让智能体在测试时自我进化技能策略，更灵活高效。
You Live More Than Once: Towards Hierarchical Skill Meta-Evolving
- 通过学习任务执行轨迹，自动优化技能与演化策略
- 在主流基准上生成更高质量的技能库，适应不同场景
- 轻量级算法适配，适合持续学习的智能体系统
测试时技能演化被视为提升部署智能体系统的新范式。现有工作主要聚焦于硬编码演化策略或依赖底层大模型昂贵参数更新的参数化学习。本文表明，在不同下游场景中，对技能演化框架本身的测试时精炼是实现智能体系统持续改进的必要条件，且轻量级算法适配是可行的。我们提出HiSME——一种轻量级分层技能元演化方案，通过从智能体的任务执行轨迹中学习元技能，联合优化技能与技能演化策略。在主流智能体基准上的实验表明，元演化能生成比纯技能演化更高质量的技能库，并为不同场景衍生多样化的元技能，从而促进未来持续经验学习。代码暂公开于 https://anonymous.4open.science/r/HiSME-BD45。
原文摘要 · Abstract (English)
Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving strategies or parametric learning that rely on expensive parameter updates in the underlying LLMs. In this paper, we demonstrate that test-time refinement of the skill evolving framework itself is necessary for continuous improvement of the agent systems in different downstream scenarios, and lightweight algorithmic adaptation is feasible. Specifically, we propose HiSME, a lightweight hierarchical skill meta-evolving solution that jointly optimizes skills and the skill evolving strategy by learning meta-skills from agents' task execution traces. Experiments on mainstream agentic benchmarks show that meta-evolving can produce a higher-quality skill library than pure skill evolving and can derive diverse meta-skills for different scenarios, thereby facilitating future continual experience learning. Our code is temporarily public at https://anonymous.4open.science/r/HiSME-BD45.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。