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Equation 8 · Part 2 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared

Symbol N

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

What this part means

the what.

Its job in the formula

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

Where the article explains it

Chinchilla’s question was: given a fixed compute budget C , what N and D minimize L ?

The passage around this formula

…tokens should 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…

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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.

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Sources cited in the surrounding passage

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