Our MIT class “6.S184: Introduction to Flow Matching and Diffusion Models” is now available on YouTube! We teach state-of-the-art generative AI algorithms for images, videos, proteins, etc. together with the mathematical tools to understand them. diffusion.csail.mit.edu (1/4)

Eli Sharabi’s full address at the United Nations today. He survived 491 days of Hamas captivity in Gaza, and with remarkable resilience and strength, he shared his story with the world. Upon being released from captivity, he learned the heartbreaking news that his wife, Lianne,

We are presenting 3 orals and 1 spotlight at #ICLR2025 on two primary topics: On generalizing the data-driven flow matching algorithm to jump processes, arbitrary discrete corruption processes, and beyond. And on highly scalable algorithms for reward-driven learning settings.

Check out our latest work on discrete flow models led by Neta Shaul! Poster and oral dates are in Neta's tweet

Come to our oral presentation on Generator Matching at ICLR 2025 tomorrow (Saturday). Learn about a generative model that works for any data type and Markov process! Oral: 3:30pm (Peridot 202-203, session 6E) Poster: 10am-12:30pm #172 (Hall 3 + Hall 2B) arxiv.org/abs/2410.20587

The longer reasoning LLM thinks - the more likely to be correct, right? Apparently not. Presenting our paper: “Don’t Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning”. Link: arxiv.org/abs/2505.17813 1/n

Padding in our non-AR sequence models? Yuck. 🙅 👉 Instead of unmasking, our new work *Edit Flows* perform iterative refinements via position-relative inserts and deletes, operations naturally suited for variable-length sequence generation. Easily better than using mask tokens.

A new paper: We finetune an LLM to rethink and resample previously generated tokens, allowing to reduce sampling errors and improve performance.

**Transition Matching** is a new iterative generative paradigm using Flow Matching or AR models to transition between generation intermediate states, leading to an improved generation quality and speed!

Check out our team's latest work, led by Uriel Singer and Neta Shaul!

Introducing Transition Matching (TM) — a new generative paradigm that unifies Flow Matching and autoregressive models into one framework, boosting both quality and speed! Thank you for the great collaboration Neta Shaul Itai Gat Yaron Lipman