Longtao Zheng 郑龙韬

Email: longtao001 [at] e.ntu.edu.sg
GitHub: https://github.com/ltzheng

Longtao Zheng is a PhD student at the College of Computing and Data Science, Nanyang Technological University (NTU) Singapore, advised by Prof. Bo An. Previously, he obtained his Bachelor's degree in computer science from University of Science and Technology of China (USTC) in 2022. His research focuses on foundation models for decision making, controllable video generation, and reinforcement learning.

Research Highlights

Motivated by the goal of creating general-purpose agents that can interact with the digital world, my work primarily focused on using the Internet-scale knowledge within foundation models to generalize beyond available data and to unseen situations. I am also interested in training agents and world models from videos.

Publications ( show selected / show all by date / show all by topic )

Topics: Foundation Models for Decision Making / Controllable Video Generation
Past topics: Multi-Agent Reinforcement Learning / Others (*/†: indicates equal contribution.)

MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation

Longtao Zheng, Yifan Zhang, Hanzhong Guo, Jiachun Pan, Zhenxiong Tan, Jiahao Lu, Chuanxin Tang, Bo An, Shuicheng Yan

arXiv Preprint Paper / Project Page / Code / Model

A SOTA and open-weight model for audio-driven talking video generation

AgentStudio: A Toolkit for Building General Virtual Agents

Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan

ICLR 2025 Paper / Project Page / Code / Data

A trinity of environments, tools, and benchmarks for general virtual agents

Cradle: Empowering Foundation Agents Towards General Computer Control

Cradle Team

NeurIPS 2024 Workshop on Open-World Agents Paper / Project Page / Code

An agent that can play AAA video games

Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control

Longtao Zheng, Rundong Wang, Xinrun Wang, Bo An

ICLR 2024 Paper / Project Page / Code

A computer agent with state abstraction, trajectory prompting, and memory

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

Rundong Wang, Longtao Zheng, Wei Qiu, Bowei He, Bo An, Zinovi Rabinovich, Yujing Hu, Yingfeng Chen, Tangjie Lv, Changjie Fan

arXiv Preprint Paper / Code

Autocurricula for MARL in complex sparse-reward environments like Google Football