I’m Qingfeng Lan, a PhD student at the University of Alberta, supervised by A. Rupam Mahmood. Generally, I’m interested in developing simple and efficient machine learning algorithms supported by sound theories and verified by rigorous experiments. In particular, my research focuses on designing continual (reinforcement) learning algorithms with higher sample, memory, and computation efficiency. I believe that the ability to extract, accumulate, and exploit knowledge continually and efficiently is essential for the success of artificial general intelligence in the real world. I have also worked on meta-learning, exploration, language modelling, and quantum reinforcement learning.
Please check my CV and Google Scholar for more information.

I am looking for internship / visiting opportunity in 2024.


Feel free to contact me if you want to share anything (research, idea, experience, etc.) with me.

  • Email: qlan3 [AT]
  • WeChat (微信): Lancelqf
  • Twitter: lancelan3


  • Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
    Haque Ishfaq*, Qingfeng Lan*, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
    ICLR 2024, Poster. [paper] [code]

  • Elephant Neural Networks: Born to Be a Continual Learner
    Qingfeng Lan, A. Rupam Mahmood
    ICML Workshop on High-dimensional Learning Dynamics 2023, Poster. [paper]

  • Learning to Optimize for Reinforcement Learning
    Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu
    arXiv preprint 2023. [paper] [code]

  • Overcoming Policy Collapse in Deep Reinforcement Learning
    Shibhansh Dohare, Qingfeng Lan, A. Rupam Mahmood
    EWRL 2023. [paper]

  • Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation
    Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood
    TMLR 2023, CoLLAs certification. [paper] [code]

  • Model-free Policy Learning with Reward Gradients
    Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, A. Rupam Mahmood
    AISTATS 2022, Poster. [paper] [code]

  • Variational Quantum Soft Actor-Critic
    Qingfeng Lan
    arXiv preprint 2021. [paper] [code]

  • Predictive Representation Learning for Language Modeling
    Qingfeng Lan, Luke Kumar, Martha White, Alona Fyshe
    arXiv preprint 2021. [paper]

  • Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
    Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White
    ICLR 2020, Poster. [paper] [code] [video]

  • Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
    Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russell Greiner
    NeurIPS Workshop on Causal Machine Learning 2019, Poster Spotlight. [paper]

  • A Deep Top-K Relevance Matching Model for Ad-hoc Retrieval
    Zhou Yang, Qingfeng Lan, Jiafeng Guo, Yixing Fan, Xiaofei Zhu, Yanyan Lan and Yue Wang, Xueqi Cheng
    CCIR 2018, Best Paper Award Candidate. [paper] [code]

Open-Source Code

  • Jaxplorer: A Jax reinforcement learning framework for exploring new ideas.
  • Optim4RL: A Jax framework of learning to optimize for reinforcement learning.
  • Explorer: A PyTorch reinforcement learning framework for exploring new ideas.
  • Gym Games: A collection of Gymnasium compatible games for reinforcement learning.
  • Quantum Explorer: A quantum reinforcement learning framework based on PyTorch and PennyLane.


  • University of Alberta, Sep 2020-Present

  • University of Alberta, Sep 2018-Aug 2020

    • Master of Science (Thesis-based) in Computing Science
    • Supervisor: Alona Fyshe
  • University of Chinese Academy of Sciences, Sep 2014-July 2018

    • Bachelor of Engineering, major in Computer Science and Technology, minor in Physics
    • Supervisor: Yanyan Lan; Tutor: Guojie Li
  • St Edmund Hall, University of Oxford, Oct 2017-Mar 2018