用贝叶斯优化在低维空间找强专家，节省5倍评估成本
Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?
- 在权重空间随机嵌入后用贝叶斯优化搜索最优专家
- 仅需5倍少的评估次数，效果媲美甚至超过随机搜索
- 适合想低成本微调LLM且不依赖反向传播的研究者
无梯度的后训练已成为大语言模型（LLMs）的有力替代方案，但现有方法仍代价高昂。我们探究在有限评估预算下，结构化搜索能否找到一个表现强劲的单一专家。基于有用权重更新位于低维子空间的证据，我们在权重空间的随机线性嵌入中应用贝叶斯优化。该方法无需反向传播，使用高斯过程代理高效引导候选评估。在0.5B至3B参数的Qwen2.5-Instruct模型上，多个推理基准测试显示，采用五倍少的候选评估次数，该方法匹配或超越了RandOpt。结果表明，代理引导的搜索能显著降低无梯度后训练的评估成本，同时生成更强的可部署单个专家。
原文摘要 · Abstract (English)
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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