Before it
Llama 2 had been trained on two trillion tokens.
The problem
Chinchilla-optimal training leaves inference-time performance on the table for a model you serve constantly.
What it introduced
8B and 70B models trained on fifteen trillion tokens, far past the compute-optimal point.
Why it matters
Overtraining for inference efficiency became the open-model norm, and these were the first widely deployed examples.
What modern models inherit
Train small models for much longer than compute-optimal.
What came next
Llama 3.1 added the 405B model and 128K context.