Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation

ArXi:2509.19602v2 Announce Type: replace Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While these methods perform well in single-task learning, extending them to multi-task learning exacerbates common issues, such as task interference and negative transfer, due to the limited number of trainable parameters. To address these challenges, we