English(EN) OptiMind: Improving Small Language Models for Optimization Modeling through Formulation Guidance and Multi-Turn Learning

OptiMind系统提升小型语言模型在优化任务中的表现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了OptiMind系统，旨在增强小型语言模型（SLMs）在数学优化任务中的能力。OptiMind通过整合数据清洗、类别特定提示以及带有求解器反馈的多轮强化学习来解决当前SLMs的局限性。这种方法使SLMs能够迭代地优化其问题公式和求解轨迹，在清洗后的基准测试中达到与前沿模型相当的性能。该项目的代码、数据和训练模型已在GitHub和Hugging Face上公开。 AI

影响增强了在资源受限环境中为特定任务部署强大LLMs的可行性。

排序理由这是一篇详细介绍改进小型语言模型的新系统和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xinzhi Zhang, Zeyi Chen, Humishka Zope, Hugo Barbalho, Konstantina Mellou, Marco Molinaro, Janardhan Kulkarni, Ishai Menache, Sirui Li · 2026-11-11 05:00

OptiMind：通过公式指导和多轮学习改进用于优化建模的小型语言模型

arXiv:2509.22979v3 Announce Type: replace Abstract: Mathematical optimization drives decisions in supply chains, logistics, energy, and scheduling, but translating natural-language problems into solver-executable formulations remains a bottleneck. While frontier models show promi…

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报道来源 [1]

OptiMind：通过公式指导和多轮学习改进用于优化建模的小型语言模型

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