Meta Paper: Small Models Need Broad Tuning to Predict Scaling Laws
rohanpaul_ai · x · 2026-08-17
A new Meta paper, "Small-Scale Experiments: Are They There Yet?", finds that small models aren't necessarily bad predictors of scaling laws but are often under-tuned. Scaling laws emerge around 4M parameters, trainable in under an hour on one GPU. Small models are unusually sensitive to hyperparameters; the law only becomes accurate with 256 configurations. This suggests that for research, cheap experiments need more tuning breadth rather than model size.
Related event: Scaling Laws Hold Even in 4M-Parameter Models, Meta Paper Shows(2 posts)→
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