Temperature, top-k and top-p explained: the model only sets the odds, a sampler rolls the dice, and temperature 0 does not promise the same answer every time

A language model never picks a word. Its last output is a list of scores, one for every token it knows (262,144 of them on one model we run). Ordinary code outside the network, called a sampler, turns the scores into chances and draws one token. Temperature, top-k, top-p, min-p and the repetition p…

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1. 2026-11-14 00:57 · r/machinelearningnews
   Temperature, top-k and top-p explained: the model only sets the odds, a sampler rolls the dice, and temperature 0 does not promise the same answer every time