diff --git a/actpred/dataset.py b/actpred/dataset.py
index db3f25b..1911729 100644
--- a/actpred/dataset.py
+++ b/actpred/dataset.py
@@ -76,7 +76,9 @@ class ActionDataset(Dataset):
                     raise ValueError(
                         f"数据集为空（0 样本），无法计算归一化统计量。"
                         f"请检查 data_dir={data_dir} 中的 episode 数据。")
-                all_actions = np.stack([s[2] for s in self.samples])
+                all_actions = np.stack(
+                    [np.concatenate([s[1], s[2][None]]) for s in self.samples]
+                ).reshape(-1, self.action_dim)
                 self._action_mean = all_actions.mean(axis=0)
                 self._action_std = all_actions.std(axis=0) + 1e-8
 
diff --git a/actpred/pretrain_mt.py b/actpred/pretrain_mt.py
index 08446a0..eebce47 100644
--- a/actpred/pretrain_mt.py
+++ b/actpred/pretrain_mt.py
@@ -217,13 +217,23 @@ def main():
                     f"{cfg.data.action_stats_path}")
             stats = np.load(cfg.data.action_stats_path)
         else:
-            all_actions = np.stack([s[1] for s in train_ds.samples])
+            # 统计量必须覆盖 history 与 target 并集：只按 targets 算时，
+            # targets 中恒定的列 std 落到 1e-8 地板值，而 history 窗口
+            # 可能含该列的其他取值（如 .mt 脚本前几行），z-score 会爆炸
+            # （实测 (90-45)/1e-8=4.5e9），后续 act_tokens ±1e9 直接
+            # 打爆 DFC 量化（NegativeSlopeExponentNonFixable）
+            all_actions = np.stack(
+                [np.concatenate([s[0], s[1][None]]) for s in train_ds.samples]
+            ).reshape(-1, cfg.data.action_dim)
             stats = np.stack([all_actions.mean(axis=0),
                               all_actions.std(axis=0) + 1e-8])
             stats_path = os.path.join(cfg.train.checkpoint_dir,
                                       "action_stats.npy")
             np.save(stats_path, stats)
-            cfg.data.action_stats_path = stats_path
+            # 只存裸文件名：推理端按 checkpoint 目录解析相对路径
+            # （predict.py/export_deploy.py/gen_calibration.py 同语义），
+            # 存 "./checkpoints/..." 会被二次拼接成 checkpoints/./checkpoints
+            cfg.data.action_stats_path = "action_stats.npy"
             print(f"action_stats 已保存: {stats_path}（训练集 targets 统计）")
         if stats.shape != (2, cfg.data.action_dim):
             raise ValueError(
diff --git a/actpred/test_actpred.py b/actpred/test_actpred.py
index 00e2664..994570b 100644
--- a/actpred/test_actpred.py
+++ b/actpred/test_actpred.py
@@ -261,6 +261,30 @@ class TestActionDataset(unittest.TestCase):
             self.assertIn("WARNING", buf.getvalue())
             self.assertEqual(len(ds), 8 - 3)  # 按较少者（8 帧图像）截断
 
+    def test_stats_cover_history_values(self):
+        """归一化统计量必须覆盖 history 独有取值，否则 z-score 爆炸
+
+        col0 仅在 history（首行）出现 100，targets 全为 0：只按 targets
+        算统计时 std 落到 1e-8 地板值，z=(100-0)/1e-8=1e10。
+        """
+        with tempfile.TemporaryDirectory() as tmpdir:
+            base = Path(tmpdir)
+            ep_dir = base / "episode_0001"
+            images_dir = ep_dir / "images"
+            images_dir.mkdir(parents=True, exist_ok=True)
+            for i in range(7):
+                Image.fromarray(np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8)
+                                ).save(images_dir / f"{i:04d}.jpg")
+            actions = np.zeros((7, 4), dtype=np.float32)
+            actions[0, 0] = 100.0
+            np.save(ep_dir / "actions.npy", actions)
+            ds = ActionDataset(tmpdir, history_len=5, action_dim=4,
+                               normalize_actions=True)
+            self.assertGreater(ds._action_std[0], 1.0)  # 而非 1e-8 地板值
+            z_max = (np.abs(actions[:, 0] - ds._action_mean[0])
+                     / ds._action_std[0]).max()
+            self.assertLess(z_max, 10.0)
+
 
 class TestConfig(unittest.TestCase):
     """测试配置和 CLI 参数"""
diff --git a/actpred/train.py b/actpred/train.py
index b99956a..57fa1e3 100644
--- a/actpred/train.py
+++ b/actpred/train.py
@@ -89,7 +89,8 @@ def main():
         stats_path = os.path.join(cfg.train.checkpoint_dir, "action_stats.npy")
         np.save(stats_path, train_ds.get_action_stats())
         print(f"Action stats saved to: {stats_path}")
-        cfg.data.action_stats_path = stats_path
+        # 只存裸文件名：推理端按 checkpoint 目录解析相对路径
+        cfg.data.action_stats_path = "action_stats.npy"
     val_ds = ActionDataset(
         cfg.data.data_dir, cfg.data.history_len, cfg.data.action_dim,
         cfg.data.image_width, cfg.data.image_height, cfg.data.normalize_actions,