diff --git a/chatlearn/models/fsdp_module.py b/chatlearn/models/fsdp_module.py
index 68e0a0a2..5e17f294 100644
--- a/chatlearn/models/fsdp_module.py
+++ b/chatlearn/models/fsdp_module.py
@@ -285,20 +285,22 @@ def model_setup(self):
             model.to_empty(device="cuda")
 
             # load real state dict
-            options = StateDictOptions(full_state_dict=True, cpu_offload=False, broadcast_from_rank0=True)
-
-            # module-wise sync avoid OOM while run model like qwen3-moe-235B
-            for name, module in model.named_modules():
-                has_weights = any(k.startswith(name + ".") for k in full_state.keys()) and len(list(module.children()))==0
-                if has_weights:
-                    set_model_state_dict(
-                        module,
-                        {k.replace(name + ".", ""): v for k, v in full_state.items() if k.startswith(name + ".")},
-                        options=options
-                    )
-            # set_model_state_dict(model, full_state, options=options)
-
-            # load buffer data
+            options = StateDictOptions(full_state_dict=True, cpu_offload=False, broadcast_from_rank0=True, strict=False)
+
+            # bucket-wise sync avoid OOM while run model like qwen3-moe-235B
+            update_bucket = {}
+            bucket_size = 3 * 1024 ** 3
+            numel_cnt = 0
+            for name, param in full_state.items():
+                numel_cnt += param.numel()
+                update_bucket[name] = full_state[name]
+                if numel_cnt >= bucket_size:
+                    set_model_state_dict(model, update_bucket, options=options)
+                    update_bucket = {}
+                    numel_cnt = 0
+            set_model_state_dict(model, update_bucket, options=options)
+
+            # load buffer data because persistent maybe False
             if dist.get_rank()==0:
                 for name, buf in model.named_buffers():
                     buf.data.copy_(buffer_dict[name])
@@ -323,7 +325,7 @@ def model_setup(self):
         # resume model weights
         if self.resume_training:
             self.load_checkpoint(self._episode_id)
-        del full_state
+        del full_state, update_bucket
         self.offload()
 
     def get_fsdp_param_name(self, block_size=3_000_000_000) -> List[List]: