Compute Optimal

2020 年 OpenAI 提出模型大小與資料量的冪次律，建議『大模型、少資料』。兩年後 Chinchilla 修正 compute-optimal 配置，讓資料量的重要性重新被看見。拆解 Scaling Laws 演進，以及它如何影響你現在的訓練決策與成本結構。

In 2020, OpenAI proposed a power-law relationship between model size and data volume, which led many teams toward a simple idea: bigger models, less data. Two years later, Chinchilla revised that picture with a compute-optimal view, showing that data volume mattered far more than many people had assumed. This matters if you’re deciding whether the next gain should come from more GPUs, a larger parameter count, or a larger corpus. The history of scaling laws is not just academic. It changes how you think about training efficiency, model selection, and where your cost structure really sits.