Scaling laws: why model loss is a straight line on a log-log plot, and why GPT-3 was undertrained
Article summary
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You'd expect training a bigger language model to be a gamble. It isn't — and that is one of the most useful facts in modern AI. Across many orders of magnitude, a model's test loss falls as a smooth power law in the compute you spend: plot loss against compute on log-log axes and the points fall on a straight line. I built two demos that compute the real curves in the browser, and together they explain both why labs can plan nine-figure training runs and why GPT-3, of all things, was…
1Key Takeaways
- You'd expect training a bigger language model to be a gamble.
- It isn't — and that is one of the most useful facts in modern AI.
- Across many orders of magnitude, a model's test loss falls as a smooth power law in the compute you spend: plot loss against compute on log-log axes and the points fall on a straight line.
- I built two demos that compute the real curves in the browser, and together they explain both why labs can plan nine-figure training runs and why GPT-3, of all things, was….
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that you'd expect training a bigger language model to be a gamble.
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