arXiv cs.LG
· Papers
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
arXiv:2608.11342v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is a standard approach for adapting LLMs to a target distribution, but in settings such as personalization, where each author requires separate weight access, optimization, storage, and retraining, its costs become prohibitive. We propose Weig