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Equation 11 · Part 9 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute

subscript

L(N)≈L∞+(NcN)αNL(N) \approx L_\infty + \left(\frac{N_c}{N}\right)^{\alpha_N}
subscript

What this part means

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

Its job in the formula

A subscript distinguishes a version, component, step, or member of a quantity. It does not automatically mean multiplication.

The passage around this formula

with N parameters and D training tokens, the factor of six counting the forward and backward passes. Empirically, test loss falls as a power law in each of parameters, data, and compute over many orders of magnitude, a relationship first characterised systematically by Kaplan and colleagues [ 5 ] . The important structural feature is the functional form: a term of the shape L(N)≈L∞+(NcN)αNL(N) \approx L_\infty + \left(\frac{N_c}{N}\right)^{\alpha_N}. has an irreducible floor L∞L_\infty and diminishing returns above it. Doubling N buys a fixed decrement in loss, not a fixed multiple of capability.

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Learn the underlying idea

A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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Sources cited in the surrounding passage

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