| Error bounds of imitating policies and environments T Xu, Z Li, Y Yu Neural Information Processing System (NeurIPS), 2020 | 114* | 2020 |
| 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 International Conference on Machine Learning (ICML), 2024 | 59* | 2024 |
| 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 | 43 | 2021 |
| Adam-mini: Use fewer learning rates to gain more Y Zhang, C Chen, Z Li, T Ding, C Wu, DP Kingma, Y Ye, ZQ Luo, R Sun arXiv preprint arXiv:2406.16793, 2024 | 39* | 2024 |
| Why transformers need adam: A hessian perspective Y Zhang, C Chen, T Ding, Z Li, R Sun, ZQ Luo Neural Information Processing System (NeurIPS), 2024 | 33 | 2024 |
| When is RL better than DPO in RLHF? A Representation and Optimization Perspective Z Li, T Xu, Y Yu Tiny Paper of International Conference on Learning Representations (ICLR), 2024 | 24* | 2024 |
| Self-Guided Evolution Strategies with Historical Estimated Gradients FY Liu, ZN Li, C Qian International Joint Conferences on Artificial Intelligence (IJCAI), 2020 | 23 | 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 (ICLR), 2022 | 21 | 2022 |
| Rethinking ValueDice - Does It Really Improve Performance? Z Li, T Xu, Y Yu, ZQ Luo Blog of International Conference on Learning Representations (ICLR), 2022 | 18 | 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 | 15* | 2022 |
| Imitation learning from imperfection: Theoretical justifications and algorithms Z Li, T Xu, Z Qin, Y Yu, ZQ Luo Neural Information Processing System (NeurIPS), 2023 | 14* | 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 | 11 | 2024 |
| Provably Efficient Adversarial Imitation Learning with Unknown Transitions T Xu, Z Li, Y Yu, ZQ Luo Conference on Uncertainty in Artificial Intelligence (UAI), 2023 | 11 | 2023 |
| 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 | 6* | 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 | 5 | 2022 |
| Deploying offline reinforcement learning with human feedback Z Li, K Xu, L Liu, L Li, D Ye, P Zhao arXiv preprint arXiv:2303.07046, 2023 | 3 | 2023 |
| RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques Z Tang, Z Li, Z Xiao, T Ding, R Sun, B Wang, D Liu, F Huang, T Liu, B Yu, ... arXiv preprint arXiv:2501.14492, 2025 | 2 | 2025 |
| Enabling Scalable Oversight via Self-Evolving Critic Z Tang, Z Li, Z Xiao, T Ding, R Sun, B Wang, D Liu, F Huang, T Liu, B Yu, ... arXiv preprint arXiv:2501.05727, 2025 | 2 | 2025 |
| 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 | 2 | 2024 |
| Unlocking Black-Box Prompt Tuning Efficiency via Zeroth-Order Optimization H Zhan, C Chen, T Ding, Z Li, R Sun Findings of Conference on Empirical Methods in Natural Language Processing …, 2024 | 1 | 2024 |
Tian XuNanjing UniversityПотвърден имейл адрес: lamda.nju.edu.cn
