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 interned at Apple MLR. His research interests include diffusion language models, fast generative models, and generalization.
Upcoming Session
January 19, 2026
TiDAR: Think in Diffusion, Talk in Autoregression
Jingyu Liu will discuss TiDAR, a hybrid decoding approach that combines diffusion-style parallel drafting with autoregressive verification for high quality and high throughput.
Time: Jan 19 (Monday) · 1 PM ET / 10 AM PT / 7 PM CET / 11:30 PM IST
Meeting link: click here
Paper: TiDAR
Abstract: TiDAR is a hybrid language model that drafts tokens in parallel using diffusion, then verifies them autoregressively, to match AR quality while generating much faster.
Latest Sessions
View All Sessions
Latest Relevant Videos
View All Videos
Stay Updated
Join our community and never miss a session