Chenlu Ye

According to our database, Chenlu Ye authored at least 19 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Adaptive Layerwise Perturbation: Unifying Off-Policy Corrections for LLM RL.

Chenlu Ye, Xuanchang Zhang, Yifan Hao, Zhou Yu, Ziji Zhang, Abhinav Gullapalli, Hao Chen, Jing Huang, Tong Zhang

CoRR, March, 2026

2025

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training.

Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang

CoRR, October, 2025

Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training.

Chenlu Ye, Zhou Yu, Ziji Zhang, Hao Chen, Narayanan Sadagopan, Jing Huang, Tong Zhang, Anurag Beniwal

CoRR, September, 2025

Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models.

Yifan Hao, Chenlu Ye, Chi Han, Tong Zhang

CoRR, June, 2025

Daunce: Data Attribution through Uncertainty Estimation.

Xingyuan Pan, Chenlu Ye, Joseph Melkonian, Jiaqi W. Ma, Tong Zhang

CoRR, May, 2025

Self-rewarding correction for mathematical reasoning.

Wei Xiong, Hanning Zhang, Chenlu Ye, Lichang Chen, Nan Jiang, Tong Zhang

CoRR, February, 2025

Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning.

Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang

J. Mach. Learn. Res., 2025

Logarithmic Regret for Online KL-Regularized Reinforcement Learning.

Heyang Zhao, Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Catoni Contextual Bandits are Robust to Heavy-tailed Rewards.

Chenlu Ye, Yujia Jin, Alekh Agarwal, Tong Zhang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods.

Yifan Hao, Xingyuan Pan, Hanning Zhang, Chenlu Ye, Rui Pan, Tong Zhang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Sharp Analysis for KL-Regularized Contextual Bandits and RLHF.

Heyang Zhao, Chenlu Ye, Quanquan Gu, Tong Zhang

CoRR, 2024

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference.

Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, Tong Zhang

CoRR, 2024

Online Iterative Reinforcement Learning from Human Feedback with General Preference Model.

Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong, Nan Jiang, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption.

Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint.

Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF.

Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang

CoRR, 2023

Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks.

Jianqing Fan, Zhaoran Wang, Zhuoran Yang, Chenlu Ye

CoRR, 2023

Corruption-Robust Offline Reinforcement Learning with General Function Approximation.

Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes.

Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang

Proceedings of the International Conference on Machine Learning, 2023