diff --git a/skyrl/backends/skyrl_train/workers/megatron/megatron_worker.py b/skyrl/backends/skyrl_train/workers/megatron/megatron_worker.py
index 58061e9c3c..d237bd64d8 100644
--- a/skyrl/backends/skyrl_train/workers/megatron/megatron_worker.py
+++ b/skyrl/backends/skyrl_train/workers/megatron/megatron_worker.py
@@ -999,7 +999,17 @@ async def _save_lora_adapters_and_sync(
             with open(os.path.join(lora_sync_path, "adapter_config.json"), "w", encoding="utf-8") as f:
                 json.dump(adapter_config, f, ensure_ascii=False, indent=4)
 
-            # Send LoRA disk loading request to inference engine.
+        # Sync after rank-0 disk write so all ranks see consistent state on
+        # Weka. Critically, this barrier is BEFORE the rank-0 HTTP fan-out to
+        # inference engines — fan-out to 4 engines on multi-engine configs can
+        # take >gloo-timeout (~10s) which would hang ranks 1-3 at the barrier.
+        torch.distributed.barrier()
+
+        # Rank-0 HTTP fan-out happens AFTER the barrier, so ranks 1-3 don't
+        # block on its duration. The caller's outer barrier
+        # (broadcast_to_inference_engines, line ~1029) still gates the next
+        # training phase on the fan-out completing.
+        if torch.distributed.get_rank() == 0:
             from skyrl.backends.skyrl_train.inference_servers.remote_inference_client import (
                 RemoteInferenceClient,
             )
@@ -1010,8 +1020,6 @@ async def _save_lora_adapters_and_sync(
                 lora_request = LoraLoadRequest(lora_path=lora_sync_path, lora_name=lora_name)
                 await inference_engine_client.update_named_weights(lora_request)
 
-        torch.distributed.barrier()
-
     async def broadcast_to_inference_engines(
         self,
         inference_engine_client: "InferenceEngineInterface",