教育经历
2005年-2009年，西安交通大学，获学士学位
2009年-2011年，香港科技大学，获硕士学位
2013年-2017年，新加坡国立大学，获博士学位
工作经历
2025年11月-至今，复旦大学人工智能创新与产业研究院，研究员
2022年8月-2025年11月，无限光年，负责大模型后训练
2022年1月-2022年8月，字节跳动，广告核心部门工作
2018年12月-2021年12月，蚂蚁集团，人工智能部工作
2017年12月-2018年12月，以色列理工学院，博士后
研究方向
现阶段研究方向如下：Reinforcement learning、 LLM post-training、AI for science、 Optimization
主要成果
多篇论文发表在 ICML、Neurips、ICLR 等A类会议上
主导项目在蚂蚁、字节的工业系统中落地
搭建无限光年LLM强化学习数据、异步训练框架、算法全链路
招生专业
依托计算与智能创新学院招生，专业为计算机科学与技术。
发表论文
1. VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning.Haozhe Wang, Chao Qu, Zuming Huang, Wei Chu, Fangzhen Lin, Wenhu Chen. NeurIPS 2025 （spotlight）.
2. Atomic Thinking of LLMs: Decoupling and Exploring Mathematical Reasoning Abilities. Jiayi Kuang, Haojing Huang, Yinghui Li, Xinnian Liang, Zhikun Xu, Yangning Li, Xiaoyu Tan, Chao Qu, Meishan Zhang, Ying Shen, Philip S. Yu. NeurIPS 2025.
3. Equivariant Masked Position Prediction for Efficient Molecular Representation. Junyi An*, Chao Qu*, Yun-Fei Shi, XinHao Liu, Qianwei Tang, Fenglei Cao, Yuan Qi. ICLR 2025.
4. Refine Knowledge of Large Language Models via Adaptive Contrastive Learning. Yinghui Li, Haojing Huang, Jiayi Kuang, Yangning Li, Shu-Yu Guo, Chao Qu, Xiaoyu Tan, Hai-Tao Zheng, Ying Shen, Philip S. Yu. ICLR 2025.
5. One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs. Yinghui Li, Jiayi Kuang, Haojing Huang, Zhikun Xu, Xinnian Liang, Yi Yu, Wenlian Lu, Yangning Li, Xiaoyu Tan, Chao Qu, Ying Shen, Hai-Tao Zheng, Philip S. Yu. ICML 2025.
6. Hybrid Directional Graph Neural Network for Molecules. Junyi An*, Chao Qu*, Zhipeng Zhou, Fenglei Cao, Yinghui Xu, Yuan Qi, Furao Shen. ICLR 2024 (Spotlight)
7. ILTS: Inducing Intention Propagation in Decentralized Multi-Agent Tasks with Large Language Models. Xihe Qiu, Haoyu Wang, Xiaoyu Tan, Chao Qu. CIKM 2024.
8. Subequivariant Reinforcement Learning Framework for Coordinated Motion Control. Haoyu Wang, Xiaoyu Tan, Xihe Qiu, Chao Qu. ICRA 2024.
9. LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints. Weidi Xu, Jingwei Wang, Lele Xie, Jianshan He, Hongting Zhou, Taifeng Wang, Xiaopei Wan, Jingdong Chen, Chao Qu, Wei Chu. ICLR 2024.
10. Provably Invariance Learning without Domain Information. Xiaoyu Tan, LIN Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Xu Yinghui, Peng Cui, Yuan Qi. ICML 2023.
11. Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes. Chao Qu*, Xiaoyu Tan*, Siqiao Xue, Xiaoming Shi, James Zhang, Hongyuan Mei. AAAI 2023.
12. A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud. Siqiao Xue*, Chao Qu*, Xiaoming Shi, Cong Liao, Shiyi Zhu, Xiaoyu Tan, Lintao Ma, Shiyu Wang, Shijun Wang, Yun Hu, Lei Lei, Yangfei Zheng, Jianguo Li, James Zhang. KDD 2022.
13. Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning. Chao Qu, Shie Mannor, Huan Xu, Junwu Xiong, Yuan Qi, Le Song. NeurIPS 2019.
14. Nonlinear Distributional Gradient Temporal-Difference Learning. Chao Qu, Shie Mannor, Huan Xu. ICML 2019.
15. Non-convex Conditional Gradient Sliding. Chao Qu, Yan Li, Huan Xu. ICML 2018 .
16. Fast Rate Analysis of Some Stochastic Optimization Algorithms. Chao Qu, Chongjing Ong, Huan Xu. ICML 2016.
17. Subspace Clustering with Irrelevant Features via Robust Dantzig Selector. Chao Qu, Huan Xu. NeurIPS 2015.
