Fully Asynchronous Rollout Example

This example shows a simple way to make rollout generation fully asynchronous: a single global worker is created once and then keeps running in the background, continuously pulling prompts and launching generation tasks. Training only needs to fetch already finished results. This removes the per‑step wait that happens in the normal synchronous style.

Files

• fully_async_rollout.py: global async worker + generate_rollout_fully_async entry.
• run-qwen3-4b-fully_async.sh: example launch script with Qwen3‑4B.

Prerequisite

First set up model & environment following the Qwen3-4B example.

Quick Start

cd slime
bash examples/fully_async/run-qwen3-4b-fully_async.sh

You should see log lines like:

Creating new global async worker...
Continuous async rollout worker started

How It Works (Very Short)

• First call: create AsyncRolloutWorker (thread + asyncio loop).
• Loop keeps up to --rollout-batch-size tasks in flight using generate_and_rm_group.
• Completed groups are pushed into a queue; caller drains until it has enough samples.
• Worker is stopped automatically at process exit.

Limitations

• No evaluation mode.
• Ordering is best effort (sorted at the end by index).
• Minimal error handling.

Config Differences (2 Key Points)

To enable the fully async pattern there are only two changes compared to a normal run:

1. Use the async training driver: train_async.py (not train.py).
2. Set the rollout function path:

   --rollout-function-path fully_async_rollout.generate_rollout_fully_async
   

Why is it still "fully" async although train_async.py itself schedules rollouts step‑by‑step?

Because the real generation work is done by a persistent background worker created in generate_rollout_fully_async. Each call from train_async.py only drains already completed samples from the worker's output queue; the worker has been continuously generating since the first call. Thus rollout production (model inference) and training consume happen in parallel with minimal waiting.