Wen Sun

I'm an Assistant Professor in the Computer Science Department at Cornell University. I'm also a part-time researcher at Microsoft Research NYC.

Prior to Cornell, I was a post-doc researcher at Microsoft Research NYC from 2019 to 2020. I completed my PhD at Robotics Institute, Carnegie Mellon University in June 2019, where I was advised by Drew Bagnell.

CV  /  PhD Thesis  /  Google Scholar  /  Email  

Prospective students, please read this.

My group works on Reinforcement Learning, AI, and Decision Making. The most recent research directions of the lab are

  • RL for generative models, e.g., fine-tune LLMs w/ RL, fine-tune diffusion models, The transformer RL and IL library
  • Algorithmic and theoretical foundations of RLHF, e.g., offline RLHF, contextual dueling bandits w/ active query
  • RL with offline and online data, e.g., hybrid RL
  • Core RL theory, e.g., Instance-dependent regret bounds via distributional RL
  • Representation learning in RL, e.g., theory of representation learning in RL, theory of representation transfer in RL
  • Learning in partially observable systems, e.g., PAC RL w/ general function approximation in POMDPs and PSRs
  • The softwares developed by our group can be found here.

    Fall 2023: CS 4780/5780 Introduction to Machine Learning

    Spring 2023: CS 6789 Foundations of Reinforcement Learning

    Spring 2021: CS 4789/5789 Introduction to Reinforcement Learning

    Recent Talks / Lectures/ Tutorials

  • Simons Institute (Sep 2022): Generalization and robustness in offline reinforcement learning
  • IJCAI 2022 Tutorial: Adversarial sequential decision making
  • Recorded lectures of CS4789 (Intro to RL) Spring 2021 are available here
  • COLT 2021 RL Tutorial: Statistical Foundations of RL, videos are here
  • Monograph
    Reinforcement Learning: Theory and Algorithms
    Alekh Agarwal, Nan Jiang, Sham Kakade, Wen Sun

    We are periodically making updates to the book draft. Content based on the courses taught by Nan at UIUC, the courses taught by Alekh and Sham at UW, and CS 6789 at Cornell.

    Dataset Reset Policy Optimization for RLHF
    Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Kiante Brantley, Dipendra Misra, Jason D. Lee, Wen Sun
    arXiv, 2024 [code]
    Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
    Andrew Bennett, Nathan Kallus, Miruna Oprescu, Wen Sun, Kaiwen Wang
    arXiv, 2024
    Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL
    Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun
    arXiv, 2024
    Learning to Generate Better Than Your LLM
    Jonathan D. Chang, Kiante Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun
    arXiv, 2023   [code]

    A new framework -- RL with Guided Feedback (RLGF), combining RL and pre-trained LLMs via principled interactive learning procedures.

    Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
    Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie
    arXiv, 2021  
    More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
    Kaiwen Wang, Owen Oertell, Alekh Agarwal, Nathan Kallus, Wen Sun
    ICML, 2024

    We show that distributional RL enables faster learning when the systems have low variance. This holds for contextual bandits, online and offline RL simoutaneously.

    JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
    Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun
    RLC, 2024   [code]

    A light weight, real-world database query optimization benchmark for RL

    RL for Consistency Models: Faster Reward Guided Text-to-Image Generation
    Owen Oertell, Jonathan D. Chang, Yiyi Zhang, Kiante Brantley, Wen Sun
    RLC, 2024 [Website]

    Faster Recalibration of an Online Predictor via Approachability
    Princewill Okoroafor, Bobby Kleinberg, Wen Sun
    AISTATS, 2024

    Provable Reward-Agnostic Preference-Based Reinforcement Learning
    Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
    ICLR, 2024   (Spotlight)
    Provable Offline Preference-Based Reinforcement Learning
    Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
    ICLR, 2024   (Spotlight)
    Adversarial Imitation Learning via Boosting
    Jonathan Chang, Dhruv Sreenivas, Yingbing Huang, Kiante Brantley, Wen Sun
    ICLR, 2024  
    Provably Efficient CVaR RL in Low-rank MDPs
    Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee
    ICLR, 2024  
    Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees
    Yifei Zhou, Ayush Sekhari, Yuda Song, Wen Sun
    ICLR, 2024  
    Making RL with Preference-based Feedback Efficient via Randomization
    Runzhe Wu, Wen Sun
    ICLR, 2024  

    A randomized algorithm for learning from preference feedback that achieves sample, computation, and query efficiency simoutaneously.

    Contextual Bandits and Imitation Learning via Preference-Based Active Queries
    (by alphabetic order) Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
    NeurIPS, 2023   [code]
    Selective Sampling and Imitation Learning via Online Regression
    (by alphabetic order) Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu
    NeurIPS, 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]

    We provide the first mathematical and rigorous explaination of why and when maximum-likelihood-estimation based distributional RL can be better than regular RL, in contextual bandits, online RL, and offline RL. The new distributional contextual bandit algorithm outperforms prior CB algorithms empirically.

    Refined Value-Based Offline RL under Realizability and Partial Coverage
    Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun
    NeurIPS, 2023  
    Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
    Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
    NeurIPS, 2023   (Spotlight)
    Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
    Katie Z Luo, Zhenzhen Liu, Xiangyu Chen, Yurong You, Sagie Benaim, Cheng Perng Phoo, Mark Campbell, Wen Sun, Bharath Hariharan, Kilian Q Weinberger
    NeurIPS, 2023  
    Provable Benefits of Representational Transfer in Reinforcement Learning
    (by alphabetic order) Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang
    COLT, 2023  
    Distributional Offline Policy Evaluation with Predictive Error Guarantees
    Runzhe Wu, Masatoshi Uehara, Wen Sun
    ICML, 2023   [code]

    A simple maximum likelihood estimation based approach for distributional RL in off-policy policy evaluation with finite sample complexity guarantees

    Multi-task Representation Learning for Pure Exploration in Linear Bandits
    Yihan Du, Longbo Huang, Wen Sun
    ICML, 2023  
    Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
    Kaiwen Wang, Nathan Kallus, Wen Sun
    ICML, 2023  
    Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
    Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
    ICML, 2023  
    Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
    Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun
    ICLR, 2023   [code] [Talk at RL Theory Seminar]

    Combining online data and offline data can solve RL with both statistical and computation efficiency. Experiments on Montezuma's Revenge (a video game) reveals that hybrid RL works much better than pure online RL and pure offline RL

    PAC Reinforcement Learning for Predictive State Representations
    Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee
    ICLR, 2023  

    A model-based RL algorithm that solves PSRs (a model that generalizes POMDPs) with polynomial sample complexity with general function approximation.

    Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
    Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun
    NeurIPS, 2022  

    A general model-free Actor-critic framework for POMDPs which generalizes special instances including tabular POMDPs, Linear Quadratic Gaussians, POMDPs with Hilbert Space Embeddings, and POMDPs with low-rank structures.

    Learning Bellman Complete Representations for Offline Policy Evaluation
    Jonathan Chang*, Kaiwen Wang*, Nathan Kallus, Wen Sun
    ICML, 2022 (Long talk) [code]

    Standard self-supervised representation learning approaches fail to work in offline RL due to distribution shift and the sequential nature of the problem. Our new self-supervised representation learning approach works in theory and in practice for offline RL.

    Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
    Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun
    ICML, 2022   [code] [Talk at RL Theory Seminar]

    An efficient rich-observation RL algorithm that learns to decode from rich observations to latent states (via adversarial training), while balancing exploration and exploitation

    Learning to Detect Mobile Objects from LiDAR Scans Without Labels
    Yurong You*, Katie Z Luo*, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, and Kilian Q. Weinberger
    CVPR, 2022
    On the Effectiveness of Iterative Learning Control
    Anirudh Vemula, Wen Sun, Maxim Likhachev, Drew Bagnell
    L4DC, 2022   [code]

    Investigated when ILC learns a policy that is better than the certainty equivanece policy

    Online No-regret Model-Based Meta RL for Personalized Navigation
    Yuda Song, Ye Yuan, Wen Sun, Kris Kitani
    L4DC, 2022
    Representation Learning for Online and Offline RL in Low-rank MDPs
    Masatoshi Uehara, Xuezhou Zhang, Wen Sun
    ICLR, 2022 (Spotlight)   [Slides] [Talk at RL Theory Seminar]

    Interleaving representation learning, exploration, and exploitation for efficient RL with nonlinear function approximation

    Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
    Masatoshi Uehara, Wen Sun
    ICLR, 2022   [Slides] [Talk at RL Theory Seminar]

    We show partial coverage and realizability is enough for efficient model-based learning in offline RL; notable examples include low-rank MDPs, KNRs, and factored MDPs.

    Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
    Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani
    ICLR, 2022 (Oral)   [Website]

    Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
    Yurong You, Katie Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, and Kilian Q Weinberger
    ICLR, 2022

    Corruption-Robust Offline Reinforcement Learning
    Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun
    AISTATS, 2022  
    Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
    Jonathan Chang*, Masatoshi Uehara*, Dhruv Sreenivas, Rahul Kidambi, Wen Sun
    NeurIPS, 2021   [code][poster]

    We show how to mitigate covariate shift by leveraging offline data that only provides partial coverage. A by-product of this work is new results for offline RL: partial coverage and robustness (i.e., being able to compete agaist any policy covered by the offline data)

    MobILE: Model-Based Imitation Learning From Observation Alone
    Rahul Kidambi, Jonathan Chang, Wen Sun
    NeurIPS, 2021   [poster] [code]

    IL from Observations is strictly harder than the classic IL; we incoporate exploration into the min-max IL framework (we balance exploration and imitation) to solve IL from observations near optimally in theory and efficiently in practice.

    Corruption Robust Exploration in Episodic Reinforcement Learning
    (by alphabetic order) Thodoris Lykouris, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
    COLT (longer version published at Mathematics of Operations Research), 2021   [Talk at RL Theory seminar]

    A general framework that enables (1) active action elimination in RL, and (2) enables provably robust exploration with adversarial corruptions on both rewards and transitions.

    Robust Policy Gradient against Strong Data Corruption
    Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun
    ICML, 2021   [code]

    An On-policy algorithm that is robust to constant fraction adversarial corruption; The TRPO/NPG based implementation scales to high-dimension control tasks and is robust to strong data corruption.

    PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
    Yuda Song, Wen Sun
    ICML, 2021   [code]

    We propose a simple model-based algorithm that achieves state-of-art in both dense reward continuous control tasks and sparse reward control tasks that require efficient exploration

    Fairness of Exposure in Stochastic Bandits
    Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
    ICML, 2021  
    Bilinear Classes: A Structural Framework for Provable Generalization in RL
    (by alphabetic order) Simon S. Du, Sham M. Kakade, Jason D. Lee, Shachar Lovett, Gaurav Mahajan, Wen Sun, Ruosong Wang
    ICML, 2021   (Long Talk)   [Slides] [Talk at RL theory seminar]

    A new structural complexity captures generalization in RL with function approximation in both model-free and model-based settings. Notably, we show that MDPs with linear Q* and linear V* is PAC learnable.

    PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
    (by alphabetic order) Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun
    NeurIPS, 2020   [slides] [code] [Talk at RL Theory seminar]

    We study the advantages of on-policy policy gradient methods compared to off-policy methods such as Q-learning, and provide a new PG algorithm with exploration

    Information Theoretic Regret Bounds for Online Nonlinear Control
    (by alphabetic order) Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun
    NeurIPS, 2020   [video] [code]

    We study learning-to-control for nonlinear systems captured by RKHS or Gaussian Processes. While being more general, the regret bound is near optimal when specialized to LQRs

    FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
    (by alphabetic order) Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun
    NeurIPS, 2020 (Oral)  

    Representation Learning in RL needs to be done jointly with exploration; we show how to do this correctly and riguously

    Constrained Episodic Reinforcement Learning in Concave-convex and Knapsack Settings
    (by alphabetic order) Kiante Brantley, Miroslav Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun
    NeurIPS, 2020  

    We study multi-objective RL and show how to do cautious exploration under various constraints

    Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates
    Wenhao Luo, Wen Sun, Ashish Kapoor
    NeurIPS, 2020 (Spotlight)  
    Provably Efficient Model-based Policy Adaptation
    Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao
    ICML, 2020   [video & code]

    We study Sim-to-Real under a model-based framework resulting an algorithm that enjoyes strong theoretical guarantees and excellent empirical performance

    Imitation Learning as f-Divergence Minimization
    Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha Srinivasa
    WARF, 2020  

    We unify Imitation Learning by casting it as f-divergence minimization problem

    Disagreement-Regularized Imitation Learning
    Kiante Brantley, Wen Sun, Mikael Henaff
    ICLR, 2020 (Spotlight)   [code]

    Using disagreement among an ensemble of pre-trained behavior cloning policies to reduce covariate shift in IL

    Policy Poisoning in Batch Reinforcement Learning and Control
    Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu
    NeurIPS, 2019   [code] [poster]
    Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
    (by alphabetic order) Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, David P. Woodruff
    NeurIPS, 2019  
    Provably Efficient Imitation Learning from Observations Alone
    Wen Sun, Anirudh Vemola, Byron Boots, J. Andrew Bagnell,
    ICML 2019 (Long Talk) [code] [slides]

    Frame IL with observations alone as a sequence of two-player minmax games.
    Polynomial sample complexity for learning near-optimal policy with general function approximation.

    Contextual Memory Tree
    Wen Sun, Alina Beygelzimer, Hal Daume III, John Langford, Paul Mineiro
    ICML, 2019 (Long Talk) [code] [slides]

    An incremental & learnable memory system maintained in a nearly balanced tree structure to ensure logarithmic time operations

    Model-based RL in CDPs: PAC bounds and Exponential Improvements over Model-free Approaches
    Wen Sun Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
    COLT, 2019 [slides]

    A theoretical comparison between model-based RL and model-free RL.
    A sample efficient model-based RL algorithm.

    Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
    Anirudh Vemula, Wen Sun, Drew Bagnell
    AISTATS 2019 [code] [poster]

    Exploration in action space can be much more efficient than zero-th order method when the number of policy parameters is way larger than the dimension of action space and planning horizon.

    Dual Policy Iteration
    Wen Sun, Geoff Gordon, Byron Boots, Drew Bagnell
    NeurIPS 2018 [code] [slides]

    Leverage Model-based control (i.e., iLQR) and reward-aware Imitation Learning (e.g., AggreVaTeD) to double boost policy improvement

    Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning
    Wen Sun, Drew Bagnell, Byron Boots
    ICLR, 2018 [poster]

    Combination of IL & RL: use expert's value function as reward shaping to shorten planning horizon which in turn speeds up RL

    Sketching for Kronecker Product Regression and P-splines
    (by alphabetic order) Huaian Diao, Zhao Song, Wen Sun, David P. Woodruff
    AISTATS, 2018   (Oral)
    Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
    Wen Sun, Arun Venkatraman, Geoffrey J. Gordon, Byron Boots, J. Andrew Bagnell
    ICML, 2017   (also selected for oral presentation at RLDM 2017) [code] [slides]

    Can be viewed as an actor-critic algorithm with critic being expert's state-action Q function; exponential sample complexity seperation between IL and pure RL

    Safety-Aware Algorithms for Adversarial Contextual Bandit
    Wen Sun, Debadeepta Dey, Ashish Kapoor
    ICML, 2017 [slides]

    Minizing Regret while maintaining average risk below a pre-specified safety-threshold in long term

    Gradient Boosting on Stochastic Data Streams
    Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, Drew Bagnell
    AISTATS, 2017

    Learning to Filter with Predictive State Inference Machines
    Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell.
    ICML 2016 [slides]

    Learning to predict future recurrently.
    Can be viewed as a recurrent structure whose hidden state encodes information for accurately predicting future

    Online Bellman Residual Algorithms with Predictive Error Guarantees
    Wen Sun, Drew Bagnell
    UAI, 2015   Best Student Paper Award [slides]

    Adversarial online policy evaluation.
    A reduction from adversarial policy evaluation to general no-regret & stable online learning.

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