diff --git a/trl/trainer/grpo_trainer.py b/trl/trainer/grpo_trainer.py
index 04a7e79cb0..be5b6ffa7d 100644
--- a/trl/trainer/grpo_trainer.py
+++ b/trl/trainer/grpo_trainer.py
@@ -13,11 +13,10 @@
 # limitations under the License.
 
 import os
-import random
 import textwrap
 import warnings
 from collections import defaultdict
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, Optional, Sized, Union
 from unittest.mock import patch
 
 import torch
@@ -69,21 +68,32 @@
 class RepeatRandomSampler(Sampler):
     """
     Sampler that repeats the indices of a dataset N times.
+
+    Args:
+        data_source (`Sized`):
+            Dataset to sample from.
+        repeat_count (`int`):
+            Number of times to repeat each index.
+
+    Example:
+    ```python
+    >>> sampler = RepeatRandomSampler(["a", "b", "c", "d"], repeat_count=2)
+    >>> list(sampler)
+    [2, 2, 0, 0, 3, 3, 1, 1]
+    ```
     """
 
-    def __init__(self, data_source, repeat_count):
+    def __init__(self, data_source: Sized, repeat_count: int):
         self.data_source = data_source
         self.repeat_count = repeat_count
         self.num_samples = len(data_source)
 
     def __iter__(self):
-        while True:
-            index = random.randint(0, self.num_samples - 1)  # Pick a random index
-            for _ in range(self.repeat_count):
-                yield index  # Yield the same index N times
+        indexes = [idx for idx in torch.randperm(self.num_samples).tolist() for _ in range(self.repeat_count)]
+        return iter(indexes)
 
     def __len__(self):
-        return self.num_samples * self.repeat_count  # Theoretically infinite, but define a max length if needed
+        return self.num_samples * self.repeat_count
 
 
 def broadcast_and_slice_dict(accelerator: Accelerator, tensor_dict: dict[str, Tensor], from_process: int = 0):