diff --git a/megatron/core/optimizer/optimizer.py b/megatron/core/optimizer/optimizer.py
index 61979a50b6c..64b2cd36835 100644
--- a/megatron/core/optimizer/optimizer.py
+++ b/megatron/core/optimizer/optimizer.py
@@ -101,7 +101,7 @@ class MegatronOptimizer(ABC):
     """
     Base class for all Megatron optimizers.
 
-    Provides a consistent interface for gradient management, parameter 
+    Provides a consistent interface for gradient management, parameter
     access, and state-dict handling across different optimization types.
 
     Args:
@@ -138,11 +138,10 @@ def get_parameters(self) -> List[torch.nn.Parameter]:
         return params
 
     def get_main_grads_for_grad_norm(self) -> List[torch.Tensor]:
-        
         """Collects gradients for norm calculation, filtering duplicates.
 
-        This method filters parameters based on whether the gradient is not None, 
-        the parameter is not shared (to avoid double-counting gradients), and 
+        This method filters parameters based on whether the gradient is not None,
+        the parameter is not shared (to avoid double-counting gradients), and
         the parameter is not a replica due to tensor model parallelism.
 
         Returns:
@@ -1172,20 +1171,26 @@ def _split_state_dict(self, state_dict):
         state_dicts = [None] * len(self.chained_optimizers)
         if state_dict is not None:
             if len(self.model_chunks) == 1:
-                state_dicts[0] = state_dict
+                # When there is only one global model chunk, all sub-optimizers
+                # (e.g., dense and MoE parts) use the same model state dict.
+                state_dicts = [state_dict] * len(self.chained_optimizers)
             else:
-                # Split state_dict if needed
+                # Split state_dict by model chunk object.
                 prefix = "model" if "model0" in state_dict.keys() else "model_"
-                offset = 0
+                chunk_to_global_idx = {chunk: idx for idx, chunk in enumerate(self.model_chunks)}
                 for optimizer_idx, optimizer in enumerate(self.chained_optimizers):
                     if hasattr(optimizer, "model_chunks"):
                         d = {}
-                        for chunk_idx in range(len(optimizer.model_chunks)):
+                        for chunk_idx, model_chunk in enumerate(optimizer.model_chunks):
+                            assert model_chunk in chunk_to_global_idx, (
+                                "Sub-optimizer model chunk was not found in "
+                                "chained optimizer model chunks"
+                            )
+                            global_idx = chunk_to_global_idx[model_chunk]
                             assert (
-                                f"{prefix}{offset}" in state_dict
-                            ), f"Wrong state_dict format, cannot find '{prefix}{offset}'"
-                            d[f"{prefix}{chunk_idx}"] = state_dict[f"{prefix}{offset}"]
-                            offset += 1
+                                f"{prefix}{global_idx}" in state_dict
+                            ), f"Wrong state_dict format, cannot find '{prefix}{global_idx}'"
+                            d[f"{prefix}{chunk_idx}"] = state_dict[f"{prefix}{global_idx}"]
                         if len(d) > 0:
                             state_dicts[optimizer_idx] = d
         return state_dicts