LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization

• URL: http://arxiv.org/abs/2507.04487v4
• Date: Wed, 24 Sep 2025 08:26:00 GMT
• Title: LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
• Authors: Xujia Wang, Yunjia Qi, Bin Xu,
• Abstract summary: LoSiA(Low-Resources Subnet Integration Adaptation) is an innovative method that dynamically localizes and optimize critical parameters during the training process.<n>We present LoSiA-Pro, a faster implementation of LoSiA, which reduces the training latency by about $27%$ compared to LoRA.
• Score: 6.641493851051085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, such as LoRA, significantly reduce the number of trainable parameters by introducing low-rank decomposition matrices. However, existing methods perform extensive matrix multiplications in domain specialization tasks, resulting in computational inefficiency and sub-optimal fine-tuning performance. Hence, we propose LoSiA(Low-Resources Subnet Integration Adaptation), an innovative method that dynamically localizes and optimizes critical parameters during the training process. Specifically, it identifies a sub-network using gradient sparsity analysis and optimizes it as the trainable target. This design enables effective high-rank adaptation by updating only the sub-network parameters, reducing the additional matrix multiplication. We also present LoSiA-Pro, a faster implementation of LoSiA, which reduces the training latency by about $27\%$ compared to LoRA. Extensive evaluations show that our method achieves minimal performance drop compared to full fine-tuning, while requiring the least training time across domain specialization and common-sense reasoning tasks. Further analysis shows that LoSiA also reduces forgetting during continued training. The source code is available at https://github.com/KlozeWang/LoSiA.

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