Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
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Computer Science > Machine Learning
arXiv:2609.03177 (cs)
[Submitted on 2 Sep 2026]
Title:Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings
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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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.03177 [cs.LG] |
| (or arXiv:2609.03177v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.03177
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings, by Frank Hu and 2 other authors
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