diff --git a/vllm/worker/multi_step_model_runner.py b/vllm/worker/multi_step_model_runner.py
index 1cd3a5470673f..97696a4bda34c 100644
--- a/vllm/worker/multi_step_model_runner.py
+++ b/vllm/worker/multi_step_model_runner.py
@@ -313,7 +313,6 @@ def execute_model(
 
             # event for the pythonization so that we only pythonize if the
             # tensors are ready. May be able to be combined with the step event
-            # torch.cuda.synchronize()
             output_ready_event = torch.cuda.Event()
             output_ready_event.record(current_stream)
             if self.parallel_config.pipeline_parallel_size > 1:
diff --git a/vllm/worker/multi_step_worker.py b/vllm/worker/multi_step_worker.py
index 1d0e9c8ef9da8..0b1342347bc5a 100644
--- a/vllm/worker/multi_step_worker.py
+++ b/vllm/worker/multi_step_worker.py
@@ -135,17 +135,12 @@ def prepare_input(
                 pass
                 # cache the worker input and model input for the next steps
                 # TODO(will) see below
-
-                # self.multi_step_states[virtual_engine] = MultiStepState(
-                #     worker_input=worker_input, model_input=model_input)
             else:
-                # TODO(will) possible to also use the cached worker input and model input
-                # this can be done if we want to optimize the broadcast to only send
-                # the last sampled token ids for non-first multi steps
+                # TODO(will) possible to also use the cached worker input and
+                # model input this can be done if we want to optimize the
+                # broadcast to only send the last sampled token ids for
+                # non-first multi steps
 
-                # multi_step_state = self.multi_step_states[virtual_engine]
-                # cached_model_input = multi_step_state.model_input
-                # cached_worker_input = multi_step_state.worker_input
                 assert isinstance(
                     model_input, MutableModelInputForGPUWithMultiStepMetadata)
                 # we need to update the last sampled token ids in the model input
@@ -153,10 +148,6 @@ def prepare_input(
                 model_input.add_sampler_output(
                     SamplerOutput(outputs=[], sampled_token_ids=None),
                     model_input.last_sampled_token_ids)
-                # self.multi_step_states[virtual_engine] = MultiStepState(
-                #     worker_input=worker_input, model_input=model_input)
-                # model_input = cached_model_input
-                # worker_input = cached_worker_input
 
         assert model_input is not None
         assert worker_input is not None