用优化策略当老师，指导程序自动设计启发式算法。
Teacher-Aware Evolution of Heuristic Programs from Learned Optimization Policies
- 让预训练的优化策略充当行为导师，提供局部反馈。
- 在调度、路径和图优化任务上性能超越现有方法。
- 部署无需神经网络推理，适合实际应用落地。
基于大模型的启发式自动设计在组合优化中展现潜力，但现有方法主要依赖最终任务表现。本文提出一种教师感知的进化框架，使用独立训练的优化策略作为行为教师。不直接部署或模仿教师，而是查询其在候选启发式程序访问状态下的动作偏好，以此作为演化的局部反馈。该方法在任务性能与教师衍生的行为信号双重引导下，发现静态可执行的启发式算法。在调度、路由和图优化基准测试中，该方法优于仅以性能驱动的基线，且部署时无需神经网络推理。结果表明，学习到的优化策略可被重用于自动启发式发现的反馈源。
原文摘要 · Abstract (English)
LLM-based automatic heuristic design has shown promise for generating executable heuristics for combinatorial optimization, but existing methods mainly rely on delayed endpoint performance. We propose a \emph{teacher-aware evolutionary framework} that uses independently trained learned optimization policies as behavioral teachers. Instead of deploying or imitating the teacher, our method queries it on states visited by candidate heuristic programs and uses its action preferences as local feedback for evolution. The resulting search discovers static executable heuristics guided by both task performance and teacher-derived behavioral signals. Experiments on scheduling, routing, and graph optimization benchmarks show that our method improves over performance-driven LLM heuristic evolution baselines while requiring no neural inference at deployment. These results suggest that learned optimization policies can be repurposed as behavioral feedback sources for automatic heuristic discovery.
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