Equation 13 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared
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with N parameters and D training tokens. Chinchilla’s question was: given a fixed compute budget C , what N and D minimize L ? The data-efficient small-model strategy asks a different question with the same law: given a fixed, small N set by the deployment target, what choice of D — and, crucially, what quality of D — minimizes L ? Fixing the small side of the equation first and spending the freed budget on data rather than on parameters is the strategy’s entire premise.
Sources cited in the article section
- [4] Training Compute-Optimal Large Language Models ↗
- [5] Textbooks Are All You Need ↗
- [6] TinyStories: How Small Can Language Models Be and Still Speak Coherent English? ↗
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