diff --git a/examples/extras/galore/sft.sh b/examples/extras/galore/sft.sh
index 1e46ac1f..da1779ed 100644
--- a/examples/extras/galore/sft.sh
+++ b/examples/extras/galore/sft.sh
@@ -11,8 +11,8 @@ CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
     --use_galore \
     --galore_layerwise \
     --galore_target mlp,self_attn \
-    --galore_scale 2.0 \
     --galore_rank 128 \
+    --galore_scale 2.0 \
     --output_dir ../../../saves/LLaMA2-7B/galore/sft \
     --overwrite_cache \
     --overwrite_output_dir \
@@ -29,8 +29,8 @@ CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
     --evaluation_strategy steps \
     --load_best_model_at_end \
     --learning_rate 5e-5 \
-    --num_train_epochs 30.0 \
-    --max_samples 300 \
+    --num_train_epochs 3.0 \
+    --max_samples 3000 \
     --val_size 0.1 \
     --plot_loss \
     --pure_bf16
diff --git a/src/llmtuner/train/utils.py b/src/llmtuner/train/utils.py
index 2835eddf..d3f17116 100644
--- a/src/llmtuner/train/utils.py
+++ b/src/llmtuner/train/utils.py
@@ -234,14 +234,6 @@ def _create_galore_optimizer(
             param_groups = [dict(params=[param], weight_decay=training_args.weight_decay, **galore_kwargs)]
             optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
 
-        def optimizer_hook(param: "torch.nn.Parameter"):
-            if param.grad is not None:
-                optimizer_dict[param].step()
-                optimizer_dict[param].zero_grad()
-
-        for param in trainable_params:
-            param.register_post_accumulate_grad_hook(optimizer_hook)
-
         optimizer = DummyOptimizer(lr=training_args.learning_rate, optimizer_dict=optimizer_dict)
     else:
         param_groups = [
@@ -391,9 +383,11 @@ def create_custom_scheduler(
                 num_training_steps=num_training_steps * 2,
             )
 
-        def scheduler_hook(param: "torch.nn.Parameter"):
+        def optimizer_hook(param: "torch.nn.Parameter"):
             if param.grad is not None:
+                optimizer_dict[param].step()
+                optimizer_dict[param].zero_grad()
                 scheduler_dict[param].step()
 
         for param in optimizer_dict.keys():
-            param.register_post_accumulate_grad_hook(scheduler_hook)
+            param.register_post_accumulate_grad_hook(optimizer_hook)