diff --git a/src/llamafactory/train/ppo/trainer.py b/src/llamafactory/train/ppo/trainer.py
index 57f0b848..8b89e38a 100644
--- a/src/llamafactory/train/ppo/trainer.py
+++ b/src/llamafactory/train/ppo/trainer.py
@@ -70,7 +70,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         training_args: "Seq2SeqTrainingArguments",
         finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
-        callbacks: List["TrainerCallback"],
+        callbacks: Optional[List["TrainerCallback"]],
         model: "AutoModelForCausalLMWithValueHead",
         reward_model: Optional["AutoModelForCausalLMWithValueHead"],
         ref_model: Optional["AutoModelForCausalLMWithValueHead"],
@@ -78,7 +78,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         processor: Optional["ProcessorMixin"],
         dataset: "Dataset",
         data_collator: "DataCollatorWithPadding",
-    ):
+    ) -> None:
         backward_batch_size = training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps
         ppo_config = PPOConfig(
             model_name=model_args.model_name_or_path,
@@ -144,7 +144,7 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         self.is_deepspeed_enabled = getattr(self.accelerator.state, "deepspeed_plugin", None) is not None
         self.is_fsdp_enabled = getattr(self.accelerator.state, "fsdp_plugin", None) is not None
         self.callback_handler = CallbackHandler(
-            [callbacks], self.accelerator.unwrap_model(self.model), self.tokenizer, self.optimizer, self.lr_scheduler
+            callbacks, self.accelerator.unwrap_model(self.model), self.tokenizer, self.optimizer, self.lr_scheduler
         )
 
         if self.args.max_steps > 0:
diff --git a/src/llamafactory/train/ppo/workflow.py b/src/llamafactory/train/ppo/workflow.py
index 651296f3..df22dae5 100644
--- a/src/llamafactory/train/ppo/workflow.py
+++ b/src/llamafactory/train/ppo/workflow.py
@@ -22,7 +22,7 @@ from transformers import DataCollatorWithPadding
 from ...data import get_dataset
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
-from ..callbacks import FixValueHeadModelCallback, fix_valuehead_checkpoint
+from ..callbacks import fix_valuehead_checkpoint
 from ..trainer_utils import create_ref_model, create_reward_model
 from .trainer import CustomPPOTrainer
 
@@ -59,7 +59,7 @@ def run_ppo(
         training_args=training_args,
         finetuning_args=finetuning_args,
         generating_args=generating_args,
-        callbacks=callbacks + [FixValueHeadModelCallback()],
+        callbacks=callbacks,
         model=model,
         reward_model=reward_model,
         ref_model=ref_model,