, 3, figsize=(18, 12))
+        axes = axes.flatten()
+        
+        detection_results = {
+            "correct_finals": [],
+            "incorrect_finals": [],
+            "detection_successes": [],
+            "episode_info": []
+        }
+        
+        for i, episode_idx in enumerate(episode_indices):
+            if i >= 6:  # Limit to 6 subplots
+                break
+                
+            ax = axes[i]
+            
+            # Get episode data with correct language
+            frames, correct_language, correct_rewards, episode_length = self.get_episode_data(episode_idx, max_frames)
+            
+            if correct_rewards is None:
+                ax.text(
+                    0.5, 0.5, f"Episode {episode_idx}\nNo data available",
+                    ha="center", va="center", transform=ax.transAxes
+                )
+                ax.set_title(f"Episode {episode_idx} - Error")
+                continue
+                
+            # Generate incorrect language and predict
+            incorrect_language = mismatch_templates[i % len(mismatch_templates)]
+            incorrect_rewards = self._predict_episode_rewards(frames, incorrect_language)
+            
+            # Plot both reward curves
+            time_steps = range(len(correct_rewards))
+            ax.plot(
+                time_steps, correct_rewards, "g-", linewidth=2.5, marker="o", markersize=4,
+                label=f"Correct: '{correct_language[:25]}...'" if len(correct_language) > 25 else f"Correct: '{correct_language}'"
+            )
+            ax.plot(
+                time_steps, incorrect_rewards, "r-", linewidth=2.5, marker="s", markersize=4,
+                label=f"Incorrect: '{incorrect_language}'"
+            )
+            
+            # Calculate detection success
+            final_correct = correct_rewards[-1]
+            final_incorrect = incorrect_rewards[-1]
+            detection_success = final_correct > final_incorrect
+            
+            detection_results["correct_finals"].append(final_correct)
+            detection_results["incorrect_finals"].append(final_incorrect)
+            detection_results["detection_successes"].append(detection_success)
+            detection_results["episode_info"].append({
+                "episode_idx": episode_idx,
+                "correct_language": correct_language,
+                "incorrect_language": incorrect_language,
+                "final_correct": final_correct,
+                "final_incorrect": final_incorrect
+            })
+            
+            # Color-coded title based on detection success
+            success_indicator = "✓" if detection_success else "✗"
+            title_color = "darkgreen" if detection_success else "darkred"
+            ax.set_title(
+                f"Episode {episode_idx} {success_indicator}\nΔ: {final_correct - final_incorrect:.3f}",
+                color=title_color, fontweight="bold", fontsize=11
+            )
+            
+            ax.set_xlabel("Frame Index")
+            ax.set_ylabel("Reward")
+            ax.legend(fontsize=8, loc='upper left')
+            ax.grid(True, alpha=0.3)
+            
+            # Add final reward values as text
+            ax.text(
+                0.98, 0.02, 
+                f"Final: C={final_correct:.3f}, I={final_incorrect:.3f}",
+                transform=ax.transAxes, ha="right", va="bottom", fontsize=9,
+                bbox=dict(boxstyle="round,pad=0.3", facecolor="lightyellow", alpha=0.7)
+            )
+        
+        # Hide unused subplots
+        for i in range(n_episodes, 6):
+            axes[i].axis('off')
+        
+        # Calculate summary metrics
+        detection_accuracy = np.mean(detection_results["detection_successes"]) if detection_results["detection_successes"] else 0.0
+        mean_correct = np.mean(detection_results["correct_finals"]) if detection_results["correct_finals"] else 0.0
+        mean_incorrect = np.mean(detection_results["incorrect_finals"]) if detection_results["incorrect_finals"] else 0.0
+        
+        # Add overall title
+        step_text = f" - Step {step}" if step is not None else ""
+        fig.suptitle(
+            f"RLearN Language Detection{step_text}\n"
+            f"Accuracy: {detection_accuracy:.1%} | Mean Δ: {mean_correct - mean_incorrect:.3f} | "
+            f"Success: {sum(detection_results['detection_successes'])}/{len(detection_results['detection_successes'])}",
+            fontsize=16, y=0.95
+        )
+        
+        plt.tight_layout()
+        plt.subplots_adjust(top=0.90)
+        
+        # Save the figure
+        save_path.parent.mkdir(parents=True, exist_ok=True)
+        plt.savefig(save_path, dpi=150, bbox_inches='tight', facecolor='white')
+        plt.close()
+        
+        summary = {
+            "detection_accuracy": detection_accuracy,
+            "mean_correct_final": mean_correct,
+            "mean_incorrect_final": mean_incorrect,
+            "separation_score": mean_correct - mean_incorrect,
+            "num_episodes": len(detection_results["detection_successes"]),
+            "individual_results": detection_results["episode_info"]
+        }
+        
+        return summary
+
+
+def select_evaluation_episodes(dataset, num_episodes: int = 9, seed: int = 42) -> list[int]:
+    """
+    Select a diverse set of episodes for evaluation holdout.
+    
+    Args:
+        dataset: LeRobot dataset instance
+        num_episodes: Number of episodes to select
+        seed: Random seed for reproducibility
+        
+    Returns:
+        List of episode indices
+    """
+    np.random.seed(seed)
+    
+    total_episodes = dataset.num_episodes
+    if num_episodes >= total_episodes:
+        return list(range(total_episodes))
+    
+    # Select random episodes
+    episode_indices = np.random.choice(total_episodes, num_episodes, replace=False).tolist()
+    
+    return sorted(episode_indices)
diff --git a/src/lerobot/policies/rlearn/modeling_rlearn.py b/src/lerobot/policies/rlearn/modeling_rlearn.py
index 8715cb9b6..9086d12a5 100644
--- a/src/lerobot/policies/rlearn/modeling_rlearn.py
+++ b/src/lerobot/policies/rlearn/modeling_rlearn.py
@@ -556,6 +556,20 @@ class RLearNPolicy(PreTrainedPolicy):
         total_loss = loss + L_mismatch
         loss_time = time.perf_counter() - loss_start
         
+        # 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():
+                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"Pred range: [{preds.min():.3f}, {preds.max():.3f}]") 
+                print(f"Pred mean: {preds.mean():.3f}")
+                print(f"Loss: {loss:.4f}")
+                print("First sample targets:", target[0, :5].cpu().numpy())
+                print("First sample preds:", preds[0, :5].cpu().numpy())
+                print("="*25)
+        
         total_forward_time = time.perf_counter() - forward_start
 
         # Log individual loss components
diff --git a/src/lerobot/scripts/train.py b/src/lerobot/scripts/train.py
index 60f66379a..7e4dfaece 100644
--- a/src/lerobot/scripts/train.py
+++ b/src/lerobot/scripts/train.py
@@ -212,6 +212,28 @@ def train(cfg: TrainPipelineConfig):
         ds_meta=dataset.meta,
         episode_data_index=episode_data_index,
     )
+    
+    # Setup RLearN evaluation visualizations if enabled
+    eval_visualizer = None
+    eval_holdout_episodes = None
+    if (getattr(cfg.policy, "type", None) == "rlearn" and 
+        getattr(cfg.policy, "enable_eval_visualizations", False)):
+        
+        try:
+            from lerobot.policies.rlearn.eval_visualizer import RLearNEvalVisualizer, select_evaluation_episodes
+            
+            logging.info("Setting up RLearN evaluation visualizations")
+            eval_visualizer = RLearNEvalVisualizer(policy, dataset, device=str(device))
+            eval_holdout_episodes = select_evaluation_episodes(
+                dataset, 
+                num_episodes=getattr(cfg.policy, "eval_holdout_episodes", 9),
+                seed=getattr(cfg.policy, "eval_visualization_seed", 42)
+            )
+            logging.info(f"Selected {len(eval_holdout_episodes)} holdout episodes for evaluation: {eval_holdout_episodes}")
+        except ImportError as e:
+            logging.warning(f"Could not setup RLearN evaluation visualizations: {e}")
+            eval_visualizer = None
+    
     preprocessor, postprocessor = make_processor(
         policy_cfg=cfg.policy, pretrained_path=cfg.policy.pretrained_path, dataset_stats=dataset.meta.stats
     )
@@ -386,6 +408,8 @@ def train(cfg: TrainPipelineConfig):
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
         is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
+        is_eval_viz_step = (eval_visualizer is not None and 
+                           step % getattr(cfg.policy, "eval_visualization_freq", 1000) == 0)
 
         if is_log_step:
             logging.info(train_tracker)
@@ -437,6 +461,87 @@ def train(cfg: TrainPipelineConfig):
                 wandb_logger.log_dict(wandb_log_dict, step, mode="eval")
                 wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
 
+        # RLearN evaluation visualizations
+        if is_eval_viz_step:
+            logging.info(f"Creating RLearN evaluation visualizations at step {step}")
+            try:
+                with torch.no_grad():
+                    policy.eval()
+                    
+                    # Create evaluation visualizations directory
+                    eval_viz_dir = cfg.output_dir / "eval_visualizations"
+                    eval_viz_dir.mkdir(parents=True, exist_ok=True)
+                    
+                    # Create reward prediction visualization (3x3 grid)
+                    reward_viz_path = eval_viz_dir / f"reward_predictions_step_{step:06d}.png"
+                    reward_metrics = eval_visualizer.create_episode_grid_visualization(
+                        episode_indices=eval_holdout_episodes,
+                        save_path=reward_viz_path,
+                        step=step,
+                        max_frames=getattr(cfg.policy, "eval_max_frames", 128)
+                    )
+                    
+                    # Log metrics
+                    eval_viz_metrics = {
+                        "eval_viz/mean_voc_s": reward_metrics["mean_voc_s"],
+                        "eval_viz/std_voc_s": reward_metrics["std_voc_s"],
+                        "eval_viz/valid_episodes": reward_metrics["num_valid_episodes"],
+                        "eval_viz/total_episodes": reward_metrics["total_episodes"],
+                        "eval_viz/mean_episode_length": reward_metrics["mean_episode_length"],
+                    }
+                    
+                    logging.info(f"RLearN Evaluation Results at Step {step}:")
+                    logging.info(f"  Mean VOC-S: {reward_metrics['mean_voc_s']:.4f} (±{reward_metrics['std_voc_s']:.4f})")
+                    logging.info(f"  Valid Episodes: {reward_metrics['num_valid_episodes']}/{reward_metrics['total_episodes']}")
+                    logging.info(f"  Mean Episode Length: {reward_metrics['mean_episode_length']:.1f}")
+                    logging.info(f"  Visualizations saved to: {eval_viz_dir}")
+                    
+                    if wandb_logger:
+                        wandb_logger.log_dict(eval_viz_metrics, step, mode="eval_viz")
+                        
+                        # Log the visualization image both as regular image and as artifact
+                        try:
+                            import wandb
+                            
+                            # Log as regular image for immediate viewing in wandb UI
+                            wandb_logger.wandb_run.log({
+                                f"eval_viz/reward_predictions_step_{step}": wandb.Image(str(reward_viz_path)),
+                            }, step=step)
+                            
+                            # Create and upload artifact with reward prediction visualization
+                            artifact_name = f"rlearn_reward_predictions_step_{step:06d}"
+                            artifact = wandb.Artifact(
+                                name=artifact_name,
+                                type="reward_prediction_visualization",
+                                description=f"RLearN reward prediction visualization at training step {step}",
+                                metadata={
+                                    "step": step,
+                                    "mean_voc_s": reward_metrics["mean_voc_s"],
+                                    "std_voc_s": reward_metrics["std_voc_s"],
+                                    "valid_episodes": reward_metrics["num_valid_episodes"],
+                                    "total_episodes": reward_metrics["total_episodes"],
+                                    "mean_episode_length": reward_metrics["mean_episode_length"],
+                                    "holdout_episodes": eval_holdout_episodes,
+                                }
+                            )
+                            
+                            # Add reward prediction visualization to the artifact
+                            artifact.add_file(str(reward_viz_path), name="reward_predictions.png")
+                            
+                            # Upload the artifact
+                            wandb_logger.wandb_run.log_artifact(artifact)
+                            
+                            logging.info(f"Uploaded wandb artifact: {artifact_name}")
+                            
+                        except Exception as e:
+                            logging.warning(f"Could not log visualization image to wandb: {e}")
+                    
+                    policy.train()  # Return to training mode
+                    
+            except Exception as e:
+                logging.error(f"Error during RLearN evaluation visualization: {e}")
+                # Continue training even if evaluation fails
+
     if eval_env:
         eval_env.close()
     logging.info("End of training")