Biography

Shuze Daniel Liu is a Research Scientist at Meta, working on LLM fine-tuning with Reinforcement Learning. His research focuses on Reinforcement Learning. He received his Ph.D. in Computer Science from the University of Virginia, advised by Professor Shangtong Zhang, and his M.S. from Yale University. He regularly serves on the Program Committee for major AI conferences including ICML, NeurIPS, ICLR, and AAAI.

Journal Articles

• The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise.
  Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang.
  Journal of Machine Learning Research (JMLR), 2025.

• Optimal Pricing of Information.
  Shuze Daniel Liu, Weiran Shen, Haifeng Xu.
  Major Revision in Operations Research (OR), 2025.

Conference Publications

• The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise.
  Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang.
  Conference on Neural Information Processing Systems (NeurIPS), 2025.

• Doubly Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Daniel Liu, Claire Chen, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025

• Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning.
  Claire Chen*, Shuze Daniel Liu*, Shangtong Zhang.
  International Conference on Learning Representations (ICLR), 2025

• Efficient Multi-Policy Evaluation for Reinforcement Learning.
  Shuze Daniel Liu, Claire Chen, Shangtong Zhang.
  AAAI Conference on Artificial Intelligence (AAAI), 2025.
  Oral Presentation, 4.7%

• Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design.
  Shuze Daniel Liu, Shangtong Zhang.
  International Conference on Machine Learning (ICML), 2024.

• Optimal Pricing of Information.
  Shuze Daniel Liu, Weiran Shen, Haifeng Xu.
  ACM Conference on Economics and Computation (EC), 2021.

• Strengthening Smart Contracts to Handle Unexpected Situations.
  Shuze Daniel Liu, Farhad Mohsin, Oshani Seneviratne, Lirong Xia.
  IEEE International Conference on Decentralized Applications and Infrastructures, 2019.

* indicates equal contribution

Working Papers

• Optimal Policy Evaluation for Reinforcement Learning.
  Shuze Daniel Liu, Claire Chen, Will Ma, Shangtong Zhang.
  

• Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation.
  Claire Chen, Shuze Daniel Liu, Licheng Luo, Rohan Chandra, Nan Jiang, Shangtong Zhang.
  

• Offline Two-Player Zero-Sum Markov Games with KL Regularization.
  Claire Chen*, Yuheng Zhang*, Xinyu Liu, Zixuan Xie, Shuze Daniel Liu, Nan Jiang.
  

• Convergence of Two-Timescale Stochastic Approximation with Markovian Samples and Applications in Reinforcement Learning.
  Vagul Mahadevan, Claire Chen, Shuze Daniel Liu, Shangtong Zhang.
  

• Transformers Implement Nonlinear In-Context Reinforcement Learning: Convergence and Emergence.
  Zixuan Xie, Xinyu Liu, Claire Chen, Shuze Daniel Liu, Rohan Chandra, Shangtong Zhang.
  

• MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics.
  Xinyu Liu, Zixuan Xie, Amir Moeini, Claire Chen, Shuze Daniel Liu, Yu Meng, Aidong Zhang, Shangtong Zhang.
  

Program Committee

ICML, NeurIPS, ICLR, AAAI, AISTATS.

Guest Lecture

Reinforcement Learning from Human Feedback (Fall 2024),
Reinforcement Learning (Spring 2024).

Teaching Assistant

Reinforcement Learning, Machine Learning, Artificial Intelligence.