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News/When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs
arxiv
PublishedMay 26, 2026 at 4:00 AM
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When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs

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Publisher summary· verbatim

arXiv:2605.24202v1 Announce Type: new Abstract: Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that are poorly understood. We study when end-to-end RL training of multi-ag

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arxivWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems16harxivQuery-Driven Learning for Lifted Reasoning16h
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