diff --git a/src/llamafactory/train/kto/trainer.py b/src/llamafactory/train/kto/trainer.py
index 3f1220a9..7c0343f5 100644
--- a/src/llamafactory/train/kto/trainer.py
+++ b/src/llamafactory/train/kto/trainer.py
@@ -4,7 +4,6 @@ from types import MethodType
 from typing import TYPE_CHECKING, Dict, Literal, Optional, Tuple, Union
 
 import torch
-from torch.utils.data import RandomSampler
 from transformers import Trainer
 from trl import KTOTrainer
 from trl.trainer import disable_dropout_in_model
@@ -14,6 +13,7 @@ from ..utils import create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
+    import torch.utils.data
     from transformers import PreTrainedModel, ProcessorMixin
 
     from ...hparams import FinetuningArguments
@@ -85,6 +85,12 @@ class CustomKTOTrainer(KTOTrainer):
         create_custom_scheduler(self.args, num_training_steps, optimizer)
         return super().create_scheduler(num_training_steps, optimizer)
 
+    def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
+        r"""
+        Replaces the sequential sampler of KTO Trainer created by trl with the random sampler.
+        """
+        return Trainer._get_train_sampler(self)
+
     def _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None:
         super()._save(output_dir, state_dict)
         if self.processor is not None:
@@ -174,21 +180,6 @@ class CustomKTOTrainer(KTOTrainer):
 
         return reference_chosen_logps, reference_rejected_logps, reference_kl_logps
 
-    def has_length(self,dataset):
-        """
-        Checks if the dataset implements __len__() and it doesn't raise an error
-        """
-        try:
-            return len(dataset) is not None
-        except TypeError:
-            # TypeError: len() of unsized object
-            return False
-
-    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
-        if self.train_dataset is None or not self.has_length(self.train_dataset):
-            return None
-        return RandomSampler(self.train_dataset)
-
     def get_batch_loss_metrics(
         self,
         model: "PreTrainedModel",