Kaixuan Ji

About

Hi! I’m Kaixuan Ji, a third-year Ph.D. student in Computer Science at UCLA, fortunately advised by Professor Quanquan Gu. Before coming to UCLA, I completed my undergraduate studies in Department of Computer Science and Technology at Tsinghua University, where I was fortunate to worked with Professors Jie Tang and Juanzi Li. My current research explores reinforcement learning theory and its role in training large language models.

Selected Publications

Bandit and Reinforcement Learning Theory

• Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
  Kaixuan Ji*, Qingyue Zhao*, Jiafan He, Weitong Zhang, Quanquan Gu, ICLR 2024

• Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits
  Qingyue Zhao*, Kaixuan Ji*, Heyang Zhao*, Tong Zhang, Quanquan Gu, arXiv:2502.06051.

Reinforcement Learning for Large Language Models

• Reinforcement Learning from Human Feedback with Active Queries
  Kaixuan Ji*, Jiafan He*, Quanquan Gu, TMLR 2025, Featured Certification

• Self-play Preference Optimization for Language Model Alignment
  Yue Wu*, Zhiqing Sun*, Huizhuo Yuan*, Kaixuan Ji, Yiming Yang, Quanquan Gu, ICLR 2025,

Efficient Methods for Large Language Model

• P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
  Xiao Liu*, Kaixuan Ji*, Yicheng Fu*, Weng Lam Tam, Zhengxiao Du, Zhilin Yang, Jie Tang, ACL 2022