AutoOR: Scalably Post-training LLMs to Autoformulate Operations Research Problems
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 autoformulate 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 ...
| Authors | Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 April 2026 |
| Last updated at source | 20 August 2026 |
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