I am a CS PhD candidate in the Machine Learning Group at University of Toronto, advised by Professor Jimmy Ba.
In 2024-2025, I interned at Google DeepMind working on LLMs. In 2021-2024, I worked on autonomous driving at Waabi.
I studied Engineering Science at University of Toronto, and spent time at Vector Institute, Mila, and Uber Advanced Technologies Group.
Contact: Email / Google Scholar / Github / Twitter
I am broadly interested in building general-purpose agents in the digital and physical worlds, with a focus on recursive self improvement.
I currently work on improving various aspects of language model reasoning and agentic capabilities.
Previously, I worked on unsupervised learning of perception, prediction, and planning in robotics.
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
International Conference on Learning Representations (ICLR), 2025
Reward models are better with next token prediction and chain of thoughts, too.
Lunjun Zhang, Yuwen Xiong, Ze Yang, Sergio Casas, Rui Hu, Raquel Urtasun
International Conference on Learning Representations (ICLR), 2024
[Paper] [Proceedings] [Poster] [Website]
A foundation model for self-driving that explicitly reasons in both 3D space and time.
Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong, Sergio Casas, Bin Yang, Mengye Ren, Raquel Urtasun
Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[Paper] [Proceedings] [Poster] [Website]
Self-supervised, scalable object discovery in the wild.
Lunjun Zhang, Ge Yang, Bradly Stadie
International Conference on Machine Learning (ICML), 2021 (Long Talk)
[Paper] [Proceedings] [Poster] [Website] [Code]
Unsupervised long-horizon planning via graph-structured world models.