DISTFLOW
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DistFlow: A Fully Distributed RL Framework for Scalable and Efficient LLM Post-Training
arXiv:2507.13833v4 Announce Type: replace Abstract: Effectively scaling Reinforcement Learning (RL) is crucial for enhancing the reasoning and alignment of Large Language Models. The massive data and complex execution flows inherent in these tasks require a distributed architecture capable of efficient scaling. However, to simplify programming and dependency management, mainstream frameworks often rely on a centralized architecture where a single node dispatches both control and data.