diff --git a/llm/config/deepseek-v3/sft_argument.json b/llm/config/deepseek-v3/sft_argument.json
index edc8452fe09e..7e713dd4b355 100644
--- a/llm/config/deepseek-v3/sft_argument.json
+++ b/llm/config/deepseek-v3/sft_argument.json
@@ -8,7 +8,7 @@
   "per_device_eval_batch_size": 1,
   "eval_accumulation_steps": 1,
   "max_steps": 100,
-  "max_grad_norm": 0,
+  "max_grad_norm": 1.0,
   "amp_master_grad": true,
   "num_train_epochs": 1,
   "learning_rate": 2.2e-05,
diff --git a/llm/run_finetune.py b/llm/run_finetune.py
index de40634ef379..36e7b221729b 100644
--- a/llm/run_finetune.py
+++ b/llm/run_finetune.py
@@ -46,7 +46,7 @@
     ReFTModel,
     intervention_mapping,
 )
-from paddlenlp.trainer import PdArgumentParser, get_last_checkpoint, set_seed, MoECorrectionBiasAdjustCallback
+from paddlenlp.trainer import PdArgumentParser, get_last_checkpoint, set_seed, MoECorrectionBiasAdjustCallback, MoeExpertsGradScaleCallback
 from paddlenlp.trainer.trainer_callback import TrainerState
 from paddlenlp.transformers import (
     AutoConfig,
@@ -474,7 +474,11 @@ def compute_metrics_do_generation(eval_preds):
         callbacks += [ZeroPaddingIterDatasetCallback()]
     
     if getattr(model_config, "topk_method", None) == "noaux_tc":
-        callbacks += [MoECorrectionBiasAdjustCallback(lr=0)]
+        # deepseek_v3 finetune do not update the bias, so set lr to 0.0
+        callbacks += [MoECorrectionBiasAdjustCallback(lr=0.0)]
+    
+    if training_args.use_expert_parallel:
+        callbacks += [MoeExpertsGradScaleCallback(training_args)]
     
     print("callbacks:", callbacks, flush=True)
     trainer = SFTTrainer(
diff --git a/paddlenlp/optimizers/moe_hybrid_parallel_optimizer.py b/paddlenlp/optimizers/moe_hybrid_parallel_optimizer.py
index b11263fe90e6..479aa9a44c05 100644
--- a/paddlenlp/optimizers/moe_hybrid_parallel_optimizer.py
+++ b/paddlenlp/optimizers/moe_hybrid_parallel_optimizer.py
@@ -179,7 +179,7 @@ def _dygraph_clip(self, params_grads):
             )
 
             is_moe_param = getattr(p, "is_moe_param", False)
-            print(f"p.name:{p.name}, is_moe_param:{is_moe_param}")
+
             if is_moe_param:
                 assert 0
             if not_shared_enable:
@@ -350,13 +350,6 @@ def __getattr__(self, item):
         return getattr(self._clip, item)
 
     def __call__(self, params_grads):
-        print("==== zyc debug in moe_hybrid_parallel_optimizer.py ====")
-        for p, g in params_grads:
-            has_moe_attr = hasattr(p, "is_moe_param")
-            is_moe_param = False
-            if has_moe_attr:
-                is_moe_param = p.is_moe_param
-            print(f"p.name:{p.name}, has_moe_attr:{has_moe_attr}, is_moe_param:{is_moe_param}")
         return self._dygraph_clip(params_grads)
 
 
diff --git a/paddlenlp/trainer/trainer.py b/paddlenlp/trainer/trainer.py
index db23594b7bc1..c44eb5bf23c7 100644
--- a/paddlenlp/trainer/trainer.py
+++ b/paddlenlp/trainer/trainer.py
@@ -1323,21 +1323,6 @@ def fused_allreduce_gradients_no_sync(paramlist, hcg):
                                 elif p.grad is not None:
                                     p.grad.scale_(1.0 / self.args.gradient_accumulation_steps)
 
-                    if os.environ.get("FIX_EP_GRAD", None):
-                        param_count = 0
-                        for p in model._layers.parameters():
-                            if hasattr(p, "is_moe_param") and p.is_moe_param:
-                                with paddle.no_grad():
-                                    if hasattr(p, "main_grad") and p.main_grad is not None:
-                                        # print("main grad scale 1/ep")
-                                        p.main_grad.scale_(1.0 / self.args.expert_parallel_degree)
-                                        param_count += 1
-                                    elif p.grad is not None:
-                                        # print("grad scale 1/ep")
-                                        p.grad.scale_(1.0 / self.args.expert_parallel_degree)
-                                        param_count += 1
-                        print("fix ep grad count:{}".format(param_count), flush=True)
-
                     # Optimizer step
                     self.callback_handler.on_optimizer_begin(
                         args, self.state, self.control, scaler=self.scaler if self.do_grad_scaling else None
diff --git a/paddlenlp/trainer/trainer_callback.py b/paddlenlp/trainer/trainer_callback.py
index 8f9beaab813e..e4394bfde2ea 100644
--- a/paddlenlp/trainer/trainer_callback.py
+++ b/paddlenlp/trainer/trainer_callback.py
@@ -46,6 +46,7 @@
     "PrinterCallback",
     "EarlyStoppingCallback",
     "MoECorrectionBiasAdjustCallback",
+    "MoeExpertsGradScaleCallback",
 ]
 
 
@@ -673,4 +674,41 @@ def update_bias(layer):
                         biases.pop(0).add_(update_list.pop(0))
                     usages.pop(0).zero_()
 
-        model.apply(update_bias)
\ No newline at end of file
+        model.apply(update_bias)
+        
+class MoeExpertsGradScaleCallback(TrainerCallback):
+    """
+    此 hook 用于修正专家参数的梯度被放大N倍的问题
+    """
+
+    def __init__(self, args):
+        """_summary_
+
+        Args:
+            args (_type_): _description_
+        """
+        if not args.use_expert_parallel:
+            raise ValueError("This callback should be used with expert parallel")
+        if args.expert_parallel_degree > 1:
+            self.expert_gradient_scaling_factor = 1.0 / args.expert_parallel_degree
+            if args.tensor_parallel_degree > 1:
+                self.expert_gradient_scaling_factor *= args.tensor_parallel_degree
+            logger.info(
+                f"EP-MoE is used, expert gradient scaling factor is set to {self.expert_gradient_scaling_factor}"
+            )
+
+    def on_optimizer_begin(self, args, state, control, **kwargs):
+        model = kwargs["model"]
+        param_count = 0
+        for p in model.parameters():
+            if not getattr(p, "no_sync", False):
+                continue
+            if hasattr(p, "is_moe_param") and p.is_moe_param:
+                with paddle.no_grad():
+                    if hasattr(p, "main_grad") and p.main_grad is not None:
+                        p.main_grad.scale_(self.expert_gradient_scaling_factor)
+                        param_count += 1
+                    elif p.grad is not None:
+                        p.grad.scale_(self.expert_gradient_scaling_factor)
+                        param_count += 1
+        logger.info("correct ep grad count:{}".format(param_count))
\ No newline at end of file