🤖 AI Summary
Large language models (LLMs) struggle with complex problem comprehension and precise mathematical modeling in operations research (OR) optimization tasks. To address this, we propose Step-Opt-Instruct—a novel framework that iteratively generates OR problems of progressively increasing complexity and incorporates a structured, stepwise validation mechanism to effectively prevent error propagation and enhance synthetic data quality. We apply supervised fine-tuning using this framework on LLaMA-3-8B and Mistral-7B. Experimental results demonstrate state-of-the-art performance across three major benchmarks—NL4OPT, MAMO, and IndustryOR—with a 17.01% improvement in micro-averaged accuracy on complex problems. The approach significantly strengthens generalization capability for multi-constraint, multi-objective decision-making tasks, advancing the frontier of natural-language-to-optimization modeling.
📝 Abstract
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.