DFlash: Block Diffusion for Flash Speculative Decoding

• URL: http://arxiv.org/abs/2602.06036v1
• Date: Thu, 05 Feb 2026 18:59:30 GMT
• Title: DFlash: Block Diffusion for Flash Speculative Decoding
• Authors: Jian Chen, Yesheng Liang, Zhijian Liu,
• Abstract summary: Autoregressive large language models (LLMs) deliver strong performance but require inherently sequential decoding.<n>We introduce DFlash, a speculative decoding framework that employs a lightweight block diffusion model for parallel drafting.
• Score: 11.98141750480807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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