Xuezhou Zhang

Papers

Total Citations

H-Index

About

Xuezhou Zhang is a rising researcher whose work lies at the intersection of reinforcement learning, algorithmic teaching, and machine learning theory. His most notable contribution is in the study of teaching-by-reinforcement, a paradigm where a teacher guides a student agent through carefully designed rewards rather than explicit demonstrations. In his highly cited 2021 paper, "The Sample Complexity of Teaching by Reinforcement on Q-Learning," Zhang introduced the concept of "teaching dimension" (TDim) for reinforcement learning—a formal measure of the sample complexity required to teach an agent via reward shaping. This work bridges the gap between theoretical machine learning and practical AI training, offering insights into how efficiently an agent can be taught to perform tasks. While his citation count is still growing, Zhang's research has already influenced discussions on interactive learning and human-AI collaboration. His focus on the theoretical underpinnings of teaching algorithms positions him as a key voice in understanding how to design more sample-efficient, interpretable AI systems—a critical challenge as reinforcement learning moves into real-world applications like robotics and autonomous systems.