LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation

📰 ArXiv cs.AI

Learn how to accelerate Bayesian optimization using large language models (LLMs) for broad exploration and selective experimentation, reducing costs and improving efficiency

advanced Published 23 May 2026

Action Steps

Implement LABO by integrating LLMs into the Bayesian optimization pipeline
Use LLMs for broad exploration of the search space to identify promising candidates
Apply selective experimentation to validate the top candidates identified by LLMs
Configure the LLM to balance exploration and exploitation in the optimization process
Test the performance of LABO on a benchmark problem to evaluate its effectiveness

Who Needs to Know This

Data scientists and researchers working on Bayesian optimization and LLMs can benefit from this approach to improve their experimentation efficiency and reduce costs

Key Insight

💡 LLMs can be used to accelerate Bayesian optimization by leveraging their low evaluation cost for broad exploration and selective experimentation

Full Article

Title: LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation

Abstract:
arXiv:2605.22054v1 Announce Type: cross Abstract: The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). However, existing approaches typically embed LLMs directly into the sampling or surrogate modeling pipeline, without fully leveraging their significantly lower evaluation cost compared to real-world experiments. To address this limitation, we propose LLM-Accelerated Bayesian

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