arXiv cs.CL
· Papers
Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation?
arXiv:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover several key insights regarding the singu