diff --git a/detr/main.py b/detr/main.py
index 044b2a3..53ce92a 100644
--- a/detr/main.py
+++ b/detr/main.py
@@ -80,7 +80,7 @@ def get_args_parser():
 
 def build_ACT_model_and_optimizer(args_override):
     parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
-    args = parser.parse_args()
+    args, _ = parser.parse_known_args()
 
     for k, v in args_override.items():
         setattr(args, k, v)
@@ -103,7 +103,7 @@ def build_ACT_model_and_optimizer(args_override):
 
 def build_CNNMLP_model_and_optimizer(args_override):
     parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
-    args = parser.parse_args()
+    args, _ = parser.parse_known_args()
 
     for k, v in args_override.items():
         setattr(args, k, v)
diff --git a/imitate_episodes.py b/imitate_episodes.py
index 0b3dd88..eda6861 100644
--- a/imitate_episodes.py
+++ b/imitate_episodes.py
@@ -128,6 +128,7 @@ def main(args):
         'use_cached_text_features': use_cached_text_features,
         'text_tokenizer_name': text_tokenizer_name,
         'text_max_length': text_max_length,
+        'debug_input': args.get('debug_input', False),
     }
 
     if is_eval:
@@ -376,7 +377,7 @@ def eval_bc(config, ckpt_name, save_episode=True):
     return success_rate, avg_return
 
 
-def forward_pass(data, policy):
+def forward_pass(data, policy, debug_input=False, debug_tag=''):
     image_data, qpos_data, action_data, is_pad, text_input_ids, text_attention_mask, text_feature_data, text_feature_valid = data
     image_data = image_data.cuda()
     qpos_data = qpos_data.cuda()
@@ -391,6 +392,26 @@ def forward_pass(data, policy):
     if torch.any(text_feature_valid):
         text_features = text_feature_data
 
+    if debug_input:
+        image_min = float(image_data.min().item())
+        image_max = float(image_data.max().item())
+        qpos_mean = float(qpos_data.mean().item())
+        qpos_std = float(qpos_data.std().item())
+        action_mean = float(action_data.mean().item())
+        action_std = float(action_data.std().item())
+        pad_ratio = float(is_pad.float().mean().item())
+
+        print(f'[debug_input] {debug_tag} image shape={tuple(image_data.shape)} range=[{image_min:.4f}, {image_max:.4f}]')
+        print(f'[debug_input] {debug_tag} qpos  shape={tuple(qpos_data.shape)} mean/std=({qpos_mean:.4f}, {qpos_std:.4f})')
+        print(f'[debug_input] {debug_tag} action shape={tuple(action_data.shape)} mean/std=({action_mean:.4f}, {action_std:.4f})')
+        print(f'[debug_input] {debug_tag} is_pad shape={tuple(is_pad.shape)} pad_ratio={pad_ratio:.4f}')
+        print(
+            f'[debug_input] {debug_tag} has_nan_or_inf: '
+            f'image={bool(torch.logical_not(torch.isfinite(image_data)).any().item())}, '
+            f'qpos={bool(torch.logical_not(torch.isfinite(qpos_data)).any().item())}, '
+            f'action={bool(torch.logical_not(torch.isfinite(action_data)).any().item())}'
+        )
+
     return policy(
         qpos_data,
         image_data,
@@ -408,6 +429,7 @@ def train_bc(train_dataloader, val_dataloader, config):
     seed = config['seed']
     policy_class = config['policy_class']
     policy_config = config['policy_config']
+    debug_input = config.get('debug_input', False)
 
     set_seed(seed)
 
@@ -426,7 +448,8 @@ def train_bc(train_dataloader, val_dataloader, config):
             policy.eval()
             epoch_dicts = []
             for batch_idx, data in enumerate(val_dataloader):
-                forward_dict = forward_pass(data, policy)
+                should_debug = debug_input and epoch == 0 and batch_idx == 0
+                forward_dict = forward_pass(data, policy, debug_input=should_debug, debug_tag='val/epoch0/batch0')
                 epoch_dicts.append(forward_dict)
             epoch_summary = compute_dict_mean(epoch_dicts)
             validation_history.append(epoch_summary)
@@ -445,7 +468,8 @@ def train_bc(train_dataloader, val_dataloader, config):
         policy.train()
         optimizer.zero_grad()
         for batch_idx, data in enumerate(train_dataloader):
-            forward_dict = forward_pass(data, policy)
+            should_debug = debug_input and epoch == 0 and batch_idx == 0
+            forward_dict = forward_pass(data, policy, debug_input=should_debug, debug_tag='train/epoch0/batch0')
             # backward
             loss = forward_dict['loss']
             loss.backward()
@@ -519,5 +543,7 @@ if __name__ == '__main__':
     parser.add_argument('--text_max_length', action='store', type=int, required=False)
     parser.add_argument('--image_aug', action='store_true',
                         help='Enable training-time image augmentation (color/highlight/noise/blur)')
+    parser.add_argument('--debug_input', action='store_true',
+                        help='Print one-batch input sanity checks (shape/range/nan) for val/train at epoch 0')
     
     main(vars(parser.parse_args()))