Equation 2 · From Autocomplete to Delegation: The Technical History Behind OpenAI Codex
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 equation states a bound: one expression must stay on the indicated side of the other under the article’s assumptions. 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 p
p is part of the quantity the equation computes from the expression on the right.
Symbol x_1:T
:T is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.
Symbol t
t appears in the bound of this product. The bound states where the repeated operation starts, ends, or which values it includes.
Symbol T
T appears in the bound of this product. The bound states where the repeated operation starts, ends, or which values it includes.
Symbol x_t
is an input to the expression that computes the quantity on the left.
Symbol x_<t
x_<t is an input to the expression that computes the quantity on the left.
=
The expressions on both sides represent the same quantity under the stated assumptions.
See an illustrated explanation →subscript
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.
superscript
A raised number can be a power. When it is a label or bound, it selects a case or the upper limit of a sum; the formula’s structure distinguishes these uses.
See an illustrated explanation →Starting index or lower bound: t=1
This label says where the repeated addition, multiplication, or accumulation starts. Read its value or condition together with the article’s description of the index.
Ending index or upper bound: T
This label says where the repeated addition, multiplication, or accumulation stops. It sets the last term or end of the range.
How to interpret it
Read it with the definitions, units, and assumptions supplied by the article.
What the article says around this equation
An autoregressive model factorizes a sequence as . Source code can be placed in this representation alongside natural language. The model is not executing the program in this equation. It learns statistical structure over token sequences.
Sources cited in the article section
These citations give research context. Read each source to check which claims it supports.
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