diff --git a/swift/sequence_parallel/sequence_parallel.py b/swift/sequence_parallel/sequence_parallel.py
index 4b27488721..8655a32067 100644
--- a/swift/sequence_parallel/sequence_parallel.py
+++ b/swift/sequence_parallel/sequence_parallel.py
@@ -332,7 +332,12 @@ def moe_aux_loss_hook(module, args, kwargs, output):
                 _gathered_logits.append(_bs_logits)
             router_logits = torch.stack(_gathered_logits, dim=0)
             if self.real_position_ids is not None:
-                router_logits = router_logits[:, :, :self.real_position_ids.shape[1], :]
+                if self.rp_world_size > 1:
+                    position_ids = self.pad(
+                        self.real_position_ids, padding_value=-1, position_ids=self.real_position_ids)
+                    router_logits = router_logits[:, :, position_ids[0] >= 0, :]
+                else:
+                    router_logits = router_logits[:, :, :self.real_position_ids.shape[1], :]
             output['router_logits'] = tuple(
                 [logit.reshape(-1, logit.shape[-1]) for logit in router_logits.split(1, dim=1)])
             return output
diff --git a/tests/sequence_parallel/test_moe_aux_loss.py b/tests/sequence_parallel/test_moe_aux_loss.py
new file mode 100644
index 0000000000..a1c86c5533
--- /dev/null
+++ b/tests/sequence_parallel/test_moe_aux_loss.py
@@ -0,0 +1,62 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from datetime import timedelta
+from torch.distributed import init_device_mesh
+from transformers.modeling_outputs import MoeModelOutputWithPast
+from transformers.models.qwen3_moe.modeling_qwen3_moe import load_balancing_loss_func
+
+from swift.sequence_parallel import sequence_parallel
+
+
+class RouterOutputModel(torch.nn.Module):
+
+    def forward(self, router_logits, attention_mask=None):
+        return MoeModelOutputWithPast(router_logits=router_logits)
+
+
+def _check_moe_aux_loss(rank, rendezvous, ring_size, sequence_size):
+    world_size = ring_size * sequence_size
+    dist.init_process_group(
+        'gloo', init_method=rendezvous, rank=rank, world_size=world_size, timeout=timedelta(seconds=60))
+    try:
+        sp = sequence_parallel
+        sp.world_size = world_size
+        sp.rp_world_size = ring_size
+        sp.sp_world_size = sequence_size
+        sp.device_mesh = init_device_mesh(
+            'cpu', (1, ring_size, sequence_size), mesh_dim_names=('data', 'ring', 'sequence'))
+        model = RouterOutputModel()
+        sp._prepare_moe_aux_loss(model)
+        # Internal padding, tail-only padding, aligned sequences, and a one-token sample.
+        for lengths in ([3, 5], [8, 3], [8, 8], [1, 3, 5], [5]):
+            positions = torch.cat([torch.arange(length) for length in lengths]).unsqueeze(0)
+            sp.extra_kwargs['text_position_ids'] = positions
+            padded_positions = sp.pad(positions, padding_value=-1, position_ids=positions)
+            reference = torch.randn(2, sum(lengths), 4, generator=torch.Generator().manual_seed(42))
+            reference.requires_grad_()
+            padded = sp.pad(reference.detach(), padding_value=0, position_ids=positions)
+            local = sp.split(padded, dim=1, position_ids=padded_positions).detach().requires_grad_()
+            output = model(tuple(local.unbind(0)), attention_mask=None)
+            actual = torch.stack(output.router_logits)
+            torch.testing.assert_close(actual, reference)
+            actual_loss = load_balancing_loss_func(output.router_logits, num_experts=4, top_k=2)
+            expected_loss = load_balancing_loss_func(tuple(reference.unbind(0)), num_experts=4, top_k=2)
+            torch.testing.assert_close(actual_loss, expected_loss)
+            actual_loss.backward()
+            expected_loss.backward()
+            padded_grad = sp.pad(reference.grad, padding_value=0, position_ids=positions)
+            expected_grad = sp.split(padded_grad, dim=1, position_ids=padded_positions) * world_size
+            # GatherLoss compensates for the subsequent distributed gradient average.
+            torch.testing.assert_close(local.grad, expected_grad)
+    finally:
+        dist.destroy_process_group()
+
+
+@pytest.mark.skipif(not dist.is_available() or not dist.is_gloo_available(), reason='Gloo is not available')
+@pytest.mark.parametrize(('ring_size', 'sequence_size'), [(2, 1), (2, 2), (1, 2), (1, 1)])
+def test_moe_aux_loss_excludes_sequence_parallel_padding(tmp_path, ring_size, sequence_size):
+    rendezvous = (tmp_path / 'rendezvous').as_uri()
+    mp.spawn(_check_moe_aux_loss, args=(rendezvous, ring_size, sequence_size), nprocs=ring_size * sequence_size)