diff --git a/src/llmtuner/tuner/dpo/trainer.py b/src/llmtuner/tuner/dpo/trainer.py
index 458e99db..3f7f7af5 100644
--- a/src/llmtuner/tuner/dpo/trainer.py
+++ b/src/llmtuner/tuner/dpo/trainer.py
@@ -10,7 +10,7 @@ from llmtuner.tuner.core.trainer import PeftModelMixin
 
 if TYPE_CHECKING:
     from transformers import PreTrainedModel
-    from llmtuner.hparams import FinetuningArguments, GeneratingArguments
+    from llmtuner.hparams import FinetuningArguments
 
 
 class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
@@ -18,12 +18,10 @@ class DPOPeftTrainer(PeftModelMixin, DPOTrainer):
     def __init__(
         self,
         finetuning_args: "FinetuningArguments",
-        generating_args: "GeneratingArguments",
         ref_model: Optional[Union["PreTrainedModel", torch.nn.Module]] = None,
         **kwargs
     ):
         self.finetuning_args = finetuning_args
-        self.generating_args = generating_args
         self.ref_model = ref_model
         self.use_dpo_data_collator = True # hack to avoid warning
         self.label_pad_token_id = IGNORE_INDEX
diff --git a/src/llmtuner/tuner/dpo/workflow.py b/src/llmtuner/tuner/dpo/workflow.py
index af184ce5..4f41d4c6 100644
--- a/src/llmtuner/tuner/dpo/workflow.py
+++ b/src/llmtuner/tuner/dpo/workflow.py
@@ -13,7 +13,7 @@ from llmtuner.tuner.dpo.trainer import DPOPeftTrainer
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
-    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
+    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
 
 
 def run_dpo(
@@ -21,7 +21,6 @@ def run_dpo(
     data_args: "DataArguments",
     training_args: "Seq2SeqTrainingArguments",
     finetuning_args: "FinetuningArguments",
-    generating_args: "GeneratingArguments",
     callbacks: Optional[List["TrainerCallback"]] = None
 ):
     dataset = get_dataset(model_args, data_args)
@@ -38,7 +37,6 @@ def run_dpo(
     # Initialize our Trainer
     trainer = DPOPeftTrainer(
         finetuning_args=finetuning_args,
-        generating_args=generating_args,
         ref_model=ref_model,
         model=model,
         args=training_args,