diff --git a/applications/ChatGPT/chatgpt/trainer/ppo.py b/applications/ChatGPT/chatgpt/trainer/ppo.py
index 789e0c2f8f1e..dacab4784039 100644
--- a/applications/ChatGPT/chatgpt/trainer/ppo.py
+++ b/applications/ChatGPT/chatgpt/trainer/ppo.py
@@ -63,6 +63,7 @@ def __init__(self,
                  **generate_kwargs) -> None:
         experience_maker = NaiveExperienceMaker(actor, critic, reward_model, initial_model, kl_coef)
         replay_buffer = NaiveReplayBuffer(train_batch_size, buffer_limit, buffer_cpu_offload)
+        generate_kwargs = _set_default_generate_kwargs(strategy, generate_kwargs, actor)
         super().__init__(strategy, experience_maker, replay_buffer, experience_batch_size, max_epochs, tokenizer,
                          sample_replay_buffer, dataloader_pin_memory, callbacks, **generate_kwargs)
         self.actor = actor
@@ -73,7 +74,6 @@ def __init__(self,
 
         self.actor_optim = actor_optim
         self.critic_optim = critic_optim
-        self._set_default_generate_kwargs(generate_kwargs, actor)
 
     def training_step(self, experience: Experience) -> Dict[str, float]:
         self.actor.train()
@@ -102,11 +102,15 @@ def training_step(self, experience: Experience) -> Dict[str, float]:
 
         return {'actor_loss': actor_loss.item(), 'critic_loss': critic_loss.item()}
 
-    def _set_default_generate_kwargs(self, generate_kwargs: dict, actor: Actor) -> None:
-        origin_model = self.strategy._unwrap_actor(actor)
-        # use huggingface models method directly
-        if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
-            generate_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation
 
-        if 'update_model_kwargs_fn' not in generate_kwargs:
-            generate_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn
+def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None:
+    origin_model = strategy._unwrap_actor(actor)
+    new_kwargs = {**generate_kwargs}
+    # use huggingface models method directly
+    if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
+        new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation
+
+    if 'update_model_kwargs_fn' not in generate_kwargs:
+        new_kwargs['update_model_kwargs_fn'] = update_model_kwargs_fn
+
+    return new_kwargs