I am an AI researcher at Meta Superintelligence (TBD) Lab, working on large-scale reinforcement learning for frontier language models. Previously, I worked on scaling reinforcement learning at xAI.
Before that, I was a postdoctoral research fellow at AI^2, Princeton AI Lab, working on the principled understanding of large language models and their application to engineering, including design and control. I worked closely with Prof. Mengdi Wang at Princeton.
I earned my Ph.D. in Computer Science from University of California, Los Angeles (UCLA), where I was advised by Prof. Quanquan Gu. Before that, I earned my Bachelor of Science from EECS at Peking University summa cum laude, where I was very fortunate to be advised by Prof. Liwei Wang.
🔥 News
- 2025.01: 🎉🎉 3 papers accepted to ICLR 2025.
- 2024.06-09: This summer, I had an internship at Meta Gen AI, where I worked on LLM alignment and reward modeling.
- 2024.05: 🎉🎉 2 papers accepted to ICML 2024.
- 2024.01: 🎉🎉 2 papers accepted to ICLR 2024.
- 2023.08: I am honored to have been awarded the UCLA Dissertation Year Fellowship!
- 2023.07: 🎉🎉 1 paper accepted to ICML 2023, Hawaii.
- 2022.09: 🎉🎉 2 papers accepted to NeurIPS 2022, New Orleans.
📝 Publications & Preprints
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Self-Play Preference Optimization for Language Model Alignment, ICLR 2025
Yue Wu*, Zhiqing Sun*, Huizhuo Yuan*, Kaixuan Ji, Yiming Yang, Quanquan Gu -
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement, ICLR 2025
Hui Yuan*, Yifan Zeng*, Yue Wu*, Huazheng Wang, Mengdi Wang, Leqi Liu* -
General Preference Modeling with Preference Representations for Aligning Language Models, preprint
Yifan Zhang*, Ge Zhang*, Yue Wu*, Kangping Xu, Quanquan Gu -
Borda Regret Minimization for Generalized Linear Dueling Bandits, ICML 2024
Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu -
Protein Conformation Generation via Force-Guided SE (3) Diffusion Models, ICML 2024
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu -
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits, ICLR 2024
Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu -
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text, ICLR 2024
Xianjun Yang, Wei Cheng, Yue Wu, Linda Petzold, William Yang Wang, Haifeng Chen -
Personalized Federated Learning under Mixture of Distributions, ICML 2023
Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng -
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension, UAI 2023
Yue Wu*, Jiafan He*, Quanquan Gu -
Active Ranking without Strong Stochastic Transitivity, NeurIPS 2022
Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud -
Towards Understanding the Mixture-of-Experts Layer in Deep Learning, NeurIPS 2022
Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li -
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons, AISTATS 2022
Yue Wu*, Tao Jin*, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu -
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation, AISTATS 2022
Yue Wu, Dongruo Zhou, Quanquan Gu, -
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods, NeurIPS 2020
Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu, -
Towards Understanding the Spectral Bias of Deep Learning, IJCAI 2021
Yuan Cao*, Zhiying Fang*, Yue Wu*, Dingxuan Zhou, Quanquan Gu -
To What Extent Do Different Neural Networks Learn the Same Representation: A Neuron Activation Subspace Match Approach, NeurIPS 2019 Spotlight
Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Liwei Wang
📖 Teaching
- 2021 Winter Teaching Assistant, UCLA CS161: Introduction to Artificial Intelligence
- 2022 Winter Teaching Assistant, UCLA CS161: Introduction to Artificial Intelligence
💬 Academic Service
- Reviewers of NeurIPS, ICML, ICLR, AISTATS, AAAI, IJCAI, and other conferences/journals in machine learning/data mining.
- Senior PC members of AAAI’23