diff --git a/python/sglang/srt/model_executor/expert_location_updater.py b/python/sglang/srt/model_executor/expert_location_updater.py
index 52cc13524..75fd7c2d7 100644
--- a/python/sglang/srt/model_executor/expert_location_updater.py
+++ b/python/sglang/srt/model_executor/expert_location_updater.py
@@ -61,7 +61,62 @@ class ExpertLocationUpdater:
         )
 
 
-def _update_expert_weights(
+def _update_expert_weights(**kwargs):
+    if get_bool_env_var("SGLANG_EXPERT_LOCATION_UPDATER_CANARY"):
+        return _update_expert_weights_with_canary(**kwargs)
+    else:
+        return _update_expert_weights_raw(**kwargs)
+
+
+# can add watchdog as well
+def _update_expert_weights_with_canary(
+    routed_experts_weights_of_layer: Dict[int, List[torch.Tensor]],
+    old_expert_location_metadata: ExpertLocationMetadata,
+    new_expert_location_metadata: ExpertLocationMetadata,
+    update_layer_ids: List[int],
+    nnodes: int,
+    rank: int,
+):
+    num_local_physical_experts = old_expert_location_metadata.num_local_physical_experts
+
+    def _get_canary_value(meta: ExpertLocationMetadata, layer_id: int):
+        return meta.physical_to_logical_map_cpu[
+            layer_id,
+            num_local_physical_experts * rank : num_local_physical_experts * (rank + 1),
+        ]
+
+    routed_experts_weights_of_layer = {
+        k: [x for x in v] for k, v in routed_experts_weights_of_layer.items()
+    }
+    for layer_id in update_layer_ids:
+        canary_tensor = (
+            _get_canary_value(old_expert_location_metadata, layer_id)
+            .clone()
+            .to(device=global_server_args_dict["device"], non_blocking=True)
+        )
+        routed_experts_weights_of_layer[layer_id].append(canary_tensor)
+
+    _update_expert_weights_raw(
+        routed_experts_weights_of_layer=routed_experts_weights_of_layer,
+        old_expert_location_metadata=old_expert_location_metadata,
+        new_expert_location_metadata=new_expert_location_metadata,
+        update_layer_ids=update_layer_ids,
+        nnodes=nnodes,
+        rank=rank,
+    )
+
+    for layer_id in update_layer_ids:
+        # can optimize speed if needed
+        expect_value = _get_canary_value(new_expert_location_metadata, layer_id)
+        actual_value = routed_experts_weights_of_layer[layer_id][-1].cpu()
+        assert torch.all(expect_value == actual_value), (
+            f"{expect_value=} {actual_value=} {layer_id=} "
+            f"{old_expert_location_metadata.physical_to_logical_map_cpu.tolist()=} "
+            f"{new_expert_location_metadata.physical_to_logical_map_cpu.tolist()=} "
+        )
+
+
+def _update_expert_weights_raw(
     routed_experts_weights_of_layer: Dict[int, List[torch.Tensor]],
     old_expert_location_metadata: ExpertLocationMetadata,
     new_expert_location_metadata: ExpertLocationMetadata,