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
this https URL

使用 LLM（大型语言模型）进行优化的最新研究使用迭代地从 LLM 中寻找下一步解决方案或直接提示 LLM 进行优化。然而，这些方法存在固有的局限性，包括低操作效率、对提示设计的高敏感性和缺乏领域特定知识。我们介绍了 LLaMoCo，这是一种第一个被设计为在代码级别上调整 LLM 以解决优化问题的指令调整框架。具体地，我们建立了一个包含明确定义的问题提示和有效优化代码的全面指令集。然后，我们开发了一种新颖的两阶段学习策略，在指令调整阶段之前，通过对比学习为模型的微调过程提供了一个热身过程，以增强其收敛行为。实验证明，通过我们的 LLaMoCo 进行精细调整的 CodeGen（350M）模型在合成和真实问题集上都实现了优于 GPT-4 Turbo 和其他竞争对手的优化性能。精细调整的模型和使用说明可在此 URL 中获得。