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arXiv · 2610.27227

Which Agent's Skill Should Change? Group-Relative Credit Assignment for Evolving Multi-Agent Teams

Yanan Ma·Yang Wang·David Le·Chenhan Yuan·Yizheng Sun·Guangwei Zhang·Chenghao Xiao·Chenghua Lin·Imon Banerjee
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Abstract

Cooperative language-model agents can improve without weight updates by revising reusable skills, but only if a shared team reward is traced to the responsible agent and skill. Existing methods credit the skills present in each trajectory, yet presence is not responsibility: a harmful skill active in successful runs still earns positive credit. We present \textbf{GRSE}, \textbf{G}roup-\textbf{R}elative \textbf{S}kill \textbf{E}volution, which assigns behavioral credit by comparison: inspired by GRPO, it executes each question several times under the same skill libraries and contrasts the executions to credit agent--skill pairs, with structured diagnosis of failures. On HotpotQA and LoCoMo with two open-weight actors, GRSE outperforms MASkills, the closest prior method, on HotpotQA and every LoCoMo category except open-domain, with the largest gains on multi-hop questions ($+6.7$ and $+3.2$ F1). Its gains rest on better-grounded retrieval rather than answers given without evidence, and its learned skills transfer between the multi-hop tasks. GRSE is robust to corrupted libraries: starting from planted harmful skills, it credits every planted skill with the correct sign and recovers the score of a clean library ($+6.3$ points), whereas MASkills misattributes two and falls below its starting point.

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Yanan Ma, Yang Wang, David Le, Chenhan Yuan, Yizheng Sun, Guangwei Zhang, Chenghao Xiao, Chenghua Lin, Imon Banerjee. 2026-10-19. Which Agent's Skill Should Change? Group-Relative Credit Assignment for Evolving Multi-Agent Teams. https://arxiv.org/abs/2610.27227

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