Verified90% confidenceFactExact time
In 2022, DeepMind published the Chinchilla paper, which corrected the optimal training ratio and indicated that, under a given compute budget, the number of model parameters and the number of training tokens should maintain an approximate ratio of 1:20
12
Sources
90%
Confidence
Long-term
Relevance
7/7/2026
First Seen
Sources
Java程序员转型AI应用开发：航空智能客服实战指南
bilibili巴卜大模型7/6/2026
Related Claims
Unverified训练FLOPs可通过Chinchilla缩放定律估算，计算量约等于6乘以参数量乘以训练token数72% similarUnverifiedThe experiment trains a small MLP on a modular addition task modulo 97, with the training set making up only 35%, using full-batch training for a total of 20k steps.70% similarUnverifiedWork such as Chinchilla points out that under a given compute budget, parameter count and training token count should grow roughly in proportion to achieve optimal efficiency.69% similarUnverifiedPost-training costs only 1–5% of the cost of training a large model from scratch, yet can yield a 20–50% performance improvement on target tasks.67% similar
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