diff --git a/src/llamafactory/train/kto/trainer.py b/src/llamafactory/train/kto/trainer.py
index 096fd935..292e61c7 100644
--- a/src/llamafactory/train/kto/trainer.py
+++ b/src/llamafactory/train/kto/trainer.py
@@ -1,7 +1,7 @@
 from collections import defaultdict
 from contextlib import nullcontext
 from types import MethodType
-from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
 
 import torch
 from transformers import Trainer
@@ -101,42 +101,39 @@ class CustomKTOTrainer(KTOTrainer):
         return -all_logps
 
     def forward(
-        self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
-    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
-        with torch.no_grad():
-            kl_model_inputs = {"input_ids": batch["kl_input_ids"], "attention_mask": batch["kl_attention_mask"]}
-            if "pixel_values" in batch:
-                kl_model_inputs["pixel_values"] = batch["pixel_values"]
-
-            if "kl_token_type_ids" in batch:
-                kl_model_inputs["token_type_ids"] = batch["kl_token_type_ids"]
-
-            kl_logits = model(**kl_model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
-
-        model_inputs = {"input_ids": batch["input_ids"], "attention_mask": batch["attention_mask"]}
+        self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"], prefix: Literal["", "kl_"] = ""
+    ) -> Tuple["torch.Tensor", "torch.Tensor"]:
+        r"""
+        Runs forward pass and computes the log probabilities.
+        """
+        batch = {k: v.detach().clone() for k, v in batch.items()}  # avoid error
+        model_inputs = {
+            "input_ids": batch["{}input_ids".format(prefix)],
+            "attention_mask": batch["{}attention_mask".format(prefix)],
+        }
         if "pixel_values" in batch:
             model_inputs["pixel_values"] = batch["pixel_values"]
 
-        if "token_type_ids" in batch:
-            model_inputs["token_type_ids"] = batch["token_type_ids"]
+        if "{}token_type_ids".format(prefix) in batch:
+            model_inputs["token_type_ids"] = batch["{}token_type_ids".format(prefix)]
 
-        target_logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
+        logits = model(**model_inputs, return_dict=True, use_cache=False).logits.to(torch.float32)
 
-        target_logps = self.get_batch_logps(
-            logits=target_logits,
-            labels=batch["labels"],
+        logps = self.get_batch_logps(
+            logits=logits,
+            labels=batch["{}labels".format(prefix)],
             average_log_prob=False,
             is_encoder_decoder=self.is_encoder_decoder,
             label_pad_token_id=self.label_pad_token_id,
         )
+        return logits, logps
 
-        kl_logps = self.get_batch_logps(
-            logits=kl_logits,
-            labels=batch["kl_labels"],
-            average_log_prob=False,
-            is_encoder_decoder=self.is_encoder_decoder,
-            label_pad_token_id=self.label_pad_token_id,
-        )
+    def concatenated_forward(
+        self, model: "PreTrainedModel", batch: Dict[str, "torch.Tensor"]
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor"]:
+        target_logits, target_logps = self.forward(model, batch)
+        with torch.no_grad():
+            _, kl_logps = self.forward(model, batch, prefix="kl_")
 
         if len(target_logps) != len(batch["kto_tags"]):
             raise ValueError("Mismatched shape of inputs and labels.")
@@ -152,6 +149,30 @@ class CustomKTOTrainer(KTOTrainer):
 
         return chosen_logps, rejected_logps, chosen_logits, rejected_logits, kl_logps
 
+    def compute_reference_log_probs(
+        self, batch: Dict[str, "torch.Tensor"]
+    ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]:
+        r"""
+        Computes log probabilities of the reference model.
+        """
+        if self.ref_model is None:
+            ref_model = self.model
+            ref_context = self.accelerator.unwrap_model(self.model).disable_adapter()
+        else:
+            ref_model = self.ref_model
+            ref_context = nullcontext()
+
+        with torch.no_grad(), ref_context:
+            (
+                reference_chosen_logps,
+                reference_rejected_logps,
+                _,
+                _,
+                reference_kl_logps,
+            ) = self.concatenated_forward(ref_model, batch)
+
+        return reference_chosen_logps, reference_rejected_logps, reference_kl_logps
+
     def get_batch_loss_metrics(
         self,
         model: "PreTrainedModel",
@@ -167,25 +188,9 @@ class CustomKTOTrainer(KTOTrainer):
             policy_chosen_logits,
             _,
             policy_kl_logps,
-        ) = self.forward(model, batch)
-
-        with torch.no_grad():
-            if self.ref_model is None:
-                ref_model = self.model
-                ref_context = self.accelerator.unwrap_model(self.model).disable_adapter()
-            else:
-                ref_model = self.ref_model
-                ref_context = nullcontext()
-
-            with ref_context:
-                (
-                    reference_chosen_logps,
-                    reference_rejected_logps,
-                    _,
-                    _,
-                    reference_kl_logps,
-                ) = self.forward(ref_model, batch)
+        ) = self.concatenated_forward(model, batch)
 
+        reference_chosen_logps, reference_rejected_logps, reference_kl_logps = self.compute_reference_log_probs(batch)
         losses, chosen_rewards, rejected_rewards, kl = self.kto_loss(
             policy_chosen_logps,
             policy_rejected_logps,