D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding

Brief overview

D3D breaks the dependency on the hidden states of the target model through an independent diffuser, achieving asynchronous drafting and verification. In seven benchmarks, D3D combined with block denoising and auxiliary autoregressive supervision increases the average decoding speed of Qwen3-8B and Qwen3-32B by 5.00 times and 5.71 times respectively, outperforming DFlash (4.03 times and 3.67 times), and reaching 7.48 times acceleration on GSM8K. The code, model checkpoints, and benchmark scripts are open source.

Article

🚀 Introducing D3D: Decoupled Diffusion Drafting for Asynchronous Speculative Decoding!

Existing diffusion-based speculative drafters rely on target-model hidden states, forcing drafting and verification to execute sequentially.

D3D breaks this dependency with a standalone diffusion drafter, enabling asynchronous drafting and verification through packed speculative decoding.

Key highlights:

Asynchronous speculation: Overlaps diffusion drafting with target verification, eliminating inter-round dependencies.
Effective drafting: Combines blockwise denoising with auxiliary autoregressive supervision to achieve long accepted prefixes.
Significant speedups: Achieves 5.00× and 5.71× average decoding speedups with Qwen3-8B and Qwen3-32B across seven benchmarks, outperforming DFlash (4.03× and 3.67×). Up to 7.48× on GSM8K!

📄 Paper: https://arxiv.org/abs/2611.06425

💻 Code: https://github.com/fudan-generative-vision/D3D

🤗 Models: https://huggingface.co/collections/fudan-generative-ai/d3d

We've released our code, model checkpoints, and benchmarking scripts.

We welcome feedback, questions, and discussions!