New framework reSolve enhances agent skill evolution and reproducibility

Researchers have developed reSolve, a new framework designed to improve the creation and reproducibility of agent skills. This system addresses the common issues of self-evolved skills underperforming human-curated ones and the difficulty of agents reproducing learned skills at deployment. reSolve utilizes a surrogate verifier to enhance sparse reward signals and employs a beam search guided by this verifier to construct solutions. In tests, this approach enabled a basic model to self-evolve skills that achieved a 74.9% mean-of-3 score, significantly outperforming both human-curated baselines and a strong GPT-5.5/OpenHands model. AI

IMPACT This research could lead to more capable and reliable AI agents by improving how their skills are developed and executed.

RANK_REASON The cluster contains a research paper detailing a new framework for agent skill evolution. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• GPT-5.5
• Hugging Face
• natural science
• OpenHands
• reSolve
• ScienceCast

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• model release

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