This paper introduces PUB, a novel multi-task optimization algorithm that addresses the "seesaw problem" in multi-task learning by balancing parameter updates instead of gradients, leading to improved performance and robustness across various applications, including recommendation systems and computer vision.
Aligning learning progress across tasks through adaptive group-based task interactions to improve overall multi-task performance while maintaining computational efficiency.
CoTBal algorithm enhances multi-task visual instruction tuning by balancing task contributions and difficulties.