Computer Science > Machine Learning
arXiv:2603.09865 (cs)
[Submitted on 10 Mar 2026]
Title:GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection
Authors:Kai Yao, Zhenghan Song, Kaixin Wu, Mingjie Zhong, Danzhao Cheng, Zhaorui Tan, Yixin Ji, Penglei Gao
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Abstract:Parameter-Efficient Fine-Tuning (PEFT) has become a key strategy for adapting large language models, with recent advances in sparse tuning reducing overhead by selectively updating key parameters or subsets of data. Existing approaches generally focus on two distinct paradigms: layer-selective methods aiming to fine-tune critical layers to minimize computational load, and data-selective methods aiming to select effective training subsets to boost training. However, current methods typically overlook the fact that different data points contribute varying degrees to distinct model layers, and they often discard potentially valuable information from data perceived as of low quality. To address these limitations, we propose Gradient-aligned Sparse Tuning (GAST), an innovative method that simultaneously performs selective fine-tuning at both data and layer dimensions as integral components of a unified optimization strategy. GAST specifically targets redundancy in information by employing a layer-sparse strategy that adaptively selects the most impactful data points for each layer, providing a more comprehensive and sophisticated solution than approaches restricted to a single dimension. Experiments demonstrate that GAST consistently outperforms baseline methods, establishing a promising direction for future research in PEFT strategies.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09865 [cs.LG] |
| (or arXiv:2603.09865v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09865
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View a PDF of the paper titled GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection, by Kai Yao and 7 other authors
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