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arXiv · 2505.16531

HOFT: Householder Orthogonal Fine-tuning

Alejandro Moreno Arcas·Albert Sanchis·Jorge Civera·Alfons Juan
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Abstract

Adaptation of foundation models using low-rank methods is a widespread approach. Another way to adapt these models is to employ orthogonal fine-tuning methods, which are less time and memory efficient despite their good generalization properties. In this work, we propose Householder Orthogonal Fine-tuning (HOFT), a novel orthogonal fine-tuning method that aims to alleviate time and space complexity. Moreover, some theoretical properties of the orthogonal fine-tuning paradigm are explored. From this exploration, Scaled Householder Orthogonal Fine-tuning (SHOFT) is proposed. Both HOFT and SHOFT are evaluated in downstream tasks, namely commonsense reasoning, machine translation, subject-driven generation and mathematical reasoning. Compared with state-of-the-art adaptation methods, HOFT and SHOFT show comparable or better results.

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Alejandro Moreno Arcas, Albert Sanchis, Jorge Civera, Alfons Juan. 2025-05-22. HOFT: Householder Orthogonal Fine-tuning. https://arxiv.org/abs/2505.16531

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