arXiv Machine Learning August 13, 2026 By Bohan Zhang, Anqi Ni, Yixin Wang, Paramveer S. Dhillon

Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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