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arXiv · 2608.10867

Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?

Nigel Bastian Cendra·Abdelhamid Ezzerg·Fernando Julio Cendra·Jeremias Knoblauch·Jakob Zeitler
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Abstract

Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly. We ask whether structured search can identify a strong single expert under a modest evaluation budget. Motivated by evidence that useful weight updates lie in low-dimensional subspaces, we apply Bayesian optimization within a random linear embedding of weight space. Our method requires no backpropagation and uses a Gaussian process surrogate to guide candidate evaluations efficiently. Across several reasoning benchmarks with Qwen2.5-Instruct models from 0.5B to 3B parameters, Bayesian optimization using five times less candidate evaluations matches or exceeds RandOpt. These results show that surrogate-guided search can substantially reduce the evaluation cost of gradient-free post-training while producing stronger deployable single experts.

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Nigel Bastian Cendra, Abdelhamid Ezzerg, Fernando Julio Cendra, Jeremias Knoblauch, Jakob Zeitler. 2026-08-11. Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?. https://arxiv.org/abs/2608.10867

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