270 citations · 1.4k across the 97 of their papers we have counts for
107 papers
Enabling Preference-driven Unlearning in Few-step Distilled Text-to-Image Diffusion Models
Gaurav Patel, Jun Fang, Greg Ver Steeg +2
Text-to-image diffusion models are increasingly distilled into few-step variants and being deployed to enable fast inference. However, their ability to generate harmful or undesire…
DEdit: Iterative Draft Editing for Speculative Decoding
Longxuan Yu, Bingsen Chen, Peng Shi +9
Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusion-based drafters further red…
Line-Coupled Language Model
Shiyuan Li, Shaorong Zhang, Zhaorui Yang +3
Autoregressive language models generate one token per decoding step, limiting the useful output of each forward pass. Although diffusion models, insertion-based decoding, and multi…
Revise, Don't Freeze: Sampler-Matched Training for Self-Correcting Masked Diffusion Language Models
Longxuan Yu, Shaorong Zhang, Yu Fu +3
Masked diffusion language models (MDLMs) re-predict every position at each denoising step, but standard samplers commit tokens once revealed, leaving this revision capability unuse…
DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs
Longxuan Yu, Yunshu Wu, Yu Fu +5
Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step…
Discrete Stochastic Localization for Non-autoregressive Generation
Yunshu Wu, Jiayi Cheng, Longxuan Yu +4
Continuous diffusion is a natural framework for non-autoregressive generation but has generally lagged behind masked discrete diffusion models (MDMs) on discrete sequence generatio…