Statistical Inference for Rank Allocation in Low-Rank Adaptation
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to downstream adaptation, allocating rank resources under a fixed parameter budget is an important problem for balancing efficiency, expressiveness, and generalization. Existing adaptive rank methods address this problem mainly through carefully designed importance scores constructed from gradient-derived sensitivity and uncertainty measures, without an explicit statistical interpretation. In this paper, we formulate LoRA rank allocation as a statistical hypothesis testing problem and propose StatLoRA, a statistic...
| Authors | Yihang Gao, Vincent Y. F. Tan |
|---|---|
| Primary category | stat.ML |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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