Runzhe Wu

Hi, I'm Runzhe Wu, a Ph.D. candidate in Computer Science at Cornell Tech in New York City, advised by Wen Sun. My research focuses on reinforcement learning.

Prior to Cornell Tech, I spent the first three years of my Ph.D. at the beautiful Ithaca campus of Cornell University. Before that, I obtained the Bachelor's degree in Computer Science from ACM Honors Class at Shanghai Jiao Tong University, where I conducted research at the APEX Lab under Weinan Zhang and Yong Yu.

I am interning at Meta in NYC during Summer 2025.

You can reach me via email at rw646 at cornell dot edu.

CV / Google Scholar / Twitter

Publications

Diffusing States and Matching Scores: A New Framework for Imitation Learning
Runzhe Wu, Yiding Chen, Gokul Swamy, Kianté Brantley, Wen Sun
ICLR, 2025
[Code]

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
Runzhe Wu*, Ayush Sekhari*, Akshay Krishnamurthy, Wen Sun
ICLR, 2025 (Oral — top 1.8%)
[Talk at RL Theory Seminars]

Making RL with Preference-based Feedback Efficient via Randomization
Runzhe Wu, Wen Sun
ICLR, 2024

Contextual Bandits and Imitation Learning via Preference-Based Active Queries
(Alphabetical order) Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
NeurIPS, 2023 (Also at the ILHF & MFPL Workshops @ ICML, 2023)
[Code]

Selective Sampling and Imitation Learning via Online Regression
(Alphabetical order) Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
NeurIPS, 2023 (Also at the ILHF Workshop @ ICML, 2023)

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun
NeurIPS, 2023
[Code]

Distributional Offline Policy Evaluation with Predictive Error Guarantees
Runzhe Wu, Masatoshi Uehara, Wen Sun
ICML, 2023
[Code]

MALib: A parallel framework for population-based multi-agent reinforcement learning
Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang
JMLR, 2023
[Website] [Code]

Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration
Runzhe Wu, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang
NeurIPS, 2021

Invited Talks

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
@ ICLR 2025, Oral Presentation (Apr 26, 2025) [Slides] [Recording (1:12:00-1:25:00)]
@ RL Theory Seminars (Nov 19, 2024) [Slides] [Recording]

Education
	Cornell Tech Ph.D. in Computer Science (Transferred from Ithaca campus) May. 2025 - Present
	Cornell University Ph.D. in Computer Science (M.S. earned en route, then transferred to Cornell Tech) Aug. 2022 - May. 2025
	Shanghai Jiao Tong University B.Eng. in Computer Science Sep. 2018 - Jun. 2022

Website template from here.