AI scaling laws: where the frontier stands
In short: In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.
Evidence class: Emerging research.
A neural scaling law is a theoretical or empirical statistical law between these parameters.
What would need to be true? • Scaling inference through increased test-time compute (TTC) to extend laws beyond training.
What could prevent it? • The inherent diminishing returns nature of many scaling laws.
Sources: Wikipedia, “Neural scaling law (Wikipedia)” (CC BY-SA 4.0).
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Sources
- Summary by Intellect · source: Neural scaling law
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