diff --git a/fastdeploy/rl/dynamic_weight_manager.py b/fastdeploy/rl/dynamic_weight_manager.py
index a6b611516..4f45413ff 100644
--- a/fastdeploy/rl/dynamic_weight_manager.py
+++ b/fastdeploy/rl/dynamic_weight_manager.py
@@ -68,11 +68,11 @@ class DynamicWeightManager:
         paddle.device.cuda.empty_cache()
 
         # step1 : restart paddle process group
-        if not self.first_load:
-            paddle.distributed.restart_process_group()
-            paddle.distributed.restart_process_group(self.parallel_config.tp_group)
-            if self.parallel_config.enable_expert_parallel:
-                paddle.distributed.restart_process_group(self.parallel_config.ep_group)
+        # if not self.first_load:
+        #     paddle.distributed.restart_process_group()
+        #     paddle.distributed.restart_process_group(self.parallel_config.tp_group)
+        #     if self.parallel_config.enable_expert_parallel:
+        #         paddle.distributed.restart_process_group(self.parallel_config.ep_group)
 
         # step2 : recreat deepep buffer when enable expert parallel
         if self.parallel_config.enable_expert_parallel and not self.first_load:
@@ -136,7 +136,7 @@ class DynamicWeightManager:
             # ep barrier
             paddle.distributed.barrier(self.parallel_config.ep_group)
             # shutdown ep group
-            paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
+            # paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
 
         paddle.device.cuda.empty_cache()
         # step2: release model weight
@@ -149,11 +149,11 @@ class DynamicWeightManager:
         if self.parallel_config.tensor_parallel_size > 1:
             # tp barrier
             paddle.distributed.barrier(self.parallel_config.tp_group)
-            paddle.distributed.shutdown_process_group(self.parallel_config.tp_group)
+            # paddle.distributed.shutdown_process_group(self.parallel_config.tp_group)
         if self.parallel_config.enable_expert_parallel:
             paddle.distributed.barrier(self.parallel_config.ep_group)
-            paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
-        paddle.distributed.shutdown_process_group()
+            # paddle.distributed.shutdown_process_group(self.parallel_config.ep_group)
+        # paddle.distributed.shutdown_process_group()
         self._update_shared_status(pid, ModelWeightsStatus.CLEARED)
 
     def _update_model_from_state(self, state_dict: Dict[str, paddle.Tensor], src_type: str):
@@ -257,7 +257,7 @@ class DynamicWeightManager:
         """
         check model weights status
         """
-        logger.info(f"dynamic weight manager is check model weights status! {model_weights_status.value[0]}")
+        # logger.info(f"dynamic weight manager is check model weights status! {model_weights_status.value[0]}")
         while (
             model_weights_status.value[0] != ModelWeightsStatus.NORMAL
             and model_weights_status.value[0] != ModelWeightsStatus.CLEARED
diff --git a/fastdeploy/worker/worker_process.py b/fastdeploy/worker/worker_process.py
index 0c29ce4d7..4d87f3c1f 100644
--- a/fastdeploy/worker/worker_process.py
+++ b/fastdeploy/worker/worker_process.py
@@ -459,7 +459,7 @@ class PaddleDisWorkerProc:
                 else:
                     paddle.distributed.barrier(self.parallel_config.tp_group)
                 if self.model_weights_signal[0] != ModelWeightsStatus.NORMAL:
-                    logger.info(
+                    logger.debug(
                         f"Rank: {self.local_rank} to update or clear parameters, signal is {self.model_weights_signal[0]}, [-1:clear, 1:update]"
                     )
                     from fastdeploy.rl.dynamic_weight_manager import (
@@ -473,10 +473,10 @@ class PaddleDisWorkerProc:
                         self.worker.model_runner,
                         self.parallel_config.engine_worker_queue_port,
                     )
-                    logger.info(f"current task queue data: {self.task_queue.num_tasks()}")
+                    logger.debug(f"current task queue data: {self.task_queue.num_tasks()}")
                     self.task_queue.clear_data()
                     self.model_weights_signal[0] = ModelWeightsStatus.NORMAL
-                    logger.info(f"Rank: {self.local_rank} has updated or cleared parameters.")
+                    logger.debug(f"Rank: {self.local_rank} has updated or cleared parameters.")
 
             if self.exist_task_signal.value[0] == ExistTaskStatus.EXIST or self.task_queue.read_finish_flag.get() == 1:
                 logger.info(f"Rank: {self.local_rank} Detected new requests.")