Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion
  Forcing
Authors: name: Xu Wang, name: Chenkai Xu, name: Yijie Jin, name: Jiachun Jin, name: Hao Zhang, name: Zhijie Deng

Diffusion Large Language Models (dLLMs) have emerged as a promising
alternative to autoregressive (AR) LLMs for text generation, with the potential
to decode multiple tokens in a single iteration. However, none of the existing
open-source dLLMs have achieved superior inference speed over AR LLMs of
similar size. This paper breaks this barrier based on a simple and effective
strategy named discrete diffusion forcing (D2F). D2F equips dLLMs with two key
capabilities: (1) block-wise autoregressive generation to enable KV cache
utilization; (2) prediction of following tokens without requiring completion of
prior blocks for inter-block parallel decoding. In this way, the vanilla dLLMs
are refurbished into an AR-diffusion hybrid paradigm for efficient inference.
D2F can be implemented with an asymmetric distillation process based on
pre-trained dLLMs. We further propose a pipelined parallel decoding algorithm,
which enables a trade-off between efficiency and efficacy. Empirically, D2F
dLLMs achieve more than 2.5times inference speed than LLaMA3 and
Qwen2.5 on GSM8K. Compared to vanilla dLLMs like LLaDA and Dream, the
acceleration can be more than 50times while maintaining comparable
output quality. The code is available at
https://github.com/zhijie-group/Discrete-Diffusion-Forcing.