LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
<|reference_start|>LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation: Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer. However, these approaches exhibit inherent limitations, including low operational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. Specifically, we establish a comprehensive instruction set containing well-described problem prompts and effective optimization codes. We then develop a novel two-phase learning strategy that incorporates a contrastive learning-based warm-up procedure before the instruction-tuning phase to enhance the convergence behavior during model fine-tuning. The experiment results demonstrate that a CodeGen (350M) model fine-tuned by our LLaMoCo achieves superior optimization performance compared to GPT-4 Turbo and the other competitors across both synthetic and realistic problem sets. The fine-tuned model and the usage instructions are available at https://anonymous.4open.science/r/LLaMoCo-722A.<|reference_end|>
@article{ma2024llamoco:,
    title={LLaMoCo: Instruction Tuning of Large Language Models for Optimization
  Code Generation},
    author={Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao,
  Yining Ma, Yue-Jiao Gong},
    journal={arXiv preprint arXiv:2403.01131},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01131},
    primaryClass={math.OC cs.AI cs.CL cs.LG cs.NE cs.SE}
}