| Error bounds of imitating policies and environments T Xu, Z Li, Y Yu Advances in Neural Information Processing Systems 33, 15737-15749, 2020 | 101 | 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 | 36 | 2021 |
| 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 Forty-first International Conference on Machine Learning, 2024 | 24* | 2024 |
| Self-Guided Evolution Strategies with Historical Estimated Gradients FY Liu, ZN Li, C Qian IJCAI, 1474-1480, 2020 | 21 | 2020 |
| HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning Z Li, Y Li, Y Zhang, T Zhang, ZQ Luo International Conference on Learning Representations, 2022 | 16 | 2022 |
| Rethinking ValueDice - Does It Really Improve Performance? Z Li, T Xu, Y Yu, ZQ Luo ICLR Blog, 2022 | 14 | 2022 |
| 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 | 11* | 2022 |
| When is RL better than DPO in RLHF? A Representation and Optimization Perspective Z Li, T Xu, Y Yu ICLR Tiny Paper, 2024 | 9* | 2024 |
| Why transformers need adam: A hessian perspective Y Zhang, C Chen, T Ding, Z Li, R Sun, ZQ Luo arXiv preprint arXiv:2402.16788, 2024 | 9 | 2024 |
| 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, 2024 | 7* | 2024 |
| Provably Efficient Adversarial Imitation Learning with Unknown Transitions T Xu, Z Li, Y Yu, ZQ Luo UAI, 2367-2378, 2023 | 7 | 2023 |
| On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization J Xiao, Z Li, X Xie, E Getzen, C Fang, Q Long, WJ Su arXiv preprint arXiv:2405.16455, 2024 | 6 | 2024 |
| Adam-mini: Use fewer learning rates to gain more Y Zhang, C Chen, Z Li, T Ding, C Wu, Y Ye, ZQ Luo, R Sun arXiv preprint arXiv:2406.16793, 2024 | 3 | 2024 |
| A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle Z Li, T Xu, Y Yu arXiv preprint arXiv:2203.11489, 2022 | 1 | 2022 |
| Efficient Exploration by Novelty-Pursuit Z Li, XH Chen Distributed Artificial Intelligence: Second International Conference, DAI …, 2020 | 1 | 2020 |
| Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity Z Li, C Chen, T Xu, Z Qin, J Xiao, R Sun, ZQ Luo arXiv preprint arXiv:2408.16673, 2024 | | 2024 |
| Sensing Jamming Strategy from Limited Observations: An Imitation Learning Perspective Y Fan, B Jiu, W Pu, Z Li, K Li, H Liu IEEE Transactions on Signal Processing, 2024 | | 2024 |
| BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation C Jia, P Wang, Z Li, YC Li, Z Zhang, N Tang, Y Yu arXiv preprint arXiv:2405.17039, 2024 | | 2024 |
Tian XuNanjing UniversityVerified email at lamda.nju.edu.cn
