diff --git a/recipes_source/distributed_async_checkpoint_recipe.rst b/recipes_source/distributed_async_checkpoint_recipe.rst
index e959883a25b..0cb22fc71d5 100644
--- a/recipes_source/distributed_async_checkpoint_recipe.rst
+++ b/recipes_source/distributed_async_checkpoint_recipe.rst
@@ -257,12 +257,14 @@ checkpoint requests users can take advantage of direct memory access to speed up
         for step in range(10):
             optimizer.zero_grad()
             model(torch.rand(8, 16, device="cuda")).sum().backward()
-            optimizer.step()
 
-            state_dict = { "app": AppState(model, optimizer) }
+            # Wait for the previous checkpoint to finish before optimizer.step() modifies weights in-place
             if checkpoint_future is not None:
-                # waits for checkpointing to finish, avoiding queuing more then one checkpoint request at a time
                 checkpoint_future.result()
+
+            optimizer.step()
+
+            state_dict = { "app": AppState(model, optimizer) }
             checkpoint_future = dcp.async_save(state_dict, storage_writer=writer, checkpoint_id=f"{CHECKPOINT_DIR}_step{step}")
 
         cleanup()