What is recorded here?
Estimated a compute-optimal balance of model parameters and training tokens, then tested it with the 70-billion-parameter Chinchilla model trained on substantially more tokens than the earlier Gopher model at a comparable training-compute budget.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The equal scaling of model size and data is an empirical result under the paper's transformer, data and compute assumptions, not a universal theorem or proof that Chinchilla dominates every later model.
Read the original work
- Training Compute-Optimal Large Language Models
- An empirical analysis of compute-optimal large language model training — Google DeepMind
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People and systems
Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; Katie Millican; George van den Driessche; Bogdan Damoc; Aurelia Guy; Simon Osindero; Karen Simonyan; Erich Elsen; Jack W. Rae; Oriol Vinyals; Laurent Sifre
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.
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English catalogue narrative
Hoffmann and colleagues: compute-optimal language-model training and Chinchilla
Estimated a compute-optimal balance of model parameters and training tokens, then tested it with the 70-billion-parameter Chinchilla model trained on substantially more tokens than the earlier Gopher model at a comparable training-compute budget.
Scientific scope
The equal scaling of model size and data is an empirical result under the paper's transformer, data and compute assumptions, not a universal theorem or proof that Chinchilla dominates every later model.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jordan Hoffmann; Sebastian Borgeaud; Arthur Mensch; Elena Buchatskaya; Trevor Cai; Eliza Rutherford; Diego de Las Casas; Lisa Anne Hendricks; Johannes Welbl; Aidan Clark; Tom Hennigan; Eric Noland; Katie Millican; George van den Driessche; Bogdan Damoc; Aurelia Guy; Simon Osindero; Karen Simonyan; Erich Elsen; Jack W. Rae; Oriol Vinyals; Laurent Sifre
Complete named paper byline. Human research, source-reported system execution and experimental validation are distinct; individual effort shares are not measured.