Foundation models are trained in stages: pretraining, midtraining, and post-training. But is this separation fundamental?
We introduce unified training: a single algorithm for the full pipeline. This gives
⚙️ Simpler infrastructure
🔬 A framework for better science
(1/n)View quoted post on X
Introducing SCALLOP 💜 Our new paper accepted at NeurIPS 2026 🐨
🚀 A scalable, efficient way to train few-step likelihood-available flow maps, enabling effective Boltzmann generation!
📽️ project page: tonyauyeung.github.io/scallop/
🔗 paper: arxiv.org/pdf/2606.29110View quoted post on X
In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. 1/NView quoted post on X
We release Diamond Maps💎 unlocking accurate and efficient guidance for diffusion models. Our experiments show that our methods scale incredibly well. Excited to see what people will build with this!
Accurate guidance has been a notoriously hard problem, but in this work, we’re bringing TWO (!) solutions to the table. The recipe for success:
1️⃣ Speed: Use distilled models (flow maps, mean flows, consistency models).
2️⃣ Exploration: Inject stochasticity to properly explore your search space.
Because this fundamentally improves anything using flow matching and diffusion, we see a lot of potential for applications across audio, robotics, molecules, and beyond.
Paper: arxiv.org/abs/2602.05993
Code: github.com/PeterHolderrie…
Huge thanks to an amazing team: Douglas Chen, @LucaEyring, @ishin_shah, Giri Anantharaman, @electronickale, @zeynepakata, Tommi Jaakkola, @nmboffi, and @max_simchowitz. It was awesome bringing this to life together!View quoted post on X
