diff --git a/trainer/train_distillation.py b/trainer/train_distillation.py
index 2b8afc3..2ec6fbe 100644
--- a/trainer/train_distillation.py
+++ b/trainer/train_distillation.py
@@ -221,13 +221,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, teacher_model, lm_config_student, start_step, wandb, args.alpha, args.temperature)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, teacher_model, lm_config_student, start_step, wandb, args.alpha, args.temperature)
+        else:
             train_epoch(epoch, loader, len(loader), teacher_model, lm_config_student, 0, wandb, args.alpha, args.temperature)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_dpo.py b/trainer/train_dpo.py
index 2f37851..31102cf 100644
--- a/trainer/train_dpo.py
+++ b/trainer/train_dpo.py
@@ -205,13 +205,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, lm_config, start_step, wandb, args.beta)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, ref_model, lm_config, start_step, wandb, args.beta)
+        else:
             train_epoch(epoch, loader, len(loader), ref_model, lm_config, 0, wandb, args.beta)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_full_sft.py b/trainer/train_full_sft.py
index fc68cd7..7b64a23 100644
--- a/trainer/train_full_sft.py
+++ b/trainer/train_full_sft.py
@@ -148,13 +148,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, start_step, wandb)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)
+        else:
             train_epoch(epoch, loader, len(loader), 0, wandb)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_grpo.py b/trainer/train_grpo.py
index 593780f..bab4223 100755
--- a/trainer/train_grpo.py
+++ b/trainer/train_grpo.py
@@ -290,15 +290,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0:  # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            grpo_train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, reward_model, reward_tokenizer, start_step, wandb)
-        else:  # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, pin_memory=True,
-                              drop_last=False, shuffle=(train_sampler is None),
-                              num_workers=args.num_workers, sampler=train_sampler)
+            grpo_train_epoch(epoch, loader, len(loader) + skip, ref_model, reward_model, reward_tokenizer, start_step, wandb)
+        else:
             grpo_train_epoch(epoch, loader, len(loader), ref_model, reward_model, reward_tokenizer, 0, wandb)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_lora.py b/trainer/train_lora.py
index fdf83bc..83d54c6 100644
--- a/trainer/train_lora.py
+++ b/trainer/train_lora.py
@@ -161,13 +161,14 @@ if __name__ == "__main__":
     # ========== 9. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, lora_params, start_step, wandb)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, lora_params, start_step, wandb)
+        else:
             train_epoch(epoch, loader, len(loader), lora_params, 0, wandb)
     
     # ========== 10. 清理分布进程 ==========
diff --git a/trainer/train_ppo.py b/trainer/train_ppo.py
index 02e9e0c..47af75a 100644
--- a/trainer/train_ppo.py
+++ b/trainer/train_ppo.py
@@ -365,15 +365,15 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0:  # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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) + start_step + 1, old_actor_model, ref_model, 
+            ppo_train_epoch(epoch, loader, len(loader) + skip, old_actor_model, ref_model, 
                            actor_scheduler, critic_scheduler, reward_model, reward_tokenizer, start_step, wandb)
-        else:  # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), 
-                              sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+        else:
             ppo_train_epoch(epoch, loader, len(loader), old_actor_model, ref_model, 
                            actor_scheduler, critic_scheduler, reward_model, reward_tokenizer, 0, wandb)
     
diff --git a/trainer/train_pretrain.py b/trainer/train_pretrain.py
index 7f2a584..ea0ca21 100644
--- a/trainer/train_pretrain.py
+++ b/trainer/train_pretrain.py
@@ -147,13 +147,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, start_step, wandb)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, start_step, wandb)
+        else:
             train_epoch(epoch, loader, len(loader), 0, wandb)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_reason.py b/trainer/train_reason.py
index 80c6879..eaae014 100644
--- a/trainer/train_reason.py
+++ b/trainer/train_reason.py
@@ -165,13 +165,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0: # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            train_epoch(epoch, loader, len(loader) + start_step + 1, tokenizer, lm_config, start_step, wandb)
-        else: # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=(train_sampler is None), sampler=train_sampler, num_workers=args.num_workers, pin_memory=True)
+            train_epoch(epoch, loader, len(loader) + skip, tokenizer, lm_config, start_step, wandb)
+        else:
             train_epoch(epoch, loader, len(loader), tokenizer, lm_config, 0, wandb)
     
     # ========== 9. 清理分布进程 ==========
diff --git a/trainer/train_spo.py b/trainer/train_spo.py
index d449491..b6308d5 100755
--- a/trainer/train_spo.py
+++ b/trainer/train_spo.py
@@ -340,15 +340,14 @@ if __name__ == "__main__":
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
         train_sampler and train_sampler.set_epoch(epoch)
-        if epoch == start_epoch and start_step > 0:  # 第一个epoch且存在检查点
-            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
-            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+        setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
+        skip = start_step + 1 if (epoch == start_epoch and start_step > 0) else 0
+        batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        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')
-            spo_train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, reward_model, reward_tokenizer, value_tracker, start_step, wandb)
-        else:  # 默认从头开始
-            loader = DataLoader(train_ds, batch_size=args.batch_size, pin_memory=True,
-                              drop_last=False, shuffle=(train_sampler is None),
-                              num_workers=args.num_workers, sampler=train_sampler)
+            spo_train_epoch(epoch, loader, len(loader) + skip, ref_model, reward_model, reward_tokenizer, value_tracker, start_step, wandb)
+        else:
             spo_train_epoch(epoch, loader, len(loader), ref_model, reward_model, reward_tokenizer, value_tracker, 0, wandb)
     
     # ========== 9. 清理分布进程 ==========