Convergence of SGD in Learning ReLU Models with Separable Data. - Tengyu Xu; Yi Zhou 017; Kaiyi Ji; Yingbin Liang (CoRR, 2018)

Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples. - Tengyu Xu; Shaofeng Zou; Yingbin Liang (NeurIPS, 2019)

Finite-Sample Analysis for SARSA with Linear Function Approximation. - Shaofeng Zou; Tengyu Xu; Yingbin Liang (NeurIPS, 2019)

Finite-Sample Analysis for SARSA and Q-Learning with Linear Function Approximation. - Shaofeng Zou; Tengyu Xu; Yingbin Liang (CoRR, 2019)

Reanalysis of Variance Reduced Temporal Difference Learning. - Tengyu Xu; Zhe Wang 021; Yi Zhou 017; Yingbin Liang (ICLR, 2020)

Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms. - Tengyu Xu; Zhe Wang 021; Yingbin Liang (NeurIPS, 2020)

Improving Sample Complexity Bounds for Actor-Critic Algorithms. - Tengyu Xu; Zhe Wang 021; Yingbin Liang (CoRR, 2020)

Non-asymptotic Convergence Analysis of Two Time-scale (Natural) Actor-Critic Algorithms. - Tengyu Xu; Zhe Wang 021; Yingbin Liang (CoRR, 2020)

Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization. - Tengyu Xu; Zhe Wang 021; Yingbin Liang; H. Vincent Poor (CoRR, 2020)

A Primal Approach to Constrained Policy Optimization: Global Optimality and Finite-Time Analysis. - Tengyu Xu; Yingbin Liang; Guanghui Lan (CoRR, 2020)

Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling. - Huaqing Xiong; Tengyu Xu; Yingbin Liang; Wei Zhang 013 (AAAI, 2021)

When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence. - Ziwei Guan; Tengyu Xu; Yingbin Liang (AISTATS, 2021)

Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms. - Tengyu Xu; Yingbin Liang (AISTATS, 2021)

Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry. - Ziyi Chen 002; Yi Zhou 017; Tengyu Xu; Yingbin Liang (ICLR, 2021)

CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee. - Tengyu Xu; Yingbin Liang; Guanghui Lan (ICML, 2021)

Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. - Tengyu Xu; Zhuoran Yang; Zhaoran Wang 001; Yingbin Liang (ICML, 2021)

A Unified Off-Policy Evaluation Approach for General Value Function. - Tengyu Xu; Zhuoran Yang; Zhaoran Wang 001; Yingbin Liang (CoRR, 2021)

PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method. - Ziwei Guan; Tengyu Xu; Yingbin Liang (ICLR, 2022)

Model-Based Offline Meta-Reinforcement Learning with Regularization. - Sen Lin 001; Jialin Wan; Tengyu Xu; Yingbin Liang; Junshan Zhang (ICLR, 2022)

A Unifying Framework of Off-Policy General Value Function Evaluation. - Tengyu Xu; Zhuoran Yang; Zhaoran Wang 001; Yingbin Liang (NeurIPS, 2022)

Deterministic policy gradient: Convergence analysis. - Huaqing Xiong; Tengyu Xu; Lin Zhao 009; Yingbin Liang; Wei Zhang 013 (UAI, 2022)

Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward. - Tengyu Xu; Yingbin Liang (CoRR, 2022)

Constraint-based multi-agent reinforcement learning for collaborative tasks. - Xiumin Shang; Tengyu Xu; Ioannis Karamouzas; Marcelo Kallmann (Comput. Animat. Virtual Worlds, 2023)

The Perfect Blend: Redefining RLHF with Mixture of Judges. - Tengyu Xu; Eryk Helenowski; Karthik Abinav Sankararaman; Di Jin; Kaiyan Peng; Eric Han; Shaoliang Nie; Chen Zhu; Hejia Zhang; Wenxuan Zhou; Zhouhao Zeng; Yun He; et al. (CoRR, 2024)

Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following. - Yun He; Di Jin; Chaoqi Wang; Chloe Bi; Karishma Mandyam; Hejia Zhang; Chen Zhu; Ning Li; Tengyu Xu; Hongjiang Lv; Shruti Bhosale; Chenguang Zhu 001; et al. (CoRR, 2024)

Faster algorithm and sharper analysis for constrained Markov decision process. - Tianjiao Li; Ziwei Guan; Shaofeng Zou; Tengyu Xu; Yingbin Liang; Guanghui Lan (Oper. Res. Lett., 2024)

Provably Efficient Offline Reinforcement Learning With Trajectory-Wise Reward. - Tengyu Xu; Yue Wang 068; Shaofeng Zou; Yingbin Liang (IEEE Trans. Inf. Theory, 2024)

HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback. - Xufeng Cai; Ziwei Guan; Lei Yuan; Ali Selman Aydin; Tengyu Xu; Boying Liu; Wenbo Ren; Renkai Xiang; Songyi He; Haichuan Yang; Serena Li; Mingze Gao; et al. (KDD (1), 2025)

Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback. - Yen-Ting Lin; Di Jin; Tengyu Xu; Tianhao Wu 002; Sainbayar Sukhbaatar; Chen Zhu; Yun He; Yun-Nung Chen; Jason Weston; Yuandong Tian; Arash Rahnama; Sinong Wang; et al. (CoRR, 2025)

Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization. - Zishun Yu; Tengyu Xu; Di Jin; Karthik Abinav Sankararaman; Yun He; Wenxuan Zhou; Zhouhao Zeng; Eryk Helenowski; Chen Zhu; Sinong Wang; Hao Ma; Han Fang (CoRR, 2025)

Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation. - Chengwei Qin; Wenxuan Zhou; Karthik Abinav Sankararaman; Nanshu Wang; Tengyu Xu; Alexander Radovic; Eryk Helenowski; Arya Talebzadeh; Aditya Tayade; Sinong Wang; Shafiq Joty; Han Fang; et al. (CoRR, 2025)

Reinforcement Learning from User Feedback. - Eric Han; Jun Chen; Karthik Abinav Sankararaman; Xiaoliang Peng; Tengyu Xu; Eryk Helenowski; Kaiyan Peng; Mrinal Kumar 005; Sinong Wang; Han Fang; Arya Talebzadeh (CoRR, 2025)

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training. - Bo Wu; Sid Wang; Yunhao Tang; Jia Ding; Eryk Helenowski; Liang Tan; Tengyu Xu; Tushar Gowda; Zhengxing Chen; Chen Zhu; Xiaocheng Tang; Yundi Qian; et al. (CoRR, 2025)

Boosting LLM Reasoning via Spontaneous Self-Correction. - Xutong Zhao; Tengyu Xu; Xuewei Wang; Zhengxing Chen; Di Jin; Liang Tan; Yen-Ting; Zishun Yu; Zhuokai Zhao; Yun He; Sinong Wang; Han Fang; et al. (CoRR, 2025)