SkillOpt-Lite: A Minimalist Pipeline for Agent Skill Self-Evolution
lmms-lab · hf · 2026-07-08
This paper proposes formalizing the simplest viable skill optimization pipeline through zero-order optimization. It eliminates redundant steps found in traditional methods while maintaining convergence and generalization capabilities via trajectory exploration, consensus mining, and verification gating principles. This approach significantly reduces the implementation complexity of agent skill self-evolution, requiring only minimal modifications to complete the entire optimization process.
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