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arXiv Statistics ML By Anastasis Kratsios, Tin Sum Cheng, Bum Jun Kim, Daniel Roy

When is Fine-Tuning Scalable? Certified Recovery from Noisy Data and Linear-Cost Fine-Tuning on Structured Task Shifts

Read the original on arXiv Statistics ML →

arXiv:2509. 00924v2 Announce Type: replace Abstract: Transformers often transfer across related tasks with little fine-tuning; we study this question in noisy nonparametric regression for continuous maps $f,g:[0,1]^d\to\mathbb{R}^D$, representing the source and shifted tasks, using a single-query, single-head transformer $\mathcal{T}(\cdot)=V\operatorname{Softmax}(-\alpha(\cdot,K))$ with $\ell^{\infty}$-based alignment scores $\alpha$.

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