diff --git a/roboimi/vla/agent.py b/roboimi/vla/agent.py
index 34fa47c..a1a1883 100644
--- a/roboimi/vla/agent.py
+++ b/roboimi/vla/agent.py
@@ -94,9 +94,51 @@ class VLAAgent(nn.Module):
         B = actions.shape[0]
 
         # 归一化 states (qpos) 和 actions
+        # ======== 归一化测试代码 (调试用) ========
+        if not hasattr(self, '_norm_test_done'):
+            self._norm_test_done = True
+            print("\n" + "=" * 60)
+            print("归一化测试 - 第一个batch:")
+            print("=" * 60)
+
+            # 检查action归一化
+            action_orig = batch['action'].clone()
+            print(f"Action 原始范围: [{action_orig.min():.4f}, {action_orig.max():.4f}]")
+            print(f"Action 各维度范围 (前5维):")
+            for i in range(min(5, action_orig.shape[-1])):
+                print(f"  维度{i}: [{action_orig[..., i].min():.4f}, {action_orig[..., i].max():.4f}]")
+
+            # 检查qpos归一化
+            state_orig = states.clone()
+            print(f"Qpos 原始范围: [{state_orig.min():.4f}, {state_orig.max():.4f}]")
+
         states = self.normalization.normalize_qpos(states)
         actions = self.normalization.normalize_action(actions)
 
+        if hasattr(self, '_norm_test_done'):
+            print(f"Action 归一化后范围: [{actions.min():.4f}, {actions.max():.4f}]")
+            print(f"Qpos 归一化后范围: [{states.min():.4f}, {states.max():.4f}]")
+
+            # 检查是否在预期范围内
+            if self.normalization.normalization_type == 'min_max':
+                action_in_range = (actions >= -1.1) & (actions <= 1.1)
+                state_in_range = (states >= -1.1) & (states <= 1.1)
+                print(f"Action 在[-1,1]范围内: {action_in_range.all().item()}")
+                print(f"Qpos 在[-1,1]范围内: {state_in_range.all().item()}")
+
+                if not action_in_range.all():
+                    print(f"⚠️  Action超出范围的维度:")
+                    for i in range(actions.shape[-1]):
+                        if not action_in_range[..., i].all():
+                            print(f"  维度{i}: min={actions[..., i].min():.4f}, max={actions[..., i].max():.4f}")
+                if not state_in_range.all():
+                    print(f"⚠️  Qpos超出范围的维度:")
+                    for i in range(states.shape[-1]):
+                        if not state_in_range[..., i].all():
+                            print(f"  维度{i}: min={states[..., i].min():.4f}, max={states[..., i].max():.4f}")
+            print("=" * 60 + "\n")
+        # ======== 归一化测试代码结束 ========
+
         state_features = self.state_encoder(states)
 
         # 1. 提取视觉特征
@@ -316,9 +358,24 @@ class VLAAgent(nn.Module):
         """
         B = proprioception.shape[0]
 
+        # ======== 推理归一化测试代码 (调试用) ========
+        if not hasattr(self, '_infer_norm_test_done'):
+            self._infer_norm_test_done = True
+            print("\n" + "=" * 60)
+            print("推理归一化测试 - 第一个推理batch:")
+            print("=" * 60)
+            print(f"Qpos输入范围: [{proprioception.min():.4f}, {proprioception.max():.4f}]")
         # 归一化 proprioception (qpos)
+        proprioception_orig = proprioception.clone()
         proprioception = self.normalization.normalize_qpos(proprioception)
 
+        if hasattr(self, '_infer_norm_test_done'):
+            print(f"Qpos归一化后范围: [{proprioception.min():.4f}, {proprioception.max():.4f}]")
+            if self.normalization.normalization_type == 'min_max':
+                in_range = (proprioception >= -1.1) & (proprioception <= 1.1)
+                print(f"Qpos在[-1,1]范围内: {in_range.all().item()}")
+        # ======== 推理归一化测试代码结束 ========
+
         # 1. 提取当前观测特征（只提取一次）
         visual_features = self.vision_encoder(images)
         state_features = self.state_encoder(proprioception)
@@ -356,8 +413,17 @@ class VLAAgent(nn.Module):
             ).prev_sample
 
         # 4. 反归一化动作序列
+        # ======== 反归一化测试代码 (调试用) ========
+        if hasattr(self, '_infer_norm_test_done'):
+            print(f"去噪后action范围 (归一化空间): [{current_actions.min():.4f}, {current_actions.max():.4f}]")
+
         denormalized_actions = self.normalization.denormalize_action(current_actions)
 
+        if hasattr(self, '_infer_norm_test_done'):
+            print(f"反归一化后action范围: [{denormalized_actions.min():.4f}, {denormalized_actions.max():.4f}]")
+            print("=" * 60 + "\n")
+        # ======== 反归一化测试代码结束 ========
+
         return denormalized_actions
 
     def get_normalization_stats(self):