arXiv AI
Oct 12

One-Step Text Generation by Seq-Drifting

The paper introduces Seq-Drifting, a new framework that enables diffusion language models to generate an entire text sequence in a single forward pass. By applying a drift field to continuous token representations, the model learns a direct mapping from Gaussian noise to text, using attraction to guide outputs toward contextually plausible tokens and repulsion to prevent repetition and collapse. Experiments show that Seq-Drifting achieves a perplexity of 36.01 on LM1B and performs competitively in unconditional, conditional, translation, summarization, and reasoning tasks.

By Jiayu Liu, Ruizhi Zhao, Tianyun Ji, Yee Whye Teh, Zhenya Huang, Ning Miao
arXiv Computation and Language
Sep 14

Representation-based Masked Diffusion Model

The paper introduces Representation-based Masked Diffusion Model (RMDM), a new framework for language modeling that improves upon existing Masked Diffusion Models by incorporating global semantic guidance. RMDM encodes text into a continuous semantic space with a pretrained encoder, normalizes this representation to a Gaussian prior via an invertible transformation, and then trains a masked diffusion model conditioned on this latent representation to coordinate parallel token updates. Experiments show that RMDM yields higher generation quality, especially when using aggressive few‑step sampling.

By Yangrong Hu, Ding Huang, Xueyu Zhou, Jian Huang