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Scaling Laws, Carefully

Named practitioner synthesis

#231

T3digested
Topic
Practitioner Notes
First seen
2026-07-16 19:08:00
Last seen
2026-07-16 19:08:00

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  • Blog / Newsletter2026-07-16 19:07:22
    Scaling Laws, Carefully

    <p>Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$ and $D$.</p>