Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
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, indic...
| Authors | Frank Hu, Shriram Chennakesavalu, David Graff |
|---|---|
| Primary category | cs.LG |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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