a novel dynamic reconfiguration paradigm for Parameter-Efficient Fine-Tuning (PEFT) that adaptively adjusts the trainable structure during training to achieve a great performance
Our paper is currently under submission to ICLR 2026.
Our proposed DAF consistently outperforms existing state-of-the-art PEFT methods on the challenging VTAB-1k benchmark.
