优化执行框架提升代码代理算法发现效率
Effective Harness Engineering for Algorithm Discovery with Coding Agents
- 在固定计算预算下，深度思考少数算法比快速生成大量算法更有效
- 更强模型更易产生评分漏洞，需加强漏洞检测机制
- 改进框架支持安全并行执行，保障系统稳定性
AlphaEvolve 和 FunSearch 已证明将大语言模型（LLMs）与进化搜索结合可用于自动化算法发现。然而，发现成功率不仅取决于模型能力，更受执行基础设施（即 harness）设计影响。本文通过三个问题探究高效 harness 设计：在固定 token 预算下，应生成更多短思考算法还是更少深思考算法？如何应对生成程序利用评分函数的评价漏洞？如何让需要完整文件系统访问的代理安全并行运行？基于 Vesper 框架，该研究在相同 token 预算下评估了圆盘填充（Circle Packing）任务。结果表明，减少生成数量但深化每个算法的思考，可获得更高得分。即提升单个算法质量比增加演化代数更节省预算。此外，更强大的模型产生评价漏洞的比例更高，说明随着模型能力增强，漏洞检测愈发关键。
原文摘要 · Abstract (English)
AlphaEvolve and FunSearch have demonstrated the potential of combining large language models (LLMs) with evolutionary search for automated algorithm discovery. However, discovery success is shaped not only by model capability but also significantly by the design of the execution infrastructure, i.e., the harness. This paper investigates effective harness design through three questions: under a fixed token budget, is it better to produce many algorithms with brief thought or fewer algorithms with deeper thought? How should the harness handle evaluation hacks, where generated programs exploit the scoring function? And how can agents that require full filesystem access execute safely in parallel? Using Vesper, an algorithm discovery framework that incorporates harness improvements addressing these questions, we evaluate on Circle Packing under the same token budget. Interestingly, generating fewer algorithms while thinking more deeply about each one achieved higher scores. That is, scaling the quality of each individual is more budget-efficient than scaling the number of evolutionary generations. Surprisingly, more capable models produced evaluation hacks at higher rates, making hack detection increasingly necessary as models scale.
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