Chao Du

Senior Research Scientist
Sea AI Lab
Email: duchao0726 [at] gmail (dot) com; duchao [at] sea (dot) com

[Google Scholar] [Github] [Twitter]

I am a senior research scientist at Sea AI Lab. I finished my Ph.D. in 2019 at TSAIL Group in the Department of Computer Science and Technology at Tsinghua University, advised by Prof. Jun Zhu and Prof. Bo Zhang. And I received my B.Eng. from the Yao Class in Institute for Interdisciplinary Information Sciences at Tsinghua University in 2014.

I work on machine learning. My research goal is to understand what intelligence is and how it arises, and to answer these questions from the view of probability and statistics. I am particularly focused on developing probabilistic methods for generative modeling from first principles, aiming to improve their capacity, interpretability, and controllability.

We are hiring Research Scientist and Research Intern to work on generative models (e.g., diffusion models, LLMs). Please feel free to contact me if you are interested.

Selected Publications and Preprints

    Generative Modeling
  • Nonparametric Data Attribution for Diffusion Models
    Yutian Zhao*, Chao Du*, Xiaosen Zheng, Tianyu Pang, Min Lin
    Preprint, 2025
    [Paper] [Code]
  • On Memorization in Diffusion Models
    Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang
    Transactions on Machine Learning Research (TMLR), 2025
    [Paper] [Code]
  • 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), Singapore, 2025
    [Paper] [Code]
  • Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
    Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin
    International Conference on Machine Learning (ICML), Hawaii, USA, 2023
    [Paper] [Code] [Slides] [Poster]
  • Reinforcement Learning
  • Continual Reinforcement Learning by Planning with Online World Models
    Zichen Liu, Guoji Fu, Chao Du, Wee Sun Lee, Min Lin
    International Conference on Machine Learning (ICML), Vancouver, Canada, 2025
    [Paper] [Code]
  • Locality Sensitive Sparse Encoding for Learning World Models Online
    Zichen Liu, Chao Du, Wee Sun Lee, Min Lin
    International Conference on Learning Representations (ICLR), Vienna, Austria, 2024
    [Paper] [Code in Appendix]
  • RL for Language Models
  • Reinforcing General Reasoning Without Verifiers
    Xiangxin Zhou, Zichen Liu, Anya Sims, Haonan Wang, Tianyu Pang, Chongxuan Li, Liang Wang, Min Lin, Chao Du
    Preprint, 2025
    [Paper] [Code]
  • Sample-Efficient Alignment for LLMs
    Zichen Liu, Changyu Chen, Chao Du, Wee Sun Lee, Min Lin
    Preprint, 2024
    [Paper] [Code]

Services

  • Area Chair: ICLR 2026
  • Reviewer: ICML, NeurIPS, ICLR, AISTATS, CVPR, ICCV, COLM, AAAI, SIGKDD, TPAMI

Contact Me

duchao0726 at gmail dot com