A single Monte Carlo tree of reasoning steps, compared branch-by-branch, can provide multiple signals for updating an LLM agent's skill, cutting evolution cost by 73.2% versus SkillOpt.
Olympiadbench: A challenging bench- mark for promoting agi with olympiad-level bilingual multimodal scien- tific problems
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Branch2Skill: Efficient Skill Evolution Through Reasoning Trees
A single Monte Carlo tree of reasoning steps, compared branch-by-branch, can provide multiple signals for updating an LLM agent's skill, cutting evolution cost by 73.2% versus SkillOpt.