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

• URL: http://arxiv.org/abs/2506.11042v1
• Date: Wed, 21 May 2025 08:33:26 GMT
• Title: GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models
• Authors: Baoquan Zhang, Guangning Xu, Michael. K. Ng,
• Abstract summary: Generative Fine-Tuning (GenFT) is a novel method that extracts structured, transferable information from $Delta W$ for efficient $Delta W$ training.<n>Experiments on VTAB-1K, FGVC, and GLUE benchmarks demonstrate that GenFT outperforms state-of-the-art PEFT methods.
• Score: 23.85784506399565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained Foundation Models (PFMs) have transformed numerous applications by enabling efficient adaptation to customized tasks. Parameter-Efficient Fine-Tuning (PEFT) has emerged as a resource-efficient alternative to full fine-tuning, especially leveraging reparameterized weights $\Delta W$ to adapt models for downstream tasks. However, a critical yet underexplored question remains: can we utilize well-pretrained weights $W_0$ to guide the update of task-specific $\Delta W$, avoiding inefficient training it from scratch? To end this, we propose Generative Parameter-Efficient Fine-Tuning (GenFT), a novel method that extracts structured, transferable information from $W_0$ for efficient $\Delta W$ training. To extract row and column structure information, GenFT applies row and column transformations to distill essential patterns from $W_0$. A tailored policy further decomposes $\Delta W$ into layer-shared and layer-specific components, balancing information reuse and individualized flexibility. GenFT is simple yet effective, achieving superior performance across CV and NLP tasks. Extensive experiments on VTAB-1K, FGVC, and GLUE benchmarks demonstrate that GenFT outperforms state-of-the-art PEFT methods, offering a new perspective for efficient model adaptation.

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