Summary of Analyzing & Reducing the Need For Learning Rate Warmup in Gpt Training, by Atli Kosson et al.
Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
by Atli Kosson, Bettina Messmer, Martin Jaggi
First submitted to arxiv on: 31 Oct 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 A novel analysis of Learning Rate Warmup’s benefits in training neural networks reveals its effectiveness in keeping update sizes limited, counteracting large initial values. By examining various metrics such as the L2-norm, directional change, and representation impact, this study provides a new perspective on warmup. The authors demonstrate that warmup helps mitigate large angular updates and critical batch size limitations early in training, with implications for optimizing AdamW/Lion optimizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning Rate Warmup is a technique used to help train neural networks, especially when working with larger groups of data. But why does it work? This study investigates the benefits of warmup by looking at how it affects the size and impact of updates made during training. The authors found that warmup helps keep early updates from being too large or significant, which can cause problems later on in the learning process. |
