Unverified50% confidenceFactExact time
Decoding strategy refers to how a model selects output from a probability distribution when generating each token; common strategies include greedy decoding, beam search, temperature sampling, top-k sampling and nucleus sampling, among others.
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10/13/2026
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UnverifiedIn a single one-step denoising step, the model directly reads out the probability distribution for each answer slot rather than sampling token by token.76% similarUnverifiedGeneration strategies such as temperature parameter, top-k sampling, and nucleus sampling are all means of adjusting and controlling the probability distribution.71% similarVerifiedSpeculative decoding is a lossless acceleration technique that does not alter the model's output distribution; it uses a draft model with far fewer parameters than the target model to generate candidate tokens, which the target model then verifies in parallel.71% similarUnverifiedSpeculative decoding generates candidate tokens through a lightweight draft model, with the main model verifying them in parallel in a single forward pass, accepting tokens that conform to the distribution and regenerating from the first rejected position.70% similarUnverifiedThe Transformer architecture computes a probability distribution over all tokens in the vocabulary at each decoding step via the softmax function, then selects the next token through sampling strategies such as temperature and top-p.70% similar
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