diff --git a/tests/test_utils.py b/tests/test_utils.py
index b95b0611a09..8570ea4bf57 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -28,7 +28,6 @@
     RepeatSampler,
     entropy_from_logits,
     flush_left,
-    flush_right,
     forward_masked_logits,
     generate_model_card,
     get_peft_config,
@@ -383,50 +382,6 @@ def test_no_tensors(self):
         assert torch.equal(new_mask, expected_mask)
 
 
-class TestFlushRight(TrlTestCase):
-    def test_basic_case(self):
-        mask = torch.tensor([[1, 1, 1, 0, 0], [0, 0, 1, 1, 0]])
-        tensor1 = torch.tensor([[2, 3, 4, 0, 0], [0, 0, 5, 6, 0]])
-        tensor2 = torch.tensor([[7, 8, 9, 0, 0], [0, 0, 10, 11, 0]])
-        new_mask, new_tensor1, new_tensor2 = flush_right(mask, tensor1, tensor2)
-
-        expected_mask = torch.tensor([[1, 1, 1], [0, 1, 1]])
-        expected_tensor1 = torch.tensor([[2, 3, 4], [0, 5, 6]])
-        expected_tensor2 = torch.tensor([[7, 8, 9], [0, 10, 11]])
-
-        assert torch.equal(new_mask, expected_mask)
-        assert torch.equal(new_tensor1, expected_tensor1)
-        assert torch.equal(new_tensor2, expected_tensor2)
-
-    def test_single_row(self):
-        mask = torch.tensor([[1, 1, 0, 0]])
-        tensor1 = torch.tensor([[2, 3, 0, 0]])
-        new_mask, new_tensor1 = flush_right(mask, tensor1)
-
-        expected_mask = torch.tensor([[1, 1]])
-        expected_tensor1 = torch.tensor([[2, 3]])
-
-        assert torch.equal(new_mask, expected_mask)
-        assert torch.equal(new_tensor1, expected_tensor1)
-
-    def test_no_shift_needed(self):
-        mask = torch.tensor([[0, 0, 1, 1], [0, 0, 0, 1]])
-        tensor1 = torch.tensor([[0, 0, 5, 6], [0, 0, 0, 7]])
-        new_mask, new_tensor1 = flush_right(mask, tensor1)
-
-        expected_mask = torch.tensor([[1, 1], [0, 1]])
-        expected_tensor1 = torch.tensor([[5, 6], [0, 7]])
-
-        assert torch.equal(new_mask, expected_mask)
-        assert torch.equal(new_tensor1, expected_tensor1)
-
-    def test_no_tensors(self):
-        mask = torch.tensor([[1, 1, 1, 0, 0], [0, 0, 1, 1, 0]])
-        new_mask = flush_right(mask)
-        expected_mask = torch.tensor([[1, 1, 1], [0, 1, 1]])
-        assert torch.equal(new_mask, expected_mask)
-
-
 class TestRepeatRandomSampler(TrlTestCase):
     def test_sampler(self):
         dataset = ["a", "b", "c", "d", "e", "f", "g"]
diff --git a/trl/trainer/utils.py b/trl/trainer/utils.py
index fe5bf86ca03..1db504acebe 100644
--- a/trl/trainer/utils.py
+++ b/trl/trainer/utils.py
@@ -492,36 +492,6 @@ def flush_left(mask: torch.Tensor, *tensors: torch.Tensor) -> torch.Tensor | tup
     return flushed_mask, *flushed_tensors
 
 
-def flush_right(mask: torch.Tensor, *tensors: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor, ...]:
-    """
-    Shift non-zero elements in the mask and corresponding tensors to the right. See `flush_left` for details.
-    """
-    _, M = mask.shape
-
-    # Create copy of mask and tensors
-    mask_copy = mask.clone()
-    tensors = [t.clone() for t in tensors]
-
-    # Shift non-zero values to the right
-    flipped_mask = torch.fliplr(mask_copy)
-    first_non_zero = flipped_mask.argmax(dim=1)
-    pos = torch.arange(M, device=mask_copy.device).unsqueeze(0)
-    idx_roll = (pos - first_non_zero.unsqueeze(1)) % M
-    mask_roll = mask_copy.gather(1, idx_roll)
-    rolled_tensors = [t.gather(1, idx_roll) for t in tensors]
-
-    # Truncate leading columns that are all zeros in mask_roll
-    col_sums = mask_roll.sum(dim=0)
-    non_empty_cols = col_sums != 0
-    first_non_empty_col = int(non_empty_cols.to(torch.int8).argmax()) if non_empty_cols.any() else M
-    flushed_mask = mask_roll[:, first_non_empty_col:]
-    flushed_tensors = [t[:, first_non_empty_col:] for t in rolled_tensors]
-
-    if not flushed_tensors:
-        return flushed_mask
-    return flushed_mask, *flushed_tensors
-
-
 def selective_log_softmax(logits, index) -> torch.Tensor:
     """
     A memory-efficient implementation of the common `log_softmax -> gather` operation.