principles.fyi · the brain · concept

test-time compute

Spending extra computation when answering, rather than during training, to get better answers.

better answer via more compute at answer-time, not more training

Test-time compute is the idea that a model can improve its answer by thinking harder at inference time instead of being trained more. The clearest example is chain-of-thought: letting the model generate intermediate reasoning before its final answer, which spends more tokens (and so more compute) per question. It is loosely grouped under post-training because it is another lever for quality on top of the pretrained base, but it acts when the model runs, not when its weights are learned.