We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant.
By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of model size the number of training tokens should also be doubled.
We test this hypothesis by training a predicted compute-optimal model,Chinchilla, that uses the same compute budget asGopherbut with 70B parameters and 4more more data.Chinchillauniformly and significantly outperformsGopher(280B) , GPT-3 (175B) , Jurassic-1 (178B) , and Megatron-Turing NLG (530B)  on a large range of downstream evaluation tasks.
This also means thatChinchillauses substantially less compute for fine-tuning and inference, greatly facilitating downstream usage.
As a highlight,Chinchillareaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement overGopher.