Paper

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

arXiv:2506.11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$. Existing methods often learn $\Delta W$ largely independently of pretrained weights $W_0$, or exploit $W_0$ mainly through initialization or simple reparameterization. To further leverage the structural information encoded in $W_0$, we propose Generative Parameter-Efficient Fine-Tuning (GenFT), a $W_0$-conditioned PEFT method that uses a determinis…

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