diff --git a/trl/trainer/grpo_trainer.py b/trl/trainer/grpo_trainer.py
index 7826b184488..678ab34449e 100644
--- a/trl/trainer/grpo_trainer.py
+++ b/trl/trainer/grpo_trainer.py
@@ -64,6 +64,12 @@
     selective_log_softmax,
 )
 
+try:
+    from torch.distributed.checkpoint.state_dict import get_model_state_dict
+
+    fsdp2_available = True
+except ImportError:
+    fsdp2_available = False
 
 if is_peft_available():
     from peft import PeftConfig, get_peft_model
@@ -925,6 +931,25 @@ def _sync_fsdp_params_to_vllm(self, module: nn.Module, prefix: str = "", visited
                     elif self.vllm_mode == "colocate":
                         llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
                         llm_model.load_weights([(full_name, param.data)])
+        elif isinstance(module, torch.distributed.fsdp.FSDPModule):
+            assert fsdp2_available, "FSDP2 is not available"
+
+            # Only run this logic at the root call (prefix is empty)
+            if prefix == "":
+                # Get the canonical state dict using the high-level torch.distributed.checkpoint API
+                model_state_dict = get_model_state_dict(module)
+
+                # Sync the state dict to vLLM
+                if self.vllm_mode == "server" and self.accelerator.is_main_process:
+                    for name, param in model_state_dict.items():
+                        self.vllm_client.update_named_param(name, param)
+                elif self.vllm_mode == "colocate":
+                    llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
+                    try:
+                        for k, v in model_state_dict.items():
+                            llm_model.load_weights([(k, v)])
+                    except ValueError:
+                        print(f"Error loading weights for {k} with shape {v.shape}")
 
     @profiling_decorator
     def _move_model_to_vllm(self):