Equation 13 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute
What does this equation mean?
Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
Read it piece by piece
Symbol N
N is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.
How to interpret it
Read this expression with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
has an irreducible floor and diminishing returns above it. Doubling N buys a fixed decrement in loss, not a fixed multiple of capability.
Sources cited in the article section
- [4] Attention Is All You Need ↗
- [5] Scaling Laws for Neural Language Models ↗
- [6] Training Compute-Optimal Large Language Models ↗
These citations give research context. Read each source to check which claims it supports.
Return to OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute