Training Compute-Optimal Large Language Models

Better AI Comes From More Data, Not Just Bigger Models

Many big AI systems got bigger while using about the same amount of training data, and that left them undertrained.
New results show that to get the most from a given compute budget, you should scale model size and data together — so for every doubling of model size, double the data too, simple as that.
A smaller model trained on much more data beat far larger rivals, showing you can get better and cheaper AI by changing the balance.
The star example, called Chinchilla, uses a smaller network but sees more data and it outperforms several huge models while needing less power for everyday use.
That means faster, cheaper AI for apps and people, and more models that most teams can actually run.
This flips a common idea on its head, and it points a clear way forward: spend compute on enough data, not only on size.
People building the next AI will likely choose smarter training not just massive size, and the results are already impressive.

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Training Compute-Optimal Large Language Models

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