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cs.AI2026
Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition
Yihao Ouyang, Shiwei Li, Haozhao Wang +5
Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-tuning (FFT). Many LoRA varia…
cs.AI2026
Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions
Yihao Ouyang, Shiwei Li, Haozhao Wang +7
Low-rank adaptation (LoRA) approximates the update of a pretrained weight matrix using the product of two low-rank matrices. However, standard LoRA follows an explicit-rank paradig…