Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings

2026-09-02

AI Summary

研究探讨了前沿大语言模型在连续和离散设置下的批量优化性能，发现其在语义丰富场景中表现更优。

Abstract

Frontier large language models (LLMs) have become attractive priors for optimization due to their large-scale pretraining that enables them to navigate a variety of optimization settings. However, the effectiveness of modern reasoning LLMs in batch optimization settings remains underexplored. Here we investigate the performance of the current generation of frontier LLMs as batch optimizers in both continuous and discrete settings. We find that while LLMs are competitive zero-shot batch optimizers for numerical test functions, their performance is brittle compared to classical non-LLM optimization approaches. However, LLM priors are significantly better in semantically rich settings, indicating that their batch optimization behavior is highly effective when navigating and reasoning over the discrete spaces most similar in structure to their pretraining data.

Problem

• Frontier LLMs
• batch optimization
• continuous and discrete settings
• reasoning models

Innovation

• batch optimizers
• discrete settings
• semantically rich