[KongchangAI]
Unverified50% confidenceFactExact time

The temperature parameter T adjusts the sharpness of the logits distribution via softmax(logits/T): the lower T is, the closer it is to greedy decoding, and the higher T is, the closer it is to uniform sampling.

1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen

Sources

Aaronson Oracle: A Prediction Experiment by a Quantum Computing Pioneer

Related Entities

Temperature ParameterconceptsoftmaxconceptGreedy DecodingconceptLarge Modelconcept

Related Claims

UnverifiedFor a non-atomic output distribution μ, when all mappings fall within the range of sup-distance τ≤1 from a non-decreasing shape and total variation V≤2, the minimax lower bound on the compound risk is (mn)^{-2/3} + min{τ^2,1/n} + min{τ^2,V^2,(V/n)^{2/3}} (ignoring constant factors).52% similar

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