Chongxuan Li(李崇轩)

Associate Professor
Gaoling School of AI, Renmin University of China
Email: chongxuanli [at] ruc (dot) edu (dot) cn

[Google Scholar] [Profiles-CN] [Interview-CN]

Machine Learning Group (GSAI-ML)
[GSAI-ML Github] [GSAI-ML 知乎]

I am a tenure-track associate professor at Gaoling School of AI, Renmin University of China. I recieved my Ph.D. in the Department of Computer Science and Technology at Tsinghua University, supervised by Prof. Bo Zhang and Prof. Jun Zhu. During my Ph.D., I had spent a great year at AMLab, working with Prof. Max Welling. I received the B.E. degree from Institute for Interdiscriplinary Information Sciences at Tsinghua University in 2014.

My group focuses on deep generative models, which aim to characterize the distribution of the input data and simulate the underlying world. Our mission is to understand the power and limitation of existing models, design scalable and effecient next-generation models, and develop principled and effective algorithms for AIGC applicaitons.

Selected Publications Full Publications

    Diffusion language models
  • Large Language Diffusion Models
    Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li
    Preprint, 2025 [Paper] [Code] [Demo]
  • Scaling up Masked Diffusion Models on Text
    Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, Chongxuan Li
    International Conference on Learning Representations (ICLR), 2025 [Paper] [Code]
  • Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data
    Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, Chongxuan Li
    International Conference on Learning Representations (ICLR), 2025 [Paper] [Code]
  • 3D world models
  • FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis
    Luxi Chen, Zihan Zhou, Min Zhao, Yikai Wang, Ge Zhang, Wenhao Huang, Hao Sun, Ji-Rong Wen, Chongxuan Li
    Preprint, 2025 [Paper] [Code] [Demo]
  • ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
    Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li†, Hang Su, Jun Zhu†
    Advances in Neural Information Processing Systems (NeurIPS), 2023 [Paper] [Code] [Demo]
  • Diffusion transformers
  • RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
    Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li†, Jun Zhu†
    Preprint, 2025 [Paper] [Code] [Demo]
  • All are Worth Words: a ViT Backbone for Diffusion Models
    Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li†, Hang Su, Jun Zhu†
    The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023 [Paper] [Code]
  • Fast sampling for diffusion models
  • Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
    Fan Bao, Chongxuan Li†, Jun Zhu†, Bo Zhang
    International Conference on Learning Representations (ICLR), 2022 [Paper] [Code]
    Oral, Outstanding Paper Award (7/3391)
  • DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
    Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
    Advances in Neural Information Processing Systems (NeurIPS), 2022 [Paper] [Code]

GSAI-ML Group

PhD Students
  • Zhengyi Wang (with Jun Zhu and Hang Su)
  • Yong Zhong
  • Shen Nie
  • Zebin You
  • Rongzhen Wang
  • Kaiwen Xue
  • Chenyu Zheng
  • Luxi Chen
  • Fengqi Zhu
Master Students
  • Zihan Zhou
Undergraduate
  • Jingyang Ou
  • Sitong Chen
  • Bokai Yan

Alumni

  • Min Zhao, PhD (with Jun Zhu), 2020-2024, Shuimu Program, now Postdoc at Tsinghua University
  • Cheng Lu, PhD (with Jun Zhu and Jianfei Chen), 2019-2023, ByteDance Scholarship, now research scientist at OpenAI
  • Fan Bao, PhD (with Jun Zhu), 2019-2023, ByteDance Scholarship, now CTO at ShengShu
  • Kun Xu, PhD (with Jun Zhu), 2016-2021, now at Citadel
  • Hanzhong Guo, master, 2022-2024, now PhD at HKU
  • Tsung Wei Tsai, master (with Jun Zhu), 2019-2021, now at Goldman Sachs
  • Selected Awards

    Talk

    • Diffusion Models and AIGC
      Tutorial in Vision and Learning Seminar (VALSE) @ Wuxi, 2023
      [Slides in Chinese]
    • Diffusion Probabilistic Models: Foundations, Fast Inference and Controllable Generation
      Forum of Techniques in Foundation Model, Beijing Academy of Artificial Intelligence BAAI, online, 2022-12-17
      [Slides] [Blog]
    • Deep Generative Models: Representation, Learning and Inference
      Tutorial of unsupervised learning in Vision and Learning Seminar (VALSE) @ Hangzhou, 2021
      A short version is also presented in MLNLP community, online, 2021
      [Slides]

    Teaching

    • Deep Generative Models: Principles and Applications (Online) Available at BiliBili
    • Probability and Randomized Algorithms (Graduate, Autumn, since 2021)
    • Deep Generative Models: Principles and Applications (Graduate, Spring, since 2022)
    • Probabilistic Graphical Models: Principles and Applications (Undergraduate, Spring, since 2023)

    Book Chapter

    • 可解释人工智能导论(第二章 贝叶斯方法),电子工业出版社,2022年4月

    Service

    • Associate Editor: IEEE TPAMI
    • 责任编委: 软件学报
    • Area Chair: NeurIPS, ICLR, ACM MM
    • Reviewer: Nature Communications
    Last updated on Feb. 2025. Special thanks to Chao Du and Jiajun Wu for the style files of the homepage.