[论文讨论] DFlash: Block Diffusion for Flash Speculative Decoding #57
Description
论文信息
标题: DFlash: Block Diffusion for Flash Speculative Decoding
作者: Jian Chen, Yesheng Liang, Zhijian Liu
发布时间: 2026-02-05
分类: cs.CL
PDF: Download
简介
Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding, leading to high inference latency and poor GPU utilization. Speculative decoding mitigates this bottleneck by using a fast draft model whose outputs are verified in parallel by the target LLM; however, existing methods still rely on autoregressive drafting, which remains sequential and limits practical speedups. Diffusion LLMs offer a promising alternative by enabling parallel generation, but current diffusion models typically underperform compared with autoregressive models. In this paper, we introduce DFlash, a speculative decoding framework that employs a lightweight block diffusion model for parallel drafting. By generating draft tokens in a single forward pass and conditioning the draft model on context features extracted from the target model, DFlash enables efficient drafting with high-quality outputs and higher acceptance rates. Experiments show that DFlash achieves over 6x lossless acceleration across a range of models and tasks, delivering up to 2.5x higher speedup than the state-of-the-art speculative decoding method EAGLE-3.
推荐理由
论文 2 适合讨论：扩散模型与自回归 LLM 的结合是新兴方向，6x 加速的实际工程价值需要评估
讨论
请对这篇论文发表您的见解：
- 论文的创新点是什么？
- 方法是否合理？
- 实验结果是否可信？
- 有哪些可以改进的地方？
由 arXiv Monitor 自动创建