English(EN) Learning to Model Optimization Problems from Solver Outcomes

新RL方法可自动从自然语言生成优化模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-31 04:00

研究人员开发了一种名为“从求解器结果中建模优化”（OMSO）的新方法，该方法使用强化学习从自然语言描述中自动创建优化模型。该方法通过与数学规划求解器交互来训练模型，将执行结果转化为奖励。OMSO旨在提高可扩展性，并减少对昂贵的流程监督或闭源LLM的依赖，在Gurobi和OR-Tools等各种数据集和求解器上均显示出有效性。 AI

影响这项研究通过使优化建模更易于访问和更高效，有可能显著改善复杂决策过程的自动化。

排序理由该集群包含一篇学术论文，详细介绍了使用强化学习进行优化建模的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Runda Guan, Xiangqing Shen, Jiajun Zhang, Yifan Zhang, Jian Cheng, Rui Xia · 2026-10-31 04:00

从求解器结果中学习建模优化问题

arXiv:2604.00442v2 Announce Type: replace Abstract: Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-source LLMs with high inference latency or fine-tune …

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从求解器结果中学习建模优化问题

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