Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape

• URL: http://arxiv.org/abs/2409.14396v1
• Date: Sun, 22 Sep 2024 11:24:10 GMT
• Title: Flat-LoRA: Low-Rank Adaption over a Flat Loss Landscape
• Authors: Tao Li, Zhengbao He, Yujun Li, Yasheng Wang, Lifeng Shang, Xiaolin Huang,
• Abstract summary: Low-Rank Adaptation (LoRA) is an efficient way to fine-tune models by optimizing only a low-rank matrix. A solution that appears flat in the LoRA space may exist sharp directions in the full parameter space, potentially harming generalization performance. We propose Flat-LoRA, an efficient approach that seeks a low-rank adaptation located in a flat region of the full parameter space.
• Score: 52.98187034726091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning large-scale pre-trained models is prohibitively expensive in terms of computational and memory costs. Low-Rank Adaptation (LoRA), a popular Parameter-Efficient Fine-Tuning (PEFT) method, provides an efficient way to fine-tune models by optimizing only a low-rank matrix. Despite recent progress made in improving LoRA's performance, the connection between the LoRA optimization space and the original full parameter space is often overlooked. A solution that appears flat in the LoRA space may exist sharp directions in the full parameter space, potentially harming generalization performance. In this paper, we propose Flat-LoRA, an efficient approach that seeks a low-rank adaptation located in a flat region of the full parameter space.Instead of relying on the well-established sharpness-aware minimization approach, which can incur significant computational and memory burdens, we utilize random weight perturbation with a Bayesian expectation loss objective to maintain training efficiency and design a refined perturbation generation strategy for improved performance. Experiments on natural language processing and image classification tasks with various architectures demonstrate the effectiveness of our approach.

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