GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters

• URL: http://arxiv.org/abs/2510.19778v1
• Date: Wed, 22 Oct 2025 17:11:49 GMT
• Title: GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters
• Authors: Anand Choudhary, Yasser Sulaıman, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux, Antoine Bosselut,
• Abstract summary: GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters.<n>We introduce a novel sparse fine-tuning technique named GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters.
• Score: 20.34415141254838
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sparse fine-tuning techniques adapt LLMs to downstream tasks by only tuning a sparse subset of model parameters. However, the effectiveness of sparse adaptation depends on optimally selecting the model parameters to be fine-tuned. In this work, we introduce a novel sparse fine-tuning technique named GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters, which fine-tunes only those model parameters which have the largest gradient magnitudes on downstream tasks and the smallest pre-trained magnitudes, intuitively prioritizing parameters that are highly task-relevant, but minimally disruptive to pre-trained knowledge. Our experimentation with LLaMA3 8B and Gemma 2B as base models shows that GaLLoP consistently improves or matches the in-distribution as well as out-of-distribution performance obtained via the usage of other leading parameter-efficient fine-tuning techniques, including LoRA, DoRA, and SAFT. Our analysis demonstrates that GaLLoP mitigates catastrophic forgetting and memorization of task data, as important pre-trained parameters remain unchanged, and stabilizes performance relative to other fine-tuning techniques, robustly generalizing across most random seeds.

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