Discrete Diffusion Reading Group

Exploring diffusion-based generative models on discrete spaces.

About the Reading Group

Diffusion LLMs are faster, more controllable successors to traditional LLMs and are rapidly gaining adoption. This reading group aims to build a community for exchanging and debating emerging ideas in this space. While our primary focus is discrete diffusion models for language, we also invite work that extends these methods to other modalities and applications—such as molecular design, drug discovery, and beyond. Each session features an author-led presentation followed by Q&A, with recordings shared on our YouTube channel.

Paper Discussions

Authors present their work followed by discussions and Q&A sessions

Recorded Sessions

All sessions are recorded and available on YouTube

Community

Stay informed through our email list and Twitter/X

Meet the Organizers

Subham Sahoo

Holds a Ph.D. from Cornell Tech, where he specialized in Diffusion Language Models. He has made foundational contributions to the field, with his work deployed at scale by Google, NVIDIA, and ByteDance across language generation and drug discovery.

Justin Deschenaux

PhD student in Machine Learning at EPFL, advised by Prof. Caglar Gulcehre. Previously completed Bachelor's and Master's degrees in Telecommunication System and interned at Apple MLR. His research interests include diffusion language models, fast generative models, and generalization.

Zhihan Yang

PhD student at Cornell CS. Previously completed his Bachelor's degrees in Mathematics and Statistics at Carleton College. He is a winner of the CRA Outstanding Undergraduate Researcher Award and his research focuses on principled, controllable, and efficient generative models.