👤 About Me

I am Weixin Wang, a fourth year Ph.D. candidate in Prof. Pan Xu’s lab at Duke University.

My research centers on Machine Learning, with a focus on developing computationally efficient and data-efficient algorithms with strong empirical performance and rigorous theoretical guarantees. My core interests span Reinforcement Learning (RL) and Large Language Models (LLMs), especially randomized exploration in RL and inference-time scaling of LLMs. I am also interested in diffusion models, robust RL, and high-dimensional statistics.

I am fortunate to collaborate with Yu Yang, Zhishuai Liu (lab members), Wei Deng, Andrew Bennett, Ruoxi Cheng and many other excellent researchers.

🔥 News

• 2026.6:   I will be joining Morgan Stanley in New York this summer as a Machine Learning Research Associate mentored by Andrew Bennett!
• 2026.1:  🎉🎉 Two papers are accepted to ICLR 2026!
• 2025.12:   I attended NeurIPS 2025 at San Diego!
• 2025.8:   I attended Princeton 2025 Machine Learning Theory Summer School!
• 2025.5:  🎉🎉 Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction is accepted as Poster to ICML 2025!
• 2024.12:   I attended NeurIPS 2024 at Vancouver!
• 2024.9:  🎉🎉 Randomized Exploration in Cooperative Multi-agent Reinforcement Learning is accepted as Poster to NeurIPS 2024!

📝 Publications

• Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling [Paper]

  Weixin Wang, Wei Deng, Anderson Schneider, Yuriy Nevmyvaka, Pan Xu, Andrew Bennett

  Under review.

• Inference-Time Alignment of Diffusion Models via Trust-Region Iterative Twisted Sequential Monte Carlo [Paper]

  Weixin Wang^*, Yu Yang^*, Wei Deng, Pan Xu

  NeurIPS 2026 Workshop: Transitioning from Pre-Training to Post-Training.

• Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective [Paper]

  Weixin Wang^*, Haoyang Zheng^*, Guang Lin, Wei Deng, Pan Xu

  NeurIPS 2025 Workshop: Dynamics at the Frontiers of Optimization, Sampling, and Games (DynaFront).

• Diffusion Posterior Sampling for Fast Adaptation in Multi-task Nonlinear Contextual Bandits

  Weixin Wang^*, Yu Yang^*, Pan Xu

  NeurIPS 2026 Workshop on Towards Test-Time Continual Learning Agents (TTCL).

• Inverse Reinforcement Learning with Dynamic Reward Scaling for LLM Alignment [Paper]

  Ruoxi Cheng^*, Haoxuan Ma^*, Weixin Wang^*, Ranjie Duan, Jiexi Liu, Xiaoshuang Jia, Simeng Qin, Xiaochun Cao, Yang Liu, Xiaojun Jia

  In Proc. of the 14th International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026.

• Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits [Paper]

  Jiazheng Sun^*, Weixin Wang^*, Pan Xu

  Under review.

• Breaking the Total Variance Barrier: Sharp Sample Complexity for Linear Heteroscedastic Bandits with Fixed Action Set

  Heyang Zhao^*, Tianyuan Jin^*, Weixin Wang, Vincent Y. F. Tan, Pan Xu, Quanquan Gu

  In Proc. of the 14th International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026.

• Near-Optimal Reinforcement Learning for Linear Distributionally Robust Markov Decision Processes [Paper]

  Zhishuai Liu^*, Weixin Wang^*, Pan Xu

  Reinforcement Learning Journal (RLJ), vol. 7, 2026. Presented at the Third Reinforcement Learning Conference (RLC 2026), Montréal, Canada.

• Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction [Paper]

  Yiting He^*, Zhishuai Liu^*, Weixin Wang, Pan Xu

  In Proc. of the 42nd International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.

• Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning [Paper] [Code]

  Hao-Lun Hsu^*, Weixin Wang^*, Miroslav Pajic, Pan Xu

  In Proc. of the 38th Conference on Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.

💻 Internships

• 2026.06 - 2026.08, Machine Learning Research Associate, Morgan Stanley, New York.

📖 Educations

• 2023.08 - now, Ph.D., Department of Electrical and Computer Engineering, Duke University.
• 2019.09 - 2023.06, B.S., School of the Gifted Young, University of Science and Technology of China.