OR-Toolformer: Modeling and Solving Operations Research Problems with Tool Augmented Large Language Models

• URL: http://arxiv.org/abs/2510.01253v1
• Date: Wed, 24 Sep 2025 14:42:40 GMT
• Title: OR-Toolformer: Modeling and Solving Operations Research Problems with Tool Augmented Large Language Models
• Authors: Jianzhang Zhang, Jialong Zhou, Chuang Liu,
• Abstract summary: Large language models (LLMs) demonstrate strong mathematical reasoning.<n>We introduce OR-Toolformer, which fine-tunes Llama-3.1-8B-Instruct with a semi-automatic data synthesis pipeline.<n>On three of four standard benchmarks, OR-Toolformer achieves up to 80.1% execution accuracy.
• Score: 3.7202906625021934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) demonstrate strong mathematical reasoning, but reliance on closed-source APIs for OR tasks raises privacy concerns, and training open-source models from scratch incurs high compute costs. We introduce OR-Toolformer, which fine-tunes Llama-3.1-8B-Instruct with a semi-automatic data synthesis pipeline that generates diverse OR problem-answer pairs and augments the model with external solvers to produce API calls. On three of four standard benchmarks, OR-Toolformer achieves up to 80.1% execution accuracy, exceeding size-matched baselines by over 4.3%. In zero-shot evaluation on two unseen OR problem types, it attains 54% average accuracy, a 21 percentage-point improvement over the strongest baseline. These findings validate the efficacy of tool-augmented fine-tuning LLMs for accurate and generalizable OR problem modeling and solving.

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