用迭代生成与分步验证提升大模型优化建模能力
Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validation
- 通过逐步增加问题复杂度并分步验证，生成高质量训练数据
- 在复杂优化任务上实现微平均准确率17.01%的提升
- 适合想用大模型自动化决策的运筹优化研究者
大型语言模型（LLMs）虽已革新多个领域，但在处理运筹学（OR）中的复杂优化建模任务时仍面临挑战。本文提出Step-Opt-Instruct框架，通过迭代问题生成系统性提升问题复杂度，并采用分步验证机制严格校验数据，防止错误传播，确保生成数据质量。基于该框架，我们微调了开源LLM如LLaMA-3-8B和Mistral-7B，构建出Step-Opt模型，在NL4OPT、MAMO和IndustryOR等基准上达到领先性能。大量实验表明，该模型在复杂任务中表现卓越，尤其在困难问题上的微平均准确率提升达17.01%。结果证明，结合结构化验证与渐进式问题精炼，可有效推进大模型在决策自动化中的应用。代码与数据集见https://github.com/samwu-learn/Step。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have revolutionized various domains but encounter substantial challenges in tackling optimization modeling tasks for Operations Research (OR), particularly when dealing with complex problem. In this work, we propose Step-Opt-Instruct, a framework that augments existing datasets and generates high-quality fine-tuning data tailored to optimization modeling. Step-Opt-Instruct employs iterative problem generation to systematically increase problem complexity and stepwise validation to rigorously verify data, preventing error propagation and ensuring the quality of the generated dataset. Leveraging this framework, we fine-tune open-source LLMs, including LLaMA-3-8B and Mistral-7B, to develop Step-Opt--a model that achieves state-of-the-art performance on benchmarks such as NL4OPT, MAMO, and IndustryOR. Extensive experiments demonstrate the superior performance of Step-Opt, especially in addressing complex OR tasks, with a notable 17.01\% improvement in micro average accuracy on difficult problems. These findings highlight the effectiveness of combining structured validation with gradual problem refinement to advance the automation of decision-making processes using LLMs.The code and dataset are available at https://github.com/samwu-learn/Step.
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