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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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