DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2508.17337v1
• Date: Sun, 24 Aug 2025 12:45:36 GMT
• Title: DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
• Authors: Haojie Zhang,
• Abstract summary: We introduce DropLoRA, a novel pruning-based approach that focuses on pruning the rank dimension.<n>By continuously adapting the learning subspace, DropLoRA significantly boosts performance without incurring additional training or infer- ence costs.
• Score: 5.103108721904429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LoRA-based large model parameter-efficient fine-tuning (PEFT) methods use low-rank de- composition to approximate updates to model parameters. However, compared to full- parameter fine-tuning, low-rank updates often lead to a performance gap in downstream tasks. To address this, we introduce DropLoRA, a novel pruning-based approach that focuses on pruning the rank dimension. Unlike conven- tional methods that attempt to overcome the low-rank bottleneck, DropLoRA innovatively integrates a pruning module between the two low-rank matrices in LoRA to simulate dy- namic subspace learning. This dynamic low- rank subspace learning allows DropLoRA to overcome the limitations of traditional LoRA, which operates within a static subspace. By continuously adapting the learning subspace, DropLoRA significantly boosts performance without incurring additional training or infer- ence costs. Our experimental results demon- strate that DropLoRA consistently outperforms LoRA in fine-tuning the LLaMA series across a wide range of large language model gener- ation tasks, including commonsense reason- ing, mathematical reasoning, code generation, and instruction-following. Our code is avail- able at https://github.com/TayeeChang/DropLoRA.

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