UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems 事件

UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems arXiv:2605.26646v1 Announce Type: cross Abstract: LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement learning interface. Existing RL post-training frameworks mainly target single-policy optimization and lack abstractions for user-defined multi-agen