<ul data-eligibleForWebStory="false"><li>AsyncFlow is an asynchronous streaming RL framework designed for efficient post-training of large language models.</li><li>It aims to address scalability bottlenecks faced by traditional RL frameworks and challenges in complex dataflows, resource idling, and workload imbalance.</li><li>AsyncFlow introduces distributed data storage and transfer modules, automated pipeline overlapping, and producer-consumer-based asynchronous workflows for improved computational efficiency.</li><li>The framework is decoupled from underlying training and inference engines, allowing for modular and customizable user experiences. Extensive experiments have shown a significant throughput improvement compared to existing baselines.</li></ul>

AsyncFlow: An Asynchronous Streaming RL Framework for Efficient LLM Post-Training

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