Happy to share our new work: Distributional Functional Rewards (DFR)! 🎉 We improve diversity and fidelity in diffusion fine-tuning 🔧✨ Big thanks to my coauthors @JeongjaeLee0, Jong Chul Ye, @mmbronstein, @Yoshua_Bengio, @AlexanderTong7 and @danyalrehman17! 🙏 Thread below 👇

Stellar lineup at the AITHYRA Next generation AI for Biomedical Science workshop -- next Monday 2 Nov at the IMBA lecture hall in Vienna Biocenter. No registration is required.

Banger! Congrats @rom_pas_ and team 🎉

🚨 New paper alert! Despite recent advances in reward fine-tuning for diffusion and flow models, achieving high rewards remains challenging without sacrificing sample diversity or substantially drifting away from the pretrained distribution. We address this with DFR: Distributional Functional Rewards, a framework that balances reward optimization with preserving desirable properties of the generated distribution! 🧵

Excited to announce the follow-up to our NeurIPS 2026 Oral: Improved Training of Strong Stochastic Flow Maps! We improve the training of SSFMs by unlocking derivative-based losses! Joint work with @antoniofrancaib @rmincam @ChristianKoke @Niklas_TR and @AlexanderTong7

Very happy to share the first paper of my PhD: Normalizing Flow Maps! (NFM)⚡️ We make likelihood evaluation in flow maps fast and tractable by replacing the DiT backbone with a normalizing flow🔄. A big thanks to @tim_hitge, @mmbronstein, @bose_joey and the rest of the team!

🚨 New Paper Alert, 🎤 Accepted for an oral presentation at the NeurIPS 2026 STODY workshop! 📣 Introducing: Normalizing Flow Maps (NFM): Combining few-step transport from Flow maps with tractable likelihoods from Normalizing Flows. This is my first work with my first crop of new PhD students, @francescoshox and @tim_hitge, who genuinely knocked this project out of the park. 📄 Paper: arxiv.org/abs/2610.22711 📝 Blog: the-bose-lab.github.io/norma… 💻 Code: github.com/francescoshox/Nor… 🧵 1/8

Introducing Langevin Flow Maps TLDR: Neural Network Potentials that learn their own stochastic pathwise solution for faster simulation. - Transferable across molecular systems. - Transition dynamics are preserved. - No trajectory data are required. Work together with @sgmccallum, supervised by @AlexanderTong7, and @JamesFosterBath. Arxiv: arxiv.org/abs/2610.05998 Data: huggingface.co/datasets/nikl… Code: github.com/sammccallum/lfm

Our new preprint LLM-to-Flow Distillation (LFD) is finally live! 🧬 I’m convinced language is the right interface for controlling generative models. And that should apply to molecules just as much as images! LLMs know a lot about science. DPO gives us a way to transfer that knowledge into scientific generators. With LFD, “design molecules that bind to the D2 dopamine receptor” becomes a goal we can actually train a flow model to pursue. The LLM builds an adaptive curriculum around what the generator can currently produce. As the model improves, the teaching and guidance from the LLM change too. The cool part is that we can do this with models we already have, without redesigning their architectures or engineering a task-specific proxy reward. Really happy to share this work with Riccardo, Federico and the rest of the team!! Full thread below 👇

Continuous Diffusion LMs went from niche to contender this year, but the recipe is shaky: why self-conditioning? How to embed tokens? 🤨 Our new work takes your one-hot flow from 4% to 50% on TinyGSM without self-conditioning, and explores the design space of CDLMs. 🔥🚀 (1/6)

New preprint: 🚀 Global Transport (GT) couplings for classifier-free guided flows! Surprisingly, a simple label-free optimal transport coupling beats both class-conditional OT and independent couplings once we push the CFG scale up 🧗 reaching 1.91 FID on ImageNet-256