← All parts of this equation

Equation 8 · Part 3 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared

Symbol D

L(N,D)≈E+ANα+BDβL(N, D) \approx E + \frac{A}{N^{\alpha}} + \frac{B}{D^{\beta}}
DD

What this part means

D occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

Its job in the formula

D occurs below the fraction bar. The quantity above the bar is divided by this expression; zero is excluded as a denominator.

The passage around this formula

…grow in roughly equal proportion — “for every doubling of model size the number of training tokens should also be doubled” [ 4 ] . The underlying loss law is commonly written as L(N,D)≈E+ANα+BDβL(N, D) \approx E + \frac{A}{N^{\alpha}} + \frac{B}{D^{\beta}}. 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 —…

Read this part in the article →

Learn the underlying idea

A variable is a named place for a value. Its letter is a local label: x can mean position in one formula and a data point in another.

Open the illustrated variables: a letter stands for a value guide →

See this notation across published equations →

Sources cited in the surrounding passage

These citations provide research context; check each source for the exact claim it supports.