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Speculative decoding is a technique that accelerates autoregressive generation while keeping the output distribution unchanged: the draft model first autoregressively generates k candidate tokens, the target model accepts or rejects them one by one in a single parallel forward pass, and resampling occurs at the first rejected position.
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10/23/2026
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VerifiedSpeculative 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.78% similarUnverifiedThe losslessness of speculative decoding relies on a modified rejection sampling mechanism: for each token proposed by the draft model, the target model computes its probability and decides acceptance or rejection accordingly; once rejected, resampling is done from the difference between the target model and draft model distributions (after normalization).77% 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.75% similarUnverified推测解码技术通过小模型预测草稿、大模型验证的方式加速生成，KV Cache共享机制降低重复请求的计算冗余74% similarUnverifiedSpeculative decoding is a technique that accelerates generation without changing the output distribution.73% similar
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