Hello! I’m Shangzhe Li, a Ph.D. student at UNC Chapel Hill, where I’m fortunate to work under the guidance of Prof. Weitong Zhang. I also collaborate closely with Prof. Hao Su at the University of California, San Diego. Previously, I’ve had the opportunity to work with Prof. Marco Caccamo and Prof. Nils Thuerey at the Technical University of Munich, as well as with Prof. Xinhua Zhang at the University of Illinois at Chicago.

I’m passionate about aviation, Physics, and Mathematics, and I’m pursuing my studies in Artificial Intelligence. My hometown is Guangzhou. In my free time, I enjoy live streaming on Bilibili, sharing insights on Zhihu, and indulging in my love for anime.

My research interests lie broadly in the fields of reinforcement learning and imitation learning, with a focus on both algorithmic design and practical applications:

  • Deep Reinforcement Learning: I’m interested in developing novel reinforcement learning algorithms powered by deep neural networks to tackle complex decision-making problems. Lately, I’ve been particularly focused on model-based RL.

  • Imitation Learning: I explore new approaches to imitation learning, especially adversarial imitation learning in complex, high-dimensional environments. I’m also excited about leveraging large foundation models within imitation learning frameworks.

  • Theoretical Foundations of RL/IL: I aim to better understand the theoretical underpinnings of reinforcement and imitation learning, with the goal of using theory to inform and inspire more effective algorithmic designs.

  • Applications in Post-training for Foundation Models and Robot Learning: I’m interested in applying RL to enhance the post-training (e.g., alignment, distillation, and reasoning) for large foundation models (e.g., LLMs, VLMs, VLAs and Video Generation Models), as well as to solve challenging robotic control problems in manipulation and locomotion.

🔥 News

  • 2025.09, One paper has been accepted by NeurIPS 2025 Workshop on Embodied World Models.
  • 2025.05, One paper has been accepted by ICML 2025.
  • 2025.03, I’ll be joining UNC Chapel Hill for my PhD, advised by Prof. Weitong Zhang!
  • 2025.03, One paper has been accepted by ICLR 2025 Workshop on World Models.
  • 2024.03, Summer intern offer received from Su Lab, UCSD! See you in San Diego in summer if everything goes smoothly!
  • 2023.09,  🎉🎉 Homepage has been set up.

📝 Publications

†: Equal contributions.

arXiv Preprint
arXiv Preprint

A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models

Yilin Wang †, Shangzhe Li †, Haoyi Niu, Zhiao Huang, Weitong Zhang, Hao Su

arXiv Preprint
ICML 2025

🧑‍💼 Professional Service

  • Reviewer/Program Committee Member: AAAI 2026, ICLR 2026, ICRA 2026.

📚 Teaching

  • Teaching Assistant, DATA 110: Introduction to Data Science, UNC Chapel Hill

📖 Blog Articles

Notice: All of the articles here are written in Chinese.

Physics Part:

Mathematics Part:

Convex Optimization Part: