diff --git a/dspy/teleprompt/grpo.py b/dspy/teleprompt/grpo.py
index 7998d2608d..9a8165df4a 100644
--- a/dspy/teleprompt/grpo.py
+++ b/dspy/teleprompt/grpo.py
@@ -201,7 +201,7 @@ def update_shuffled_trainset(self, original_trainset):
         for id in self.shuffled_trainset_ids:
             self.id_freqs[id] += 1
 
-        num_to_pad = self.num_dspy_examples_per_grpo_step - (len(original_trainset) % self.num_dspy_examples_per_grpo_step)
+        num_to_pad = (self.num_dspy_examples_per_grpo_step - (len(original_trainset) % self.num_dspy_examples_per_grpo_step)) % self.num_dspy_examples_per_grpo_step
         if num_to_pad > 0:
             # Select ids based on least frequent ids
             for _ in range(num_to_pad):
diff --git a/tests/teleprompt/test_grpo.py b/tests/teleprompt/test_grpo.py
index 0c2e61b509..4af4615118 100644
--- a/tests/teleprompt/test_grpo.py
+++ b/tests/teleprompt/test_grpo.py
@@ -61,8 +61,55 @@ def test_grpo_dataset_shuffler_with_num_ex_per_step_greater_dataset():
         assert counter[i] == 10
 
 
+def test_grpo_dataset_shuffler_no_padding_when_divisible():
+    dataset = [1, 2, 3, 4, 5, 6]
+    grpo = GRPO(
+        num_dspy_examples_per_grpo_step=3,
+        exclude_demos=True,
+    )
+
+    grpo.select_training_sample_and_update_shuffled_trainset(dataset, 0)
+
+    assert len(grpo.shuffled_trainset_ids) == len(dataset)
+    assert len(grpo.shuffled_trainset_ids) % grpo.num_dspy_examples_per_grpo_step == 0
+    assert grpo.id_freqs.total() == len(dataset)
+    assert all(v == 1 for v in grpo.id_freqs.values())
+
+
+def test_grpo_dataset_shuffler_across_epoch_boundary_divisible():
+    from collections import Counter
+
+    dataset = [1, 2, 3, 4, 5, 6]
+    grpo = GRPO(
+        num_dspy_examples_per_grpo_step=3,
+        exclude_demos=True,
+    )
+
+    batches, epochs = [], []
+    for i in range(6):
+        batch = grpo.select_training_sample_and_update_shuffled_trainset(dataset, i)
+        batches.append(batch)
+        epochs.append(grpo.epoch)
+
+    assert epochs == [0, 0, 1, 1, 2, 2]
+    assert len(grpo.shuffled_trainset_ids) == len(dataset)
+
+    epoch_to_ids = {}
+    for batch, epoch in zip(batches, epochs, strict=True):
+        epoch_to_ids.setdefault(epoch, []).extend(batch)
+
+    for epoch, ids in epoch_to_ids.items():
+        assert len(ids) == len(set(ids)), f"epoch {epoch} contains duplicate ids: {Counter(ids)}"
+        assert set(ids) == set(dataset), f"epoch {epoch} contains unexpected ids: {set(ids)}"
+
+    assert Counter(epoch_to_ids[0]) == Counter(dataset)
+    assert Counter(epoch_to_ids[1]) == Counter(dataset)
+
+
 if __name__ == "__main__":
     test_grpo_dataset_shuffler()
     test_grpo_dataset_shuffler_with_num_ex_per_step_less_dataset()
     test_grpo_dataset_shuffler_with_num_ex_per_step_greater_dataset()
+    test_grpo_dataset_shuffler_no_padding_when_divisible()
+    test_grpo_dataset_shuffler_across_epoch_boundary_divisible()
     print("All tests passed!")