diff --git a/nemo_rl/models/generation/vllm/vllm_worker.py b/nemo_rl/models/generation/vllm/vllm_worker.py
index ed3f045274e..7f35ad010e7 100644
--- a/nemo_rl/models/generation/vllm/vllm_worker.py
+++ b/nemo_rl/models/generation/vllm/vllm_worker.py
@@ -1269,7 +1269,7 @@ def reset_prefix_cache(self):
                 "reset_prefix_cache can only be used with async_engine=False. Use reset_prefix_cache_async instead."
             )
 
-        self.llm.llm_engine.reset_prefix_cache()
+        self.llm.llm_engine.reset_prefix_cache(reset_connector=True)
         gc.collect()
         torch.cuda.empty_cache()
 
@@ -1285,7 +1285,7 @@ def sleep(self):
             )
 
         # Reset the prefix cache to ensure that prefix cache is not reused after weights are updated
-        self.llm.llm_engine.reset_prefix_cache()
+        self.llm.llm_engine.reset_prefix_cache(reset_connector=True)
         # Clear the renderer's multimodal processor cache (sender side) so it
         # stays in sync with the receiver cache that vLLM clears internally
         # during sleep.  Without this, the sender thinks images are already
diff --git a/nemo_rl/models/generation/vllm/vllm_worker_async.py b/nemo_rl/models/generation/vllm/vllm_worker_async.py
index 201d0790044..f3e581fd2be 100644
--- a/nemo_rl/models/generation/vllm/vllm_worker_async.py
+++ b/nemo_rl/models/generation/vllm/vllm_worker_async.py
@@ -188,6 +188,13 @@ def _create_engine(self, llm_kwargs: dict[str, Any]) -> None:
                 )
             llm_kwargs["compilation_config"] = CompilationConfig(**compilation_config)
 
+        if isinstance(llm_kwargs.get("kv_transfer_config"), dict):
+            from vllm.config import KVTransferConfig
+
+            llm_kwargs["kv_transfer_config"] = KVTransferConfig(
+                **llm_kwargs["kv_transfer_config"]
+            )
+
         self.llm_async_engine_args = AsyncEngineArgs(**llm_kwargs)
         self.stat_loggers = (
             [PrometheusStatLogger]
@@ -1459,7 +1466,7 @@ async def reset_prefix_cache_async(self):
                 "reset_prefix_cache_async can only be used with async_engine=True. Use reset_prefix_cache instead."
             )
 
-        await self.llm.reset_prefix_cache()
+        await self.llm.reset_prefix_cache(reset_connector=True)
         gc.collect()
         torch.cuda.empty_cache()
 
@@ -1475,7 +1482,7 @@ async def sleep_async(self):
             )
 
         # Reset the prefix cache to ensure that prefix cache is not reused after weights are updated
-        await self.llm.reset_prefix_cache()
+        await self.llm.reset_prefix_cache(reset_connector=True)
         # Reset the multimodal processor cache (sender side) so it stays in
         # sync with the receiver cache that vLLM clears internally during
         # sleep.  Without this, the sender thinks images are already cached on