I’m Chengyang Ying, a final-year Ph.D. student of TSAIL Group in the Department of Computer Science and Technology, Tsinghua University, advised by Prof. Jun Zhu and A/Prof. Hang Su. My research interest includes Machine Learning, Reinforcement Learning, Embodied AI, and RL Agents.

Before that, I obtained my Bachelor of Science degree from the Department of Mathematical Sciences of Tsinghua University in July 2021, majored in Mathematics and minored in Computer Application.

Email: ycy21@mails.tsinghua.edu.cn; yingcy17@gmail.com

Paper reading list of part recent research on Single Agent RL

Blog: A Useful Lemma for Several RL Results

Selected Publications [Full List]

• Exploratory Diffusion Model for Unsupervised Reinforcement Learning
  Chengyang Ying, Huayu Chen, Xinning Zhou, Zhongkai Hao, Hang Su, Jun Zhu
  International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026
  Oral (Accept rate~1.2%)
  [project page] [arXiv] [code]

• A$^2$Search: Ambiguity-Aware Question Answering with Reinforcement Learning
  Fengji Zhang, Xinyao Niu, Chengyang Ying, Guancheng Lin, Zhongkai Hao, Zhou Fan, Chengen Huang, Jacky Keung, Bei Chen, Junyang Lin
  International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026
  [arXiv] [code]

• Task Aware Dreamer for Task Generalization in Reinforcement Learning
  Chengyang Ying^∗, Xinning Zhou^∗, Zhongkai Hao, Hang Su, Songming Liu, Dong Yan, Jun Zhu
  SCIENCE CHINA Information Sciences (SCIS), 2026
  [arXiv] [code]

• ManiBox: Enhancing Embodied Spatial Generalization via Scalable Simulation Data Generations
  Hengkai Tan^∗, Xuezhou Xu^∗, Chengyang Ying^∗, Xinyi Mao, Zeyuan Wang, Songming Liu, Xingxing Zhang, Zhizhong Su, Hang Su, Jun Zhu
  [project page] [arXiv]

• Self-Consistent Model-based Adaptation for Visual Reinforcement Learning
  Xinning Zhou^∗, Chengyang Ying^∗, Yao Feng, Hang Su, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Montreal, Canada, 2025
  [arXiv]

• PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
  Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu
  Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
  [project page] [arXiv] [code] [video]

• On the Reuse Bias in Off-Policy Reinforcement Learning
  Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, 2023
  [arXiv] [code] [slide]

• Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
  Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, Jun Zhu
  International Conference on Learning Representations (ICLR), Kigali, Rwanda, 2023
  [arXiv] [code]

• Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications
  Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu
  [arXiv]

• GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
  Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu
  International Conference on Machine Learning (ICML), Baltimore, USA, 2022
  [arXiv]

• Towards Safe Reinforcement Learning via Constraining Conditional Value at Risk
  Chengyang Ying, Xinning Zhou, Hang Su, Dong Yan, Ning Chen, Jun Zhu
  International Joint Conference on Artificial Intelligence (IJCAI), Vienna, Austria, 2022
  [arXiv] [code] [slide] [ICML 2021 AML Workshop]

Services

Reviewer: NeurIPS 2022-2025, ICML 2024-2026, ICLR 2024-2026, IJCAI 2024-2026, AAAI 2025-2026, CVPR 2024-2026, ICCV 2025, ECCV 2026, ICRA 2026, AISTATS 2025-2026

Selected Awards

• Beijing Outstanding Gradates, 2021.6
• Tsinghua Excellent Gradates, Tsinghua University, 2021.6
• China National Scholarship, Tsinghua University, 2018.10
• The first prize of “Gao Jiao She Bei” national mathematical modeling competition for College Students, 2018.10
• Silver Award of Chinese Mathematical Olympiad (CMO), 2016.11

Teaching

• 2026 Spring, Head TA in Statistical Machine Learning, instructed by Prof. Jun Zhu
• 2024 Fall, TA in Advanced Machine Learning, instructed by Prof. Jie Tang and Prof. Jun Zhu
• 2022 Spring, TA in Deep Learning, instructed by A/Prof. Xiaolin Hu and Prof. Jun Zhu
• 2022 Spring, TA in Statistical Machine Learning, instructed by Prof. Jun Zhu
• 2021 Spring & 2021 Fall & 2023 Spring & 2024 Spring & 2025 Spring, TA in Discrete mathematics for Computer Science, instructed by A/Prof. Hang Su

Miscellaneous

Reading group slides:

• Foundation Models for Reinforcement Learning
• PaLM-E
• Generalization in Reinforcement Learning
• A Generalist Agent
• Meta Reinforcement Learning
• Max-Entropy Reinforcement Learning

Last update: January 2026 by Chengyang Ying