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Equation 29 · Part 1 · OpenAI Model Systems from First Principles: Weights, Post-Training, and Inference Compute

Symbol p_k

pkp_k
pkp_k

What this part means

only achievable if something can identify the successful attempt.

Its job in the formula

pkp_k is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Where the article explains it

Attempts from one model on one prompt are strongly correlated, so realised gains fall well below this bound; and pkp_k is only achievable if something can identify the successful attempt.

The passage around this formula

which is concave in k and saturates quickly. Two caveats destroy any naive extrapolation from it. Attempts from one model on one prompt are strongly correlated, so realised gains fall well below this bound; and pkp_k is only achievable if something can identify the successful attempt. Without a verifier, extra samples buy candidates, not answers. This is precisely why the reasoning-effort control and the availability of parallel test-time compute are architectural facts about a product rather than mere quality dials — the GPT-5 system card describes a variant that makes use of parallel test-time compute, and states that all models were evaluated at high reasoning effort [ 1 ] .

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Sources cited in the surrounding passage

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