Summary of Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling, by Shuaipeng Li et al.
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
by Shuaipeng Li, Penghao Zhao, Hailin Zhang, Xingwu Sun, Hao Wu, Dian Jiao, Weiyan Wang, Chengjun Liu, Zheng Fang, Jinbao Xue, Yangyu Tao, Bin Cui, Di Wang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper investigates the connection between optimal learning rates and batch sizes for Adam-style optimizers in deep learning tasks. These optimizers, including Adam, Adagrad, RMSProp, Adafactor, and Lion, are alternatives to SGD-style optimizers. The study reveals that the optimal learning rate increases linearly with batch size for Adam-style optimizers, contrary to previous findings for SGD-style optimizers. Theoretical analysis and extensive experiments on computer vision (CV) and natural language processing (NLP) tasks verify this scaling law. The paper’s contributions include a proof of the scaling law and experimental validation across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how two important things in deep learning, called “Adam-style optimizers” and “batch sizes,” affect each other. These optimizers are used to help computers learn from data and make good predictions. The study finds that the best way to adjust these optimizers is different from what was thought before. It also shows that if you increase the batch size, you need to increase the learning rate too. This means that you can’t just use one or the other, but rather they work together. The paper does this by using math and computer simulations to test its ideas. |
Keywords
» Artificial intelligence » Deep learning » Natural language processing » Nlp
