Block Verification for Speculative Coupled Decoding

Autoregressive image and video models generate long sequences of visual tokens, making token-by-token decoding slow. Speculative Coupled Decoding (SCD) accelerates generation without a separate draft model by reusing draft tokens across Jacobi iterations. Its token-wise verifier stops at the first rejection, whereas Block Verification can accept longer prefixes using the same target-model evaluation. We replace SCD's verifier with Block Verification. However, verification changes the distribution of the tokens left for reuse, so applying SCD's updates with their original stored distributions can bias the output. We derive the exact post-verification distributions and use them to update the tokens before reuse. The method requires only a small code change to SCD, with no training or extra target-model passes per iteration. We prove that the resulting decoder is lossless: it preserves the target-model sampling distribution exactly. Across two image generation models and one video generation model, our method reduces mean target-model calls by 2.1%-20.7% and mean generation time by 1.6%-19.9% compared with SCD at matching window sizes.