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[2510.18428] AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library
Summary
AlphaOPT introduces a self-improving experience library for optimization modeling, enabling LLMs to learn from limited supervision and improve problem-solving capabilities.
Why It Matters
This research addresses the challenges in automating optimization modeling, a critical task across various industries. By leveraging self-improvement through experience reuse, AlphaOPT offers a more efficient alternative to traditional retraining methods, potentially enhancing decision-making processes in complex scenarios.
Key Takeaways
- AlphaOPT enables LLMs to learn optimization modeling with minimal supervision.
- The model improves its performance through a two-phase learning cycle.
- Evaluations show significant performance gains over traditional methods.
- Structured experience learning can replace costly retraining in complex tasks.
- The approach maintains manageable growth of the experience library over time.
Computer Science > Artificial Intelligence arXiv:2510.18428 (cs) [Submitted on 21 Oct 2025 (v1), last revised 15 Feb 2026 (this version, v3)] Title:AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library Authors:Minwei Kong, Ao Qu, Xiaotong Guo, Wenbin Ouyang, Chonghe Jiang, Han Zheng, Yining Ma, Dingyi Zhuang, Yuhan Tang, Junyi Li, Shenhao Wang, Haris Koutsopoulos, Hai Wang, Cathy Wu, Jinhua Zhao View a PDF of the paper titled AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library, by Minwei Kong and 14 other authors View PDF HTML (experimental) Abstract:Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise mathematical formulations and executable solver code. Existing LLM-based approaches typically rely on brittle prompting or costly retraining, both of which offer limited generalization. Recent work suggests that large models can improve via experience reuse, but how to systematically acquire, refine, and reuse such experience in structurally constrained settings remains unclear. We present \textbf{AlphaOPT}, a self-improving experience library that enables LLMs to learn optimization modeling knowledge from limited supervision, including answer-only feedback without gold-standard programs, annotated reasoning traces, or parameter updates. AlphaOPT operates in a continual two-phase cy...
