Summary of More Compute Is What You Need, by Zhen Guo
More Compute Is What You Need
by Zhen Guo
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
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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 Large language models require significant computational resources during pre-training. Researchers have historically relied on scaling laws to optimize compute budgets for model size and training tokens. This paper challenges this approach by introducing a new scaling law that suggests model performance is primarily driven by the amount of compute spent, regardless of the specific allocation to model size or dataset size. The authors predict that smaller models with larger datasets are more efficient for inference, and that scaling up model sizes may be necessary to further improve performance once web datasets become exhausted. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to train a computer program to understand language. You need lots of computational power (like super powerful computers) to make it work well. Most people use a rule-of-thumb to decide how much power to use, but this paper suggests there might be a better way. They found that the amount of power used is what really matters, not how you split it between making the program smarter or training it on more data. This could help people make their language models work better and faster in the future. |
Keywords
» Artificial intelligence » Inference » Scaling laws
