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原文摘要

Speculative decoding speeds up language models by letting a lightweight drafter propose several future tokens that the target model verifies in parallel. Block diffusion drafters such as DFlash draft a whole block in one forward pass, and DDTree builds a draft tree from their per-position distributions. Each of these distributions assumes that the drafter's own most likely continuation is correct. A tree, however, gains over a chain only on branches that leave this continuation, where the drafter's top-1 accuracy drops from 89% to 18% and its candidate lists miss the right token far more often. When re-run from the fork token, the same drafter is usually correct, so the errors come from the prefix that the distributions condition on rather than from what the drafter knows. We introduce DForkTree, a training-free method that re-anchors each branch on its own tokens. DForkTree adds n-gram statistics from the target model's own outputs to the drafter's scores as pointwise mutual information, weights them more after the fork, and builds the optimal tree under these scores exactly on the GPU. On Qwen3.5-4B and 9B, with all tree methods sharing one drafter, tree pipeline and verifier, DForkTree is the fastest tree method on every benchmark. It is up to 41.5% faster than DFlash and 15.1% faster than DDTree in the same setting, with average speedups of up to 4.75x over autoregressive decoding.