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SRPO: Setwise Relative Policy Optimization for Multi-Agent LLMs
Multi-agent large language models solve complex tasks by coordinating several policies in a shared environment. However, existing reinforcement learning methods usually optimize each response or trajectory separately, even when several outputs jointly cause one state transition. Consequently, the up
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