Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming strong autoregressive drafters such as EAGLE-3. Vanilla DFlash, however, still verifies only a single drafted trajectory per round, potentially limiting its acceptance length. We introduce DDTree (Diffusion Draft Tree), a method that constructs a draft tree directly from the per-position distributions of a block diffusion drafter. Under a fixed node budget, DDTree uses a simple best-first heap algorithm to select the continuations that are most likely to match the target model according to a surrogate defined by the draft model's output. The resulting tree is verified efficiently in a single target model forward pass using an ancestor-only attention mask. Because DDTree builds on DFlash, a leading draft model for speculative decoding, these gains place DDTree among the leading approaches to speculative decoding.

翻译：推测解码通过使用轻量级草稿模型生成多个未来标记，再由目标模型并行验证这些标记，从而加速自回归语言模型。DFlash证明，块扩散草稿模型能够在单次前向传播中生成整个草稿块，并实现最先进的推测解码性能，优于EAGLE-3等强自回归草稿模型。然而，标准DFlash每轮仍仅验证单一草稿轨迹，这限制了其接受长度。我们提出DDTree（扩散草稿树），该方法直接从块扩散草稿模型的逐位置分布构建草稿树。在固定节点预算下，DDTree使用简单的最佳优先堆算法，根据草稿模型输出的替代度量选择最可能匹配目标模型的延续路径。所得树通过仅使用祖先注意力掩码的单次目标模型前向传播高效验证。由于DDTree基于领先的推测解码草稿模型DFlash构建，这些优势使DDTree跻身推测解码的前沿方法之列。