Summary of Proactive Gradient Conflict Mitigation in Multi-task Learning: a Sparse Training Perspective, by Zhi Zhang et al.
Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspective
by Zhi Zhang, Jiayi Shen, Congfeng Cao, Gaole Dai, Shiji Zhou, Qizhe Zhang, Shanghang Zhang, Ekaterina Shutova
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A unified model for simultaneous processing of multiple tasks is crucial for advancing generalist agents. The challenge lies in addressing gradient conflict, which arises when different tasks compete during joint training. While optimization methods manipulate task gradients to achieve better task balancing, they don’t eliminate the issue. This paper investigates gradient conflict across various methods and proposes a sparse training (ST) strategy, where only a portion of the model’s parameters are updated while keeping others unchanged. Experimental results show that ST effectively reduces conflicting gradients and improves performance. Moreover, ST can be seamlessly integrated with gradient manipulation techniques to enhance their effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has been working on creating machines that can do many things at once. This is important because it will help make our computers more helpful in the future. One problem they face is when these machines are trying to learn multiple tasks, some tasks might get better while others get worse. They’ve developed a few ways to fix this issue, but none of them completely solve the problem. In this paper, they try to understand why this happens and come up with a new solution called sparse training (ST). ST is like a shortcut that only changes a little bit of the machine’s brain while leaving the rest alone. The researchers tested ST and found it works really well. They also showed that ST can be used together with other techniques to make them even better. |
Keywords
* Artificial intelligence * Optimization
