diff --git a/src/rlhf/reward.py b/src/rlhf/reward.py
index 5eb83630f4223d919ccf88787781875b3fe6f636..580ac19ff24b423e2f50a64a7fa940008adbf25c 100644
--- a/src/rlhf/reward.py
+++ b/src/rlhf/reward.py
@@ -73,11 +73,6 @@ class RewardModel(pl.LightningModule):
         # reward 得分计算
         self.pred_reward = nn.Linear(dim, 1, bias=False)
 
-        # self.pred_reward = nn.Sequential(
-        #     nn.Linear(dim, 1, bias=False),
-        #     Rearrange('... 1 -> ...')   # 降维
-        # )
-
     def load(self, path):
         path = Path(path)
         assert path.exists()
@@ -124,20 +119,10 @@ class RewardModel(pl.LightningModule):
                     {"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 3.0},
                 ]
 
-            optim_names = [
-                {"params": lr_1x},
-                {"params": lr_2x},
-                {"params": lr_3x},
-            ]
-
         else:
             optim_groups = [
                 {"params": [p for n, p in self.named_parameters()], "weight_decay": 0.0},
             ]
-            
-            optim_names = [
-                {"params": [n for n, p in self.named_parameters()]},
-            ]
         
         if self.deepspeed_offload:
             return DeepSpeedCPUAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adamw_mode=False, weight_decay=0, amsgrad=False)
diff --git a/train_rm.py b/train_rm.py
index 84f61439bbbb02183f4e55ccefb226183a1b5de4..bff30bd5910a6b1c348825a8f43feaaa1d170cb9 100644
--- a/train_rm.py
+++ b/train_rm.py
@@ -256,7 +256,7 @@ if __name__ == "__main__":
         trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
 
     # must set shuffle=True, persistent_workers=False (because worker is in another thread)
-    data_loader = DataLoader(train_data, shuffle=True, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)
+    data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)
 
     trainer.fit(rm_model, data_loader)