Training Compute-Optimal Large Language Models

Trains hundreds of models to find the best split of a fixed compute budget between model size and training data. The answer: grow both at about the same rate. The 70B Chinchilla model, trained on far more tokens, beat much larger models.

Why it matters

It changed how labs plan training runs, shifting attention to data scale and cheaper inference.

Read the paper: arXiv:2203.15556

Plain-language summary written by SolxLab. Read the full paper at the official link; all rights remain with the authors.