D3Tree：学习剪枝块扩散草稿树以加速投机解码验证

现有树方法按草稿概率在固定或按成本选定的单请求预算内选择验证节点，总是保留草稿器排序的前缀。D3Tree 则学习哪些节点值得验证：一个仅含 2H+2 参数的树规模头，从草稿状态、候选嵌入和草稿对数概率预测每个候选的条件存活分数，沿父链对分数乘积做阈值化，得到祖先封闭且大小随请求和解码轮次自适应的子树。训练在冻结草稿器上用全候选树的条件标签完成，可选联合模式还能引导草稿器偏向保留的参考路径。

服务时轻负载下验证整棵树，重负载下按预测存活率分配整批节点预算并对齐目标模型的矩阵乘法分块；树的构建、剪枝与调度在 GPU 上运行并与主机重叠。在 6 个视觉语言验证器和 17 个文本与多模态基准上，D3Tree 在 72 个负载设置中的 70 个最快：单请求相对自回归解码提速 3.2-4.0 倍，64 并发请求下仍保持 1.3-1.8 倍，而固定预算树此时已低于自回归速度；在时变到达轨迹下每个到达阶段的平均端到端延迟均最低。

D3Tree: Learning to Prune Block-Diffusion Draft Trees for Efficient Verification

Block-diffusion drafters propose a whole block of tokens in one forward pass, and draft trees built from their position-wise marginals increase the expected accepted length of speculative decoding. Every tree node occupies a query row in target verification, so under concurrent serving low-yield branches compete with other requests for verification capacity. Existing tree methods select verified nodes by draft probability under a fixed or cost-selected per-request budget, and thus always retain a prefix of the drafter's ranking. We present D3Tree, which learns which nodes of a block-diffusion draft tree are worth verifying. A tree-size head with 2H+2 parameters predicts each candidate's conditional survival from the draft state, the candidate embedding, and its draft log-probability. Thresholding the product of these scores along parent chains yields an ancestor-closed subtree whose size adapts to each request and decoding round. The head is trained on a frozen drafter with conditional labels on the full candidate tree; an optional joint mode also steers the drafter toward the retained reference path. At serving time, D3Tree verifies whole trees under light load and, under heavy load, allocates one batch-wide node budget by predicted survival, sized to the target's matrix-multiplication tiles. Retained trees share one ancestor-masked target pass, while construction, pruning, and scheduling run on the GPU and overlap with the host. Across six vision-language verifiers and 17 text and multimodal benchmarks, D3Tree is fastest in 70 of 72 load settings, with 3.2-4.0x speedup over autoregressive decoding for one request and 1.3-1.8x at 64 concurrent requests, where fixed-budget trees fall below autoregressive speed. Under a time-varying arrival trace, it has the lowest mean end-to-end latency in every arrival phase.

本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：D3Tree、speculative decoding、扩散模型、推理加速，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Hong Nie, Zhangchi Gao, Jun Yu, Bo Wang, Jianqing Sun, Shengping Liu），经 AITOP 多维评分管线结构化提炼。