diff --git a/fastdeploy/rl/dynamic_weight_manager.py b/fastdeploy/rl/dynamic_weight_manager.py
index ae613f55d..e2fbcb6c8 100644
--- a/fastdeploy/rl/dynamic_weight_manager.py
+++ b/fastdeploy/rl/dynamic_weight_manager.py
@@ -51,8 +51,7 @@ class DynamicWeightManager:
 
         logger.info(
             f"✅ DynamicLoad model built successfully by {self.load_config.load_strategy}, "
-            f" rank={self.rank}, ranks={self.nranks}, "
-            f" load ipc weight from {self.ipc_path}.")
+            f" rank={self.rank}, ranks={self.nranks}")
 
     @paddle.no_grad()
     def _capture_model_state(self):
@@ -114,21 +113,25 @@ class DynamicWeightManager:
             logger.warning(
                 "load model from no_reshard weight, maybe need more GPU memory"
             )
-        logger.info("IPC snapshot update parameters completed")
+        logger.info(
+            f"IPC snapshot update parameters completed from {model_path}")
 
     def _update_ipc(self):
         """Update using standard IPC strategy (requires Training Worker)."""
         ipc_meta = paddle.load(self.ipc_path)
         state_dict = self._convert_ipc_meta_to_tensor(ipc_meta)
         self._update_model_from_state(state_dict, "raw")
-        logger.info("IPC update parameters completed")
+        logger.info(
+            f"IPC update parameters completed from file: {self.ipc_path}")
 
     def _update_ipc_no_reshard(self):
         """Update using no-reshard IPC strategy (faster but uses more memory)."""
         ipc_meta = paddle.load(self.ipc_path)
         state_dict = self._convert_ipc_meta_to_tensor(ipc_meta)
         self.models[0].set_state_dict(state_dict)
-        logger.info("IPC no-reshard update parameters completed")
+        logger.info(
+            f"IPC no-reshard update parameters completed from file: {self.ipc_path}"
+        )
 
     def load_model(self) -> nn.Layer:
         """Standard model loading without IPC."""
@@ -159,6 +162,8 @@ class DynamicWeightManager:
     def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor],
                                  src_type: str):
         """Update model parameters from given state dictionary."""
+        if len(state_dict) == 0:
+            raise ValueError(f"No parameter found in state dict {state_dict}")
         update_count = 0
         for name, new_param in state_dict.items():
             if name not in self.state_dict: