| Bellman-consistent pessimism for offline reinforcement learning T Xie, CA Cheng, N Jiang, P Mineiro, A Agarwal Advances in neural information processing systems (Oral) 34, 6683-6694, 2021 | 371 | 2021 |
| Interpretable preferences via multi-objective reward modeling and mixture-of-experts H Wang, W Xiong, T Xie, H Zhao, T Zhang [EMNLP'24] arXiv preprint arXiv:2406.12845, 2024 | 285 | 2024 |
| Policy finetuning: Bridging sample-efficient offline and online reinforcement learning T Xie, N Jiang, H Wang, C Xiong, Y Bai Advances in neural information processing systems 34, 27395-27407, 2021 | 242 | 2021 |
| Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling T Xie, Y Ma, YX Wang Advances in Neural Information Processing Systems, 9665-9675, 2019 | 223 | 2019 |
| Adversarially trained actor critic for offline reinforcement learning CA Cheng*, T Xie*, N Jiang, A Agarwal International Conference on Machine Learning (Outstanding Paper Runner-up …, 2022 | 188 | 2022 |
| Direct nash optimization: Teaching language models to self-improve with general preferences C Rosset, CA Cheng, A Mitra, M Santacroce, A Awadallah, T Xie arXiv preprint arXiv:2404.03715, 2024 | 163 | 2024 |
| Preference fine-tuning of llms should leverage suboptimal, on-policy data F Tajwar, A Singh, A Sharma, R Rafailov, J Schneider, T Xie, S Ermon, ... [ICML'24] arXiv preprint arXiv:2404.14367, 2024 | 157 | 2024 |
| Batch value-function approximation with only realizability T Xie, N Jiang International Conference on Machine Learning, 11404-11413, 2021 | 156 | 2021 |
| Provably efficient q-learning with low switching cost Y Bai, T Xie, N Jiang, YX Wang Advances in Neural Information Processing Systems, 8004-8013, 2019 | 132 | 2019 |
| Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison T Xie, N Jiang Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence …, 2020 | 125 | 2020 |
| The role of coverage in online reinforcement learning T Xie, DJ Foster, Y Bai, N Jiang, SM Kakade [ICLR'23 Oral] arXiv preprint arXiv:2210.04157, 2022 | 110 | 2022 |
| Exploratory preference optimization: Harnessing implicit q*-approximation for sample-efficient rlhf T Xie, DJ Foster, A Krishnamurthy, C Rosset, A Awadallah, A Rakhlin [ICLR'25] arXiv preprint arXiv:2405.21046, 2024 | 94 | 2024 |
| Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency M Uehara, M Imaizumi, N Jiang, N Kallus, W Sun, T Xie arXiv preprint arXiv:2102.02981, 2021 | 77 | 2021 |
| Adversarial model for offline reinforcement learning M Bhardwaj*, T Xie*, B Boots, N Jiang, CA Cheng Advances in Neural Information Processing Systems 36, 1245-1269, 2023 | 55 | 2023 |
| Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization A Huang, W Zhan, T Xie, JD Lee, W Sun, A Krishnamurthy, DJ Foster [ICLR'25 Spotlight] arXiv preprint arXiv:2407.13399, 2024 | 54* | 2024 |
| A Block Coordinate Ascent Algorithm for Mean-Variance Optimization T Xie, B Liu, Y Xu, M Ghavamzadeh, Y Chow, D Lyu, D Yoon Advances in Neural Information Processing Systems, 1073-1083, 2018 | 50 | 2018 |
| A variant of the wang-foster-kakade lower bound for the discounted setting P Amortila, N Jiang, T Xie arXiv preprint arXiv:2011.01075, 2020 | 31 | 2020 |
| Countercurate: Enhancing physical and semantic visio-linguistic compositional reasoning via counterfactual examples J Zhang, M Cai, T Xie, YJ Lee [ACL 24] arXiv preprint arXiv:2402.13254, 2024 | 26 | 2024 |
| Harnessing density ratios for online reinforcement learning P Amortila, DJ Foster, N Jiang, A Sekhari, T Xie [ICLR'24 Spotlight] arXiv preprint arXiv:2401.09681, 2024 | 23 | 2024 |
| Reinforce LLM Reasoning through Multi-Agent Reflection Y Yuan, T Xie [ICML'25] arXiv preprint arXiv:2506.08379, 2025 | 18 | 2025 |
Nan JiangAssociate Professor of Computer Science, UIUCVerified email at illinois.edu
