SkillEvolver: A Meta-Skill Framework for Online Agent Skill Learning
Researchers have introduced SkillEvolver, a lightweight, plug-and-play solution designed to address the static nature of current agent skills. Unlike traditional methods where skills are authored once and remain unchanged, SkillEvolver employs a single meta-skill to iteratively author, deploy, and refine domain-specific skills in real-time. The system targets skill prose and code rather than model weights, allowing seamless integration into any agent without retraining. Distinct from trace-distillation techniques, SkillEvolver refines skills based on failure signals encountered by other agents during actual deployment, ensuring practical robustness. It also features a fresh-agent overfit audit to detect leakage and silent-bypass modes. Experimental results on 83 SkillsBench tasks across more than 15 domains demonstrate that SkillEvolver achieves 56.8% accuracy, significantly outperforming curated human skills (43.6%) and no-skill baselines (29.9%). Additionally, it improved mean speedup in GPU kernel optimization tasks from 1.16 to 1.51. This development marks a significant step toward dynamic, self-improving AI agents capable of adapting to real-world usage scenarios without extensive computational overhead.
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SkillEvolver: A Meta-Skill Framework for Online Agent Skill Learning
Researchers have introduced SkillEvolver, a lightweight, plug-and-play solution designed to address the static nature of current agent skills. Unlike traditional methods where skills are authored once and remain unchanged, SkillEvolver employs a single meta-skill to iteratively author, deploy, and refine domain-specific skills in real-time. The system targets skill prose and code rather than model weights, allowing seamless integration into any agent without retraining. Distinct from trace-distillation techniques, SkillEvolver refines skills based on failure signals encountered by other agents during actual deployment, ensuring practical robustness. It also features a fresh-agent overfit audit to detect leakage and silent-bypass modes. Experimental results on 83 SkillsBench tasks across more than 15 domains demonstrate that SkillEvolver achieves 56.8% accuracy, significantly outperforming curated human skills (43.6%) and no-skill baselines (29.9%). Additionally, it improved mean speedup in GPU kernel optimization tasks from 1.16 to 1.51. This development marks a significant step toward dynamic, self-improving AI agents capable of adapting to real-world usage scenarios without extensive computational overhead.
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