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Guozheng Ma

PhD Student
Nanyang Technological University

GUOZHENG001 (at) e.ntu.edu.sg


Short Bio

Currently, I am a second-year PhD student at NTUNanyang Technological University, working under the supervision of Dacheng Tao. Previously, I completed my master's studies and began my research journey at THU Tsinghua University. Earlier, I obtained my bachelor's degree at HNU Hunan University, where I cherished four wonderful years at the foothills of Yuelu Mountain.

My research goal is to unlock the true potential of Deep Reinforcement Learning toward practical real-world deployment. This requires RL agents capable of offline-online cooperation, handling multiple tasks, and never stopping learning in open-ended environments. To realize this vision, I currently focus on investigating the fundamental challenges inherent in DRL, particularly optimization pathologies, scalability limitations and exploration inefficiencies.

Meanwhile, I view RL as a paradigm for understanding and developing intelligence rather than just a technique. This inspires my interest in exploring its potential across domains, from large reasoning models and embodied agents to psychology and social sciences.

Greatness cannot be planned, so I play with joyful explorations!

News

Selected Publications [Google Scholar]

  1. ICML 2025
    Guozheng Ma*, Lu Li*, Zilin Wang, Li Shen, Pierre-Luc Bacon, Dacheng Tao
    International Conference on Machine Learning (ICML), 2025.

  2. ICLR 2024
    Guozheng Ma*, Lu Li*, Sen Zhang, Zixuan Liu, Zhen Wang, Yixin Chen, Li Shen, Xueqian Wang, Dacheng Tao
    International Conference on Learning Representations (ICLR), 2024.

  3. NeurIPS 2023
    Guozheng Ma, Linrui Zhang, Haoyu Wang, Lu Li, Zilin Wang, Zhen Wang, Li Shen, Xueqian Wang, Dacheng Tao
    Neural Information Processing Systems (NeurIPS), 2023.

  4. IJCV
    Guozheng Ma, Zhen Wang, Zhecheng Yuan, Xueqian Wang, Bo Yuan, Dacheng Tao
    International Journal of Computer Vision (IJCV), 2025.

Invited Talks

Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages.

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