AINeutralarXiv – CS AI · Jun 26/10
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When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs
Researchers investigate when multi-agent reinforcement learning improves large language model workflows, comparing shared versus isolated policy training approaches across three model scales. The study reveals that policy-sharing is a conditional design tradeoff rather than a universal stability solution, with performance dependent on workflow topology, task type, and model scale rather than policy architecture alone.