A research paper proposes factorization-guided training and inference for diffusion-drafted speculative decoding, arguing that greater proposal capacity or higher mean accepted tokens (MAT) in diffusion drafters does not necessarily yield higher throughput because verification cost scales with proposal length. The work targets speculative decoding, which accelerates large language model inference through parallel verification of tokens proposed by a drafter.
Speculative decoding accelerates large language model inference through parallel verification of tokens proposed by a drafter. Diffusion drafters further parallelize proposal generation, but greater proposal capacity or higher mean accepted tokens (MAT) need not yield higher throughput. Because veri
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