Summary of Scaling Law with Learning Rate Annealing, by Howe Tissue et al.
Scaling Law with Learning Rate Annealing
by Howe Tissue, Venus Wang, Lu Wang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 abstract presents a new approach to modeling the cross-entropy loss curves of neural language models during training. The authors find that these curves adhere to a scaling law with learning rate annealing, which takes into account two factors: power-law scaling over data size and additional loss reduction during annealing. This formulation can accurately predict the loss at any given step across various learning rate schedulers, reducing computational cost while providing more accuracy and expressiveness for training dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers discovered that neural language models’ cross-entropy loss curves follow a specific pattern when trained with varying learning rates. By understanding this pattern, they developed an equation to predict the loss at any point during training, which can be used to optimize model performance. This breakthrough has significant implications for improving the efficiency and effectiveness of large language models. |
Keywords
» Artificial intelligence » Cross entropy
