Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection

• URL: http://arxiv.org/abs/2505.12579v1
• Date: Sun, 18 May 2025 23:45:50 GMT
• Title: Adaptive parameter-efficient fine-tuning via Hessian-informed subset selection
• Authors: Shiyun Xu, Zhiqi Bu,
• Abstract summary: We propose a Hessian-informed approach to fine-tuning models.<n>AdaPEFT adapts to various tasks and models, in which the selected subset empirically transfers across training horizons and model sizes.
• Score: 8.885727065823156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient fine-tuning (PEFT) is a highly effective approach for adapting large pre-trained models to downstream tasks with minimal computational overhead. At the core, PEFT methods freeze most parameters and only trains a small subset (say $<0.1\%$ of total parameters). Notably, different PEFT methods select different subsets, resulting in varying levels of performance. This variation prompts a key question: how to effectively select the most influential subset to train? We formulate the subset selection as a multi-task problem: maximizing the performance and minimizing the number of trainable parameters. We leverage a series of transformations -- including $\epsilon$-constraint method and second-order Taylor approximation -- to arrive at the classical 0-1 knapsack problem, which we solve through the lens of Pareto optimality. Consequently, we propose AdaPEFT, a Hessian-informed PEFT that adapts to various tasks and models, in which the selected subset empirically transfers across training horizons and model sizes.

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