You Can Only Tune Normalization: A Simple and Effective Approach to Parameter-Efficient Fine-Tuning
Lingyun Huang, Jianxu Mao, Junfei Yi, Ziming Tao, Ziyang Peng, Wei He, Rui Liu, Yaonan Wang · ACM Transactions on Intelligent Systems and Technology · 2025
To tackle the issue of excessive parameter volumes during fine-tuning of large-scale pre-trained models with full parameters, Parameter-Efficient Fine-Tuning (PEFT) methods have been introduced. The core concept involves freezing the backbone network of the model and updating only a small subset of parameters. This strategy not only decreases the number of parameters needed for training but also delivers performance comparable to Full-Tuning, even surpassing it on certain datasets. However, most popular PEFT methods introduce extra parameters or modules for fine-tuning, which come with inherent limitations. In response, we propose a straightforward and efficient PEFT method called You Can Only Tune Normalization (YONO). YONO focuses solely on tuning the normalization layer and the final classification layer of the model. This method avoids adding extra modules, making it easily applicable to any model without causing inference delays. We extensively tested YONO on 28 benchmark datasets, and the results indicate that it requires significantly fewer parameters compared to other advanced PEFT methods. Additionally, we validated YONO’s efficiency and generalizability across various vision models. Finally, we further explore the essence of PEFT methods, whether they learn new knowledge or expose the capabilities that a model has already learned. Our findings suggest that YONO is more sensitive to improvements in dataset quality, making it a promising candidate for future scaling to larger models.