diff --git a/trainer/train_ppo.py b/trainer/train_ppo.py
index 4e175e7..0cf844d 100644
--- a/trainer/train_ppo.py
+++ b/trainer/train_ppo.py
@@ -12,7 +12,6 @@ import warnings
 import torch
 import torch.distributed as dist
 import torch.nn.functional as F
-from transformers import AutoTokenizer
 from contextlib import nullcontext
 from torch import optim, nn
 from torch.nn.parallel import DistributedDataParallel
@@ -76,7 +75,7 @@ def calculate_rewards(prompts, responses, reward_model):
     return rewards
 
 
-def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, start_step=0, wandb=None, use_sglang=False):
+def ppo_train_epoch(epoch, loader, iters, rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, start_step=0, wandb=None):
     actor_model.train()
     critic_model.train()
     grad_accum_step = 0
@@ -442,9 +441,9 @@ if __name__ == "__main__":
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')
-            ppo_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, start_step, wandb, use_sglang = (args.rollout_engine == "sglang"))
+            ppo_train_epoch(epoch, loader, len(loader) + skip, rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, start_step, wandb)
         else:
-            ppo_train_epoch(epoch, loader, len(loader), rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, 0, wandb, use_sglang = (args.rollout_engine == "sglang"))
+            ppo_train_epoch(epoch, loader, len(loader), rollout_engine, ref_model, actor_scheduler, critic_scheduler, reward_model, 0, wandb)
     
     # ========== 9. 清理分布进程 ==========
     if dist.is_initialized():