Automatic Rank Allocation for Low-Rank Adaptation in Large Language Models via lp Regularization

Abstract:Low-rank adaptation (LoRA) has become a popular parameter-efficient fine-tuning method for large language models. A key challenge in LoRA is how to determine the rank of each adaptation matrix, as rank directly controls its capacity and efficiency. Existing adaptive-rank methods typically allocate ranks according to manually designed importance scores, which are not directly derived from an optimization objective. In this work, we propose $\ell_p$-LoRA, a principled rank-allocation method based on $\ell_p$ regularization with $0<p<1$, which is a classical sparsity-inducing technique in signal processing and statistics. Specifically, we regularize the energy of each rank-one LoRA component, encouraging redundant components to vanish while preserving important ones. We derive the corresponding proximal subproblem and reduce the matrix optimization to a two-dimensional problem, leading to an implicit thresholding criterion for identifying redundant components. Experiments on natural language understanding and question-answering tasks demonstrate that the proposed method achieves competitive performance with existing LoRA baselines.

Machine Learning (cs.LG)

arXiv:2609.28998 [cs.LG]

(or arXiv:2609.28998v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.28998 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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