Senior Researcher
Microsoft Research AI for Science

Google Scholar | Github

guoqingliu (at) microsoft.com
21 Station Road, Cambridge, CB1 2FB, United Kingdom

• 2025-09: Our paper Accelerating protein engineering with fitness landscape modelling and reinforcement learning is published in Nature Machine Intelligence!
• 2025-02: Our paper HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model is available at arXiv!
• 2025-02: Our paper NatureLM: Deciphering the Language of Nature for Scientific Discovery is available at arXiv!
• 2024-12: Our paper Chemist-aligned retrosynthesis by ensembling diverse inductive bias models is available at arXiv!
• 2024-07: Our paper Reinforcement Learning from Bagged Reward is accepted to TMLR!
• 2024-05: Our paper Token-level Direct Preference Optimization is accepted to ICML 2024!
• 2024-01: Our paper Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers is accepted to ICLR 2024!
• 2023-10: Our paper De novo Drug Design using Reinforcement Learning with Multiple GPT Agents is accepted to NeurIPS 2023!
• 2023-04: Our paper Retrosynthetic Planning with Dual Value Networks is accepted to ICML 2023!

Guoqing Liu is a Senior Researcher at Microsoft Research AI for Science. His research interests encompass reinforcement learning (RL), the intersection of large language models (LLMs) and RL (e.g., reasoning and agents in natural language form), and their applications in scientific discovery. He gained extensive experience in deep RL research and its real-world applications (e.g., Project Suphx: Mahjong AI, Project Mariana: Game Testing) during his Ph.D. studies and his first year as a researcher at Microsoft Research Asia. More recently, his work has focused on LLM reasoning, RL agents, and their applications in drug discovery (e.g., PDVN, RetroChimera, NatureLM, and MuProtein). Prior to joining Microsoft Research, he earned his Ph.D. in 2021 from the University of Science and Technology of China (USTC) through a joint program with Microsoft Research Asia, supervised by Tie-Yan Liu and Nenghai Yu.

("*": equal contribution; "†": correspondence)
1. Accelerating protein engineering with fitness landscape modelling and reinforcement learning [Paper]
   Haoran Sun*, Liang He*, Pan Deng*, Guoqing Liu*, Zhiyu Zhao, Yuliang Jiang, Chuan Cao, Fusong Ju, Lijun Wu, Haiguang Liu, Tao Qin, Tie-Yan Liu
   Nature Machine Intelligence 2025
   
2. HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model [Paper]
   Mingqian Ma*, Guoqing Liu*, Chuan Cao*, Pan Deng*, Tri Dao, Albert Gu, Peiran Jin, Zhao Yang, Yingce Xia, Renqian Luo, Pipi Hu, Zun Wang, Yuan-Jyue Chen, Haiguang Liu, Tao Qin. arXiv 2025.
   
3. NatureLM: Deciphering the Language of Nature for Scientific Discovery [Paper]
   NatureLM Team, Co-first author, Microsoft Research AI for Science. arXiv 2025.
   
4. 3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery [Paper]
   Xiuyuan Hu, Guoqing Liu†, Can Chen, Yang Zhao, Hao Zhang, Xue Liu
   Thirteenth International Conference on Learning Representations (ICLR 2025)
5. Chemist-aligned retrosynthesis by ensembling diverse inductive bias models [Paper]
   Krzysztof Maziarz*, Guoqing Liu*, Hubert Misztela, Austin Tripp, Junren Li, Aleksei Kornev, Piotr Gaiński, Holger Hoefling, Mike Fortunato, Rishi Gupta, Marwin Segler. arXiv 2024.
   
6. Reinforcement Learning from Bagged Reward [Paper]
   Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama
   Transactions on Machine Learning Research (TMLR 2024)
7. Token-level Direct Preference Optimization [Paper]
   Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, Jun Wang
   Forty-first International Conference on Machine Learning (ICML 2024)
8. Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers [Paper]
   Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang
   Twelfth International Conference on Learning Representations (ICLR 2024)
9. Re-evaluating Retrosynthesis Algorithms with Syntheseus [Paper]
   Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl, Marwin Segler
   Faraday Discussions 2025
10. De novo Drug Design using Reinforcement Learning with Multiple GPT Agents [Paper]
    Xiuyuan Hu, Guoqing Liu†, Yang Zhao, Hao Zhang
    Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
11. QuinNet: Efficiently Incorporating Quintuple Interactions into Geometric Deep Learning Force Fields [Paper]
    Zun Wang, Guoqing Liu, Yichi Zhou, Tong Wang, Bin Shao
    Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
12. Retrosynthetic Planning with Dual Value Networks [Paper]
    Guoqing Liu*, Di Xue*, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu
    Fortieth International Conference on Machine Learning (ICML 2023)
13. You May Not Need Ratio Clipping in PPO [Paper]
    Mingfei Sun, Vitaly Kurin, Guoqing Liu, Sam Devlin, Tao Qin, Katja Hofmann, Shimon Whiteson. arXiv 2022.
    
14. Inspector: Pixel-based Automated Game Testing via Exploration, Detection, and Investigation [Paper]
    Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff and Tie-Yan Liu
    IEEE Conference on Games 2022 (COG 2022, Oral)
15. Independence-aware Advantage Estimation [Paper]
    Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minlie Huang, Tao Qin, Tie-Yan Liu
    30th International Joint Conference on Artificial Intelligence (IJCAI 2021)
16. Return-based Contrastive Representation Learning for Reinforcement Learning [Paper]
    Guoqing Liu*, Chuheng Zhang*, Li Zhao, Tao Qin, Jinhua Zhu, Jian Li, Nenghai Yu, Tie-Yan Liu
    Ninth International Conference on Learning Representations (ICLR 2021)
17. Demonstration Actor Critic [Paper]
    Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie- Yan Liu
    Neurocomputing, Volume 434, 28 April 2021, Pages 194-202 (Neurocomputing 2021)
18. Suphx: Mastering Mahjong with Deep Reinforcement Learning [Paper][News]
    Junjie Li, Sotetsu Koyamada, Qiwei Ye, Guoqing Liu, Chao Wang, Ruihan Yang, Li Zhao, Tao Qin, Tie-Yan Liu, Hsiao-Wuen Hon. arXiv 2020.
    
19. Trust Region Evolution Strategies [Paper]
    Guoqing Liu, Li Zhao, Feidiao Yang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu
    Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)

• Sep.2016 - Jun.2021, Cyberspace Security, University of Science and Technology of China, Doctor of Philosophy (Ph.D.)
• Sep.2012 - Jun.2016, Electronic Engineering & Information Science, University of Science and Technology of China, Bachelor of Engineering (B.E.)

• PC member: ICLR, ICML, NeurIPS, AAAI, AAMAS
• Journal reviewer: TPAMI, Neurocomputing, Nature Computational Science

• Aug.2016, Microsoft Research Asia, Stars of Tomorrow Internship Award
• Sep.2013, Sep.2014, Sep.2015, University of Science and Technology of China, Excellent Student Scholarship of USTC
• Dec.2013, Chinese Mathematical Society (CMS), First Price of National College Students Mathematical Competition
• Oct.2011, Chinese Mathematical Society (CMS), First Price of National High School Mathematics League