diff --git a/tests/moe/test_expert_parallel_qwen3_fsdp_sp.py b/tests/moe/test_expert_parallel_qwen3_fsdp_sp.py
index e46573ff..a2031d14 100644
--- a/tests/moe/test_expert_parallel_qwen3_fsdp_sp.py
+++ b/tests/moe/test_expert_parallel_qwen3_fsdp_sp.py
@@ -260,13 +260,6 @@ def _compare_grad_dicts(
         b32 = b.to(dtype=torch.float32)
         diff = b32 - a32
         rel = diff.norm() / (a32.norm() + 1e-12)
-        if rel.item() > rel_tol:
-            abs_diff = diff.abs()
-            print(
-                f'[rank{rank}] {k} grad diff mean={abs_diff.mean().item():.6e} '
-                f'max={abs_diff.max().item():.6e} rel_norm={rel.item():.6e} tol={rel_tol:.1e}',
-                flush=True,
-            )
         assert rel.item() <= rel_tol
 
 
@@ -414,7 +407,8 @@ def _run_worker_ep_fsdp_sp_align(
         model_sp, _ = fsdp_strategy.wrap_model(model_sp, optimizer=None)
 
         # Preprocess labels through SP strategy so they are shifted + split consistently.
-        sp_label_inputs = {'labels': labels_raw, 'position_ids': position_ids}
+        # Keep label semantics consistent with the baseline path: next-token aligned labels.
+        sp_label_inputs = {'labels': labels_shifted, 'position_ids': position_ids}
         sp_label_inputs = sp_strategy.preprocess_inputs(sp_label_inputs)
         sp_local_labels = sp_label_inputs['labels']
 
@@ -433,10 +427,7 @@ def _run_worker_ep_fsdp_sp_align(
         sp_logits = sp_out.logits.detach()
 
         # Forward alignment (full-seq logits reconstructed by SP gather).
-        if not torch.allclose(sp_logits, base_logits, rtol=1e-3, atol=1e-4):
-            diff = (sp_logits - base_logits).abs()
-            raise AssertionError(f'[rank{rank}] logits not close: mean_abs={diff.mean().item():.6e} '
-                                 f'max_abs={diff.max().item():.6e} (rtol=1e-3, atol=1e-4)')
+        assert torch.allclose(sp_logits, base_logits, rtol=1e-3, atol=1e-4)
 
         # Router alignment on this rank's slice: compare selected experts exactly.
         # SP captures only local tokens; baseline captures full tokens (we slice it).
@@ -448,9 +439,6 @@ def _run_worker_ep_fsdp_sp_align(
             sp_sel = sp_state['selected_experts']
             if sp_sel.dim() == 2:
                 sp_sel = sp_sel.view(batch_size, end - start, -1)
-            if not torch.equal(base_sel, sp_sel):
-                mismatch = (base_sel != sp_sel).sum().item()
-                print(f'[rank{rank}] block[{idx}] selected_experts mismatch count={mismatch}', flush=True)
             assert torch.equal(base_sel, sp_sel)
 
         # Backward alignment (expert grads on active local experts for this slice).
@@ -609,7 +597,8 @@ def _run_worker_fsdp_sp_align(
         sp_embeds = embed_sp(input_ids).detach().requires_grad_(True)
         model_sp, _ = fsdp_strategy.wrap_model(model_sp, optimizer=None)
 
-        sp_label_inputs = {'labels': labels_raw, 'position_ids': position_ids}
+        # Keep label semantics consistent with the baseline path: next-token aligned labels.
+        sp_label_inputs = {'labels': labels_shifted, 'position_ids': position_ids}
         sp_label_inputs = sp_strategy.preprocess_inputs(sp_label_inputs)
         sp_local_labels = sp_label_inputs['labels']
 
@@ -625,12 +614,6 @@ def _run_worker_fsdp_sp_align(
         sp_logits = sp_out.logits.detach()
 
         # Forward alignment (full-seq logits reconstructed by SP gather).
-        if not torch.allclose(sp_logits, base_logits, rtol=1e-3, atol=1e-4):
-            diff = (sp_logits - base_logits).abs()
-            print(
-                f'[rank{rank}] logits diff mean={diff.mean().item():.6e} max={diff.max().item():.6e}',
-                flush=True,
-            )
         assert torch.allclose(sp_logits, base_logits, rtol=1e-3, atol=1e-4)
 
         # Backward alignment: local CE(sum) on SP, compare gathered full-seq inputs_embeds grads.
@@ -651,11 +634,6 @@ def _run_worker_fsdp_sp_align(
         diff = sp_full - base_full
         rel = diff.norm() / (base_full.norm() + 1e-12)
         grad_rel_tol = float(os.environ.get('TWINKLE_INPUT_GRAD_REL_TOL', '1e-2'))
-        if rel.item() > grad_rel_tol:
-            abs_diff = diff.abs()
-            raise AssertionError(
-                f'[rank{rank}] inputs_embeds.grad(full) not close: mean_abs={abs_diff.mean().item():.6e} '
-                f'max_abs={abs_diff.max().item():.6e} rel_norm={rel.item():.6e} tol={grad_rel_tol:.1e}')
         assert rel.item() <= grad_rel_tol
     finally:
         dist.destroy_process_group()