arXiv cs.AI· Kuan Xu, Jianming Wang, Ni Zhang, Yaoxin Wu, Yong Wang, Zhiguang Cao·· 2 天前AI 评分34
Dream-COP：在经验世界中引导组合优化问题的算法发现
Dream-COP: Guiding Algorithm Discovery for Combinatorial Optimization Problems in an Experience World
AI 导读
Dream-COP 让策略智能体在由历史搜索轨迹构成的“经验世界”中通过回放学习可执行策略，进而指导发现智能体生成和优化组合优化程序。在五类组合优化问题上，它比固定探索发现更优算法，KP 上匹配最优值，OBPP 上把差距从 2.112% 降至 1.199%，搜索请求减少 42.5%。学到的探索策略还能借助经验世界跨问题迁移。
正文 · 原文
Abstract:Large language models (LLMs) enable algorithm discovery for combinatorial optimization problems (COPs) through iterative program generation and evaluation. A central challenge is how to organize search to discover better algorithms within a limited budget. Our key insight is that past search trajectories and their recorded outcomes form an experience world, which can be used to facilitate the COP solving through replay. We introduce Dream-COP, in which a \emph{policy agent} learns executable policies through \emph{dreaming} in this experience world to guide a \emph{discovery agent} in generating and refining COP programs. Specifically, policy replay provides feedback on the quality of the programs revealed and the search steps used, to guide policy selection more effectively. Across five COPs, \textsc{Dream} discovers better algorithms than fixed exploration in both heuristic component design and complete solver generation. Particularly, it matches the optimal values on KP and reduces the gap on OBPP from 2.112\% to 1.199\% while using 42.5\% fewer search requests. Moreover, the learned exploration policies transfer effectively across different COPs with the assitance of the experience world.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36618 [cs.LG] |
| (or arXiv:2610.36618v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36618 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kuan Xu [view email]
[v1]
Tue, 27 Oct 2026 04:22:22 UTC (484 KB)
来源：arXiv cs.AI · arxiv.org