AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

Abstract:Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating complicated descriptions of these problems into solver-ready formulations requires specialized operations research (OR) expertise, making it hard to scale. We present AutoOR, a scalable synthetic data generation and reinforcement learning pipeline that trains LLMs to autoformalize optimization problems specified in natural language across linear, mixed-integer, and non-linear categories. AutoOR generates verified training data from standard optimization forms and uses solver execution feedback as the reward signal for RL post-training. AutoOR applied to an 8B model achieves state-of-the-art or competitive results across six established OR benchmarks, matching significantly larger frontier models. For a non-linear problem class involving physical dynamics, where frontier models score near 0%, we introduce a curriculum RL strategy that bootstraps from limited initial training data to make this class tractable for post-training. We believe that methods such as AutoOR can significantly accelerate industrial decision-making with AI.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2604.16804 [cs.LG]

(or arXiv:2604.16804v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.16804 Focus to learn more arXiv-issued DOI via DataCite

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