| A survey on model-based reinforcement learning FM Luo, T Xu, H Lai, XH Chen, W Zhang, Y Yu Science China Information Sciences 67 (2), 121101, 2024 | 245 | 2024 |
| Remax: A simple, effective, and efficient reinforcement learning method for aligning large language models Z Li, T Xu, Y Zhang, Z Lin, Y Yu, R Sun, ZQ Luo arXiv preprint arXiv:2310.10505, 2023 | 188 | 2023 |
| Error bounds of imitating policies and environments T Xu, Z Li, Y Yu Advances in Neural Information Processing Systems 33, 15737-15749, 2020 | 138 | 2020 |
| Error bounds of imitating policies and environments for reinforcement learning T Xu, Z Li, Y Yu IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (10), 6968 …, 2021 | 53 | 2021 |
| Preserving diversity in supervised fine-tuning of large language models Z Li, C Chen, T Xu, Z Qin, J Xiao, ZQ Luo, R Sun arXiv preprint arXiv:2408.16673, 2024 | 38 | 2024 |
| Policy optimization in rlhf: The impact of out-of-preference data Z Li, T Xu, Y Yu arXiv preprint arXiv:2312.10584, 2023 | 38 | 2023 |
| Provably efficient adversarial imitation learning with unknown transitions T Xu, Z Li, Y Yu, ZQ Luo Uncertainty in Artificial Intelligence, 2367-2378, 2023 | 28* | 2023 |
| Imitation learning from imperfection: Theoretical justifications and algorithms Z Li, T Xu, Z Qin, Y Yu, ZQ Luo Advances in Neural Information Processing Systems 36, 18404-18443, 2023 | 25 | 2023 |
| Entropic distribution matching for supervised fine-tuning of LLMs: Less overfitting and better diversity Z Li, C Chen, T Xu, Z Qin, J Xiao, R Sun, ZQ Luo NeurIPS 2024 Workshop on Fine-Tuning in Modern Machine Learning: Principles …, 2024 | 22 | 2024 |
| Reward-consistent dynamics models are strongly generalizable for offline reinforcement learning FM Luo, T Xu, X Cao, Y Yu arXiv preprint arXiv:2310.05422, 2023 | 21 | 2023 |
| Rethinking ValueDice: Does it really improve performance? Z Li, T Xu, Y Yu, ZQ Luo arXiv preprint arXiv:2202.02468, 2022 | 19 | 2022 |
| Generalist Reward Models: Found Inside Large Language Models YC Li, T Xu, Y Yu, X Zhang, XH Chen, Z Ling, N Chao, L Yuan, ZH Zhou arXiv preprint arXiv:2506.23235, 2025 | 16 | 2025 |
| Model gradient: unified model and policy learning in model-based reinforcement learning C Jia, F Zhang, T Xu, JC Pang, Z Zhang, Y Yu Frontiers of Computer Science 18 (4), 184339, 2024 | 14 | 2024 |
| Understanding adversarial imitation learning in small sample regime: A stage-coupled analysis T Xu, Z Li, Y Yu, ZQ Luo arXiv preprint arXiv:2208.01899, 2022 | 12 | 2022 |
| Limited preference aided imitation learning from imperfect demonstrations X Cao, FM Luo, J Ye, T Xu, Z Zhang, Y Yu Forty-first international conference on machine learning, 2024 | 6 | 2024 |
| Policy rehearsing: Training generalizable policies for reinforcement learning C Jia, C Gao, H Yin, F Zhang, XH Chen, T Xu, L Yuan, Z Zhang, ZH Zhou, ... The Twelfth International Conference on Learning Representations, 2024 | 5 | 2024 |
| When is rl better than dpo in rlhf? a representation and optimization perspective Z Li, T Xu, Y Yu The Second Tiny Papers Track at ICLR 2024, 2024 | 4 | 2024 |
| Offline Imitation Learning without Auxiliary High-quality Behavior Data JJ Shao, HS Shi, T Xu, LZ Guo, Y Yu, YF Li | 3 | 2024 |
| Provably and practically efficient adversarial imitation learning with general function approximation T Xu, Z Zhang, R Chen, Y Sun, Y Yu Advances in Neural Information Processing Systems 37, 66108-66146, 2024 | 2 | 2024 |
| Reinforcement learning with sparse-executing actions via sparsity regularization JC Pang, T Xu, S Jiang, YR Liu, Y Yu arXiv preprint arXiv:2105.08666, 2021 | 2 | 2021 |
Yang YuProfessor, Nanjing UniversityVerified email at nju.edu.cn
