Beyond LoRA: Is Sparsity-Induced Adaptation Better?
Low-rank adaptation (LoRA) and its variants provide a memory- and compute-efficient alternative to full fine-tuning of pre-trained models. However, questions remain about the comparative generalizability of these approaches and how the structural restrictions on low-rank updates preserve effective adaptation performance. We present a historical framing, covering the past (full fine-tuning and original LoRA), the present (different variants of LoRA), and propose simpler, cheaper, parameter-efficient extensions by inducing sparsity within existing LoRA variants: Cheap LoRA (cLA), training a single low-rank factor with the other fixed (deterministically or, in its randomized variant, stochas...
| Authors | Elijah Cadenhead, Cristian McGee, Xin Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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