EvoSkill v2: Fully Agentic Skill Evolution from Benchmark Adaptation to a Stateful Evolver

EvoSkill v2 is a three‑agent system that edits the instructions (skills) given to an agent while keeping its model and harness unchanged. The system comprises a benchmark adaptor that turns user‑supplied benchmarks and graders into plugins, a solver that attempts tasks, and a stateful evolver that rewrites skills each epoch based on the solver’s training rollouts. Compared to existing skill optimizers, EvoSkill v2’s evolver uses a single coding agent with a bash tool, accepts every epoch’s skills, and allows multiple attempts per training task, achieving higher held‑out pass rates and lower cost per performance gain on SpreadsheetBench v2 and $ au^3$‑bench banking. whyItMatters":"EvoSkill v2 demonstrates that a single, stateful evolver can more efficiently improve agent skills, enabling even less capable models to achieve significant performance gains in benchmark tasks."

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• llms
• agents
• benchmarks