Email: zuxin1997 [at] gmail [dot] com
I am a researcher at OpenAI, working on reasoning model post-training. Previously I worked for Salesforce AI Research on LLM Agent. I received my PhD and MS degrees from Carnegie Mellon University, focusing on RL and robotics. I finished my bachelor's degree with honor (President's Award) from Beihang University.
My long-term ambition is to develop AI agents capable of achieving and surpassing human-level
performance in various daily tasks, ultimately freeing humans from repetitive work and enhancing
productivity. Beyond the commonly recognized capabilities like reasoning and planning, I believe
that continual self-evolution (in terms of training) and
self-reflection (during deployment) are also essential traits of truly intelligent
agents. This aligns with the core principles of reinforcement learning (RL), which
I view as a guiding philosophical framework for thinking and studying AI agents.
My research aims to apply the foundational principles—not just the methods—of RL to large language
model (LLM)-based agents, contributing to the promising future of AI Agent systems that that
evolve, learn, and interact in ways that
complement and enhance human capabilities. Currently, I am developing scalable approaches, such as
utilizing synthetic data, to improve models' agentic abilities, and leveraging environmental
feedback to better enable self-learning and reflection, as exemplified like the Software Engineering
Agent.
Topics:
LLM Agent /
Foundation Model /
Reasoning /
RL Algorithms
Past topics:
Embodied AI & Robotics /
Computer Vision & Autonomous Vehicles /
Fun Undergrads Projects