DFlash: Block Diffusion for Flash Speculative Decoding
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...
| Authors | Jian Chen, Yesheng Liang, Zhijian Liu |
|---|---|
| Primary category | cs.CL |
| Published | 5 February 2026 |
| Last updated at source | 28 May 2026 |
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