Papers with layer-sparse strategy
GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing methods focus on layer-selective and data-selectory fine-tuning, but ignore the fact that different data points contribute varying degrees to distinct model layers. |
| Approach: | They propose a method that performs selective fine-tuning at both data and layer dimensions as integral components of a unified optimization strategy. |
| Outcome: | Experiments show that the proposed method outperforms baseline methods in terms of performance and performance. |