Speculative decoding can accelerate autoregressive generation while preserving the…
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Speculative decoding can accelerate autoregressive generation while preserving the target model's sampling distribution. The mathematics behind this is particularly interesting because a smaller draft model can propose several tokens, and the larger target model can verify them in a single forward pass. A draft model q generates K candidate tokens sequentially. The target model p evaluates the corresponding conditional distributions in parallel. Each proposal is then accepted or rejected from left to right. For a proposed token x, the acceptance probability is: a(x) = min(1, p(x) / q(x))
Posted by Tech with Mak (48.5k followers) 4 h ago · 15 likes · 825 views · view the original post on X. Kept by the Dev Radar as AI dev tools.
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