diff --git a/roll/distributed/executor/cluster.py b/roll/distributed/executor/cluster.py
index 9c18b7c0c..84e624ddc 100644
--- a/roll/distributed/executor/cluster.py
+++ b/roll/distributed/executor/cluster.py
@@ -140,6 +140,9 @@ def _create_workers(self):
             if "ROLL_LOG_DIR" in os.environ:
                 env_vars["ROLL_LOG_DIR"] = os.environ["ROLL_LOG_DIR"]
             env_vars.update(self.worker_config.system_envs)
+            if current_platform.is_npu():
+                env_vars["HCCL_HOST_SOCKET_PORT_RANGE"] = "auto"
+                env_vars["HCCL_NPU_SOCKET_PORT_RANGE"] = "auto"
 
             runtime_env = RuntimeEnv(env_vars=env_vars)
             self.worker_config.resource_placement_groups = pgs
diff --git a/roll/third_party/deepspeed/model_update.py b/roll/third_party/deepspeed/model_update.py
index b6452902c..53e8784ce 100644
--- a/roll/third_party/deepspeed/model_update.py
+++ b/roll/third_party/deepspeed/model_update.py
@@ -29,9 +29,26 @@ def _gather_weights(is_zero3, named_params):
         return [(n, p.data) for n, p in named_params]
 
 
-def gather_deepspeed_weights(model, ds_config, buffer_size):
+def gather_deepspeed_weights(model, ds_config, buffer_size, is_lora=False):
     is_zero3 = ds_config.is_zero3()
-    named_params = [(name, param) for name, param in model.named_parameters()]
+    if is_lora:
+        if not is_zero3:
+            from peft.utils import get_peft_model_state_dict
+            lora_state_dict = get_peft_model_state_dict(model)
+            named_params = [(name, param) for name, param in lora_state_dict.items()]
+        else:
+            adapter_name = "default"
+            state_dict = model.state_dict()
+            lora_state_dict = {k: state_dict[k] for k in state_dict if ("lora_" in k and adapter_name in k)}
+            named_params = []
+            for name, param in lora_state_dict.items():
+                clean_name = name.replace(f".{adapter_name}", "")
+                if clean_name.startswith("base_model.model."):
+                    clean_name = clean_name[len("base_model.model."):]
+                named_params.append((clean_name, model.get_parameter(name)))
+            del lora_state_dict
+    else:
+        named_params = [(name, param) for name, param in model.named_parameters()]
 
     waiting_params, waiting_params_size = [], 0
     for name, param in named_params:
@@ -150,7 +167,7 @@ def _setup_broadcast_group(self):
     def _colocated_model_update(self):
         refs = []
         for named_weights in gather_deepspeed_weights(
-            self.model, self.ds_config, buffer_size=self._model_update_buffer_size
+            self.model, self.ds_config, buffer_size=self._model_update_buffer_size, is_lora=self.is_lora
         ):
             serialized_tensors = serialize_named_weights(
                 named_weights, infer_strategy=self.infer_worker_config.strategy_args.strategy_name
@@ -167,7 +184,7 @@ def _colocated_model_update(self):
                 ray.get(refs)
                 refs = []
             if co_infer_rank == 0 and self._co_infer_worker is not None:
-                refs.append(self._co_infer_worker.update_parameter_in_bucket.remote(infer_parallel_tensors))
+                refs.append(self._co_infer_worker.update_parameter_in_bucket.remote(infer_parallel_tensors, is_lora=self.is_lora))
             if self._broadcast_workers:
                 refs.extend(self._broadcast_to_infer_workers(named_weights))
         if refs:
@@ -183,6 +200,7 @@ def _broadcast_to_infer_workers(self, named_weights) -> list[ray.ObjectRef]:
                 names=[n for n, _ in named_weights],
                 dtypes=[w.dtype for _, w in named_weights],
                 shapes=[w.shape for _, w in named_weights],
+                is_lora=self.is_lora,
             )
             for worker in self._broadcast_workers
         ]
@@ -198,7 +216,7 @@ def _broadcast_to_infer_workers(self, named_weights) -> list[ray.ObjectRef]:
     def _separated_model_update(self):
         logger.info(f"start broadcast model update {self.model_update_group_name}")
         for named_weights in gather_deepspeed_weights(
-            self.model, self.ds_config, buffer_size=self._model_update_buffer_size
+            self.model, self.ds_config, buffer_size=self._model_update_buffer_size, is_lora=self.is_lora
         ):
             refs = self._broadcast_to_infer_workers(named_weights)
             ray.get(refs)