GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection
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 addre...
| Authors | Kai Yao, Zhenghan Song, Kaixin Wu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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