diff --git a/verl/workers/sharding_manager/fsdp_sglang.py b/verl/workers/sharding_manager/fsdp_sglang.py
index eec297d2862..be74bbd41b8 100644
--- a/verl/workers/sharding_manager/fsdp_sglang.py
+++ b/verl/workers/sharding_manager/fsdp_sglang.py
@@ -32,6 +32,7 @@
 from verl.protocol import all_gather_data_proto
 from verl.utils.device import get_device_id, get_torch_device
 from verl.utils.fsdp_utils import fsdp_version, load_fsdp_model_to_gpu, offload_fsdp_model_to_cpu
+from verl.utils.model import convert_weight_keys
 from verl.utils.profiler import GPUMemoryLogger, log_gpu_memory_usage, simple_timer
 from verl.utils.torch_functional import check_device_is_available
 
@@ -145,6 +146,15 @@ async def release_memory(self):
     @GPUMemoryLogger(role="FSDPSGLangShardingManager enter", logger=logger)
     async def wake_up(self):
         get_torch_device().empty_cache()
+
+        if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.rollout_config.free_cache_engine:
+            if self.multi_stage_wake_up:
+                await self.inference_engine.resume_memory_occupation(tags=["weights"])
+                log_gpu_memory_usage("Before resume SGLang weights in sharding manager", logger=logger)
+            else:
+                await self.inference_engine.resume_memory_occupation()
+                log_gpu_memory_usage("Before resume SGLang weights + kv_cache in sharding manager", logger=logger)
+
         log_gpu_memory_usage("Before state_dict() in sharding manager memory", logger=logger)
         if self.offload_param:
             load_fsdp_model_to_gpu(self.module)
@@ -155,13 +165,8 @@ async def wake_up(self):
             k: v.to(device, non_blocking=True) if fsdp_version(self.module) == 2 else v for k, v in params.items()
         }
 
-        if self.device_mesh["infer_tp"].get_local_rank() == 0 and self.rollout_config.free_cache_engine:
-            if self.multi_stage_wake_up:
-                await self.inference_engine.resume_memory_occupation(tags=["weights"])
-                log_gpu_memory_usage("Before resume SGLang weights in sharding manager", logger=logger)
-            else:
-                await self.inference_engine.resume_memory_occupation()
-                log_gpu_memory_usage("Before resume SGLang weights + kv_cache in sharding manager", logger=logger)
+        # convert weight keys to match the model config
+        params = convert_weight_keys(params, getattr(self.module, "_fsdp_wrapped_module", self.module))
 
         # Copy, not share memory
         await self.update_weights(params)