Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
Achieving SFT-level personalization performance without ever modifying underlying model weights.
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 Weightless F...
ArXiv CS.LG
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Weightless Fine-Tuning represents a significant shift by enabling model personalization through real-time logit adjustments rather than costly weight modifications. This approach drastically lowers the computational barrier for creating individualized AI experiences by bypassing the traditional, resource-intensive training cycle.
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