Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.16804 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918486287056896 |
|---|---|
| author | Motwani, Sumeet Ramesh Du, Chuan Petrov, Aleksander Davis, Christopher Torr, Philip Papania-Davis, Antonio Yan, Weishi |
| author_facet | Motwani, Sumeet Ramesh Du, Chuan Petrov, Aleksander Davis, Christopher Torr, Philip Papania-Davis, Antonio Yan, Weishi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16804 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems Motwani, Sumeet Ramesh Du, Chuan Petrov, Aleksander Davis, Christopher Torr, Philip Papania-Davis, Antonio Yan, Weishi Machine Learning Artificial Intelligence 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. |
| title | AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2604.16804 |