I am a CS PhD student in the Machine Learning Group at University of Toronto advised by Professor Jimmy Ba.
I recently interned at Google DeepMind in 2024, working on LLM.
From 2021 to 2024, I worked as a researcher at the self-driving startup Waabi. From 2017 to 2021, I studied Engineering Science at University of Toronto, and interned at Vector Institute, Mila, and Uber Advanced Technologies Group.
Contact: Email / Google Scholar / Github / Twitter
Currently, I work on LLM reasoning, alignment, and self-improvement.
Previously, I worked on unsupervised learning of perception, prediction, and planning in robotics.
Eventually, I aim to build general-purpose agents that are capable, reliable, and truth-seeking.
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
arxiv preprint, 2024
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.