AI Research Reveals Training More Data Beats Bigger Models for Limited Computing Budgets
Summary
New AI research discovers that training smaller models on more data dramatically outperforms building larger models with limited data when working within tight computing budgets, revealing practitioners should prioritize gathering more training tokens over expanding model parameters as budgets increase.
Key Points
- Researchers explore the trade-off between model size and dataset size when training large language models under fixed compute budgets, finding that total training compute scales as C∝N×D where N is parameters and D is tokens
- Experiments using tiny transformers on WikiText-2 dataset reveal that small models trained on more data outperform larger models with limited data for small budgets, while larger models become optimal when sufficient data is available for bigger budgets
- Results show optimal model size scales as N_opt∝C^0.14 and optimal dataset size as D_opt∝C^0.86, indicating that when compute budget increases, practitioners should prioritize adding more training tokens rather than increasing model parameters