Introduction

My name is Chao Yu (于超). I received my Ph.D. from the Department of Electronic Engineering at Tsinghua University in 2023. I am currently an Assistant Professor (Distinguished Research Fellow) at the Embodied Decision Intelligence Lab (EDI Lab) at SIGS of Tsinghua University. The EDI Lab is a sub-group of the Nanoscale Integrated Circuits and System Lab, Energy Efficient Computing Group (NICS-EFC) at Tsinghua University’s main campus. I also serve as the chairman of the SIGS of Tsinghua University - AgiBot Joint Research Center for Embodied Cognition and Decision Systems (JCES) and the co-founder of Striding AI (正行创新). I have been selected for the Youth Talent Support Program of the Chinese Institute of Electronics. My research has long focused on reinforcement learning-based decision intelligence. As first author or corresponding author, I have published more than 50 papers in top-tier international conferences and journals, including ICML, NeurIPS, ICLR, CVPR, ECCV, CoRL, IROS, ICRA, TMLR, and RAL, with over 7,000 citations on Google Scholar. My representative works include the multi-agent reinforcement learning algorithm MAPPO, which has received more than 4,000 Google Scholar citations, and RLinf, a large-scale reinforcement learning training framework for embodied intelligence, which has accumulated over 4,000 GitHub stars.

Feel free to reach out if you’d like to discuss research or explore potential collaboration!

Our lab is currently recruiting Master’s students, Ph.D. students, joint-program Ph.D. students with Zhongguancun Academy, postdoctoral researchers, and undergraduate research assistants. We warmly welcome students who are interested in recommended admission or applying through the graduate entrance examination to SIGS programs such as Artificial Intelligence, Data Science and Information Technology, Big Data Engineering, and Electronic Engineering, as well as applicants for the above Ph.D. and postdoctoral positions, to join us.

Highlights

VS-Bench: Evaluating VLMs for Strategic Reasoning and Decision-Making in Multi-Agent Environments

Zelai Xu, Zhexuan Xu, Xiangmin Yi, Huining Yuan, Xinlei Chen, Yi Wu, Chao Yu, Yu Wang

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), Oral  ·  2026

RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation

Chao Yu, Yuanqing Wang, Zhen Guo, Hao Lin, Si Xu, Hongzhi Zang, Quanlu Zhang, Yongji Wu, Chunyang Zhu, Junhao Hu, Zixiao Huang, Mingjie Wei, Yuqing Xie, Ke Yang, Bo Dai, Zhexuan Xu, Xiangyuan Wang, Xu Fu, Zhihao Liu, Kang Chen, Weilin Liu, Gang Liu, Boxun Li, Jianlei Yang, Zhi Yang, Guohao Dai, Yu Wang*

20th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2026)  ·  2026

Code

RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models

Hongzhi Zang, Mingjie Wei, Si Xu, Yongji Wu, Zhen Guo, Yuanqing Wang, Hao Lin, Liangzhi Shi, Yuqing Xie, Zhexuan Xu, Zhihao Liu, Kang Chen, Wenhao Tang, Quanlu Zhang, Weinan Zhang, Chao Yu, Yu Wang

Robotics: Science and Systems (RSS 2026)  ·  2026

Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game

Zelai Xu, Chao Yu, Fei Fang, Yu Wang, Yi Wu

International Conference on Machine Learning (ICML 2024)  ·  2024

Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Shusheng Xu, Wei Fu, Jiaxuan Gao, Wenjie Ye, Weilin Liu, Zhiyu Mei, Guangju Wang, Chao Yu, Yi Wu

International Conference on Machine Learning (ICML 2024), Oral  ·  2024

Code

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games

Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, Yi Wu

Conference on Neural Information Processing Systems (NeurIPS 2022), Datasets and Benchmarks Track  ·  2022

Code

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