diff --git a/src/llmtuner/model/parser.py b/src/llmtuner/model/parser.py
index 063233dc..0c5db59b 100644
--- a/src/llmtuner/model/parser.py
+++ b/src/llmtuner/model/parser.py
@@ -130,6 +130,9 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
     if finetuning_args.stage == "ppo" and model_args.shift_attn:
         raise ValueError("PPO training is incompatible with S^2-Attn.")
 
+    if finetuning_args.stage == "ppo" and finetuning_args.reward_model_type == "lora" and model_args.use_unsloth:
+        raise ValueError("Unsloth does not support lora reward model.")
+
     if training_args.max_steps == -1 and data_args.streaming:
         raise ValueError("Please specify `max_steps` in streaming mode.")
 
diff --git a/src/llmtuner/train/dpo/trainer.py b/src/llmtuner/train/dpo/trainer.py
index 7fafd98a..132df189 100644
--- a/src/llmtuner/train/dpo/trainer.py
+++ b/src/llmtuner/train/dpo/trainer.py
@@ -69,7 +69,7 @@ class CustomDPOTrainer(DPOTrainer):
         Returns:
             A tensor of shape (batch_size,) containing the cross-entropy loss of each samples.
         """
-        all_logps = self._get_batch_logps(
+        all_logps = self.get_batch_logps(
             chosen_logits,
             chosen_labels,
             average_log_prob=True
@@ -89,7 +89,7 @@ class CustomDPOTrainer(DPOTrainer):
             return_dict=True
         ).logits.to(torch.float32)
 
-        all_logps = self._get_batch_logps(
+        all_logps = self.get_batch_logps(
             all_logits,
             batch["labels"],
             average_log_prob=False