8 papers
Reinforcing the Generation Order of Multimodal Masked Diffusion Models
Yidong Ouyang, Zhe Wang, Sourav Bhabesh +1
Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation order…
Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
Lixing Zhang, Yidong Ouyang, Weifu Li +3
Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many re…
Corrected Samplers for Discrete Flow Models
Zhengyan Wan, Yidong Ouyang, Liyan Xie +3
Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A…
Error Analysis of Discrete Flow with Generator Matching
Zhengyan Wan, Yidong Ouyang, Qiang Yao +4
Discrete flow models offer a powerful framework for learning distributions over discrete state spaces and have demonstrated superior performance compared to the discrete diffusion…
dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models
Zhengyan Wan, Yidong Ouyang, Panwen Hu +1
Discrete flow models (DFMs) are a class of flexible generative models for generating discrete data, and diffusion large language models (dLLMs) can be viewed as a special case with…
Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
Zhengyan Wan, Yidong Ouyang, Liyan Xie +3
Guidance provides a simple and effective framework for posterior sampling by steering the generation process towards the desired distribution. When modeling discrete data, existing…