AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs
AstraFlow is a new dataflow-oriented reinforcement learning system designed for agentic large language models. It aims to improve the efficiency of training these models by decoupling various components and supporting complex workloads. The system has shown promising results, achieving faster training times and comparable accuracy to existing systems.
- ▪AstraFlow replaces conventional trainer-centered control with principled component abstractions.
- ▪The system supports multi-policy collaborative training and efficiently utilizes diverse compute resources.
- ▪AstraFlow achieves a training speedup of 2.7x while maintaining comparable accuracy to existing reinforcement learning systems.
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Computer Science > Machine Learning arXiv:2605.15565 (cs) [Submitted on 15 May 2026] Title:AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs Authors:Haizhong Zheng, Yizhuo Di, Jiahui Wang, Shuowei Jin, Xueshen Liu, Yongji Wu, Z. Morley Mao, Ion Stoica, Jiawei Zhao, Beidi Chen View a PDF of the paper titled AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs, by Haizhong Zheng and 9 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capabilities of large language models, but agentic RL remains prohibitively expensive.
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