Peter Potaptchik

✨🚀Reward alignment just hit a major milestone🚀✨ Controlling generative models is usually bottlenecked by expensive trajectory rollouts 🐢🐌 or massive particle ensembles 🐢🐌 Meta Flow Maps (MFMs) eliminate both We extend consistency models and flow maps into the stochastic regime—enabling efficient generative control Is that a text-to-image model you see below? Nope. It’s just ImageNet with inference-time steering 🤯🤯🤯 Want unbiased off-policy fine-tuning ? We got that too 📜arxiv.org/abs/2601.14430 🌐meta-flow-maps.github.io 🧵1/10

Code is now available here: github.com/adh1s/mfm Great work @adhisarav @PPotaptchik !

@adhisarav @AbbasMammadov11 @msalbergo @yeewhye Tagging some people who might be interested! @sedielem @ArnaudDoucet1 @du_yilun @HannesStaerk @mmbronstein @ValentinDeBort1 @pabbeel @svlevine @bose_joey @AlexanderTong7 @RickyTQChen @jaschasd @sirbayes @ArashVahdat

📜 arXiv: arxiv.org/abs/2601.14430 🌐 Project Page: meta-flow-maps.github.io 💻 Code: Forthcoming Huge shoutout to my co-first author @adhisarav! 🚀✨ And a special thanks to our other incredible co-authors @AbbasMammadov11, Alvaro Prat, @msalbergo and @yeewhye Really excited about future directions here! 🧵 10/10

✨🚀Reward alignment just hit a major milestone🚀✨ Controlling generative models is usually bottlenecked by expensive trajectory rollouts 🐢🐌 or massive particle ensembles 🐢🐌 Meta Flow Maps (MFMs) eliminate both We extend consistency models and flow maps into the stochastic regime—enabling efficient generative control Is that a text-to-image model you see below? Nope. It’s just ImageNet with inference-time steering 🤯🤯🤯 Want unbiased off-policy fine-tuning ? We got that too 📜arxiv.org/abs/2601.14430 🌐meta-flow-maps.github.io 🧵1/10

@msalbergo Tagging some people who might be interested! @bose_joey @thjashin @ArnaudDoucet1 @du_yilun @k_neklyudov @AlexanderTong7 @yeewhye @HannesStaerk @mmbronstein @nmboffi @ValentinDeBort1 @JamesTThorn @RickyTQChen @carles_domingo @wgrathwohl @lipmanya

@msalbergo But wait, is there cool theory too? Oh boy yes!🤤 We've got: 🔹Connections to SOC 🔹Esscher transform interpretation 🔹Higher-order cumulants 🔹Optimal control variates 🔹Connections to Weighted Flow Matching 🔹Extensions to Flow Maps 📄: arxiv.org/abs/2512.21829 (5/5)

🎆⭐️You thought 2025 would end quietly? Think again. One last exciting update: Tilt Matching!⭐️🎆 We propose a simple, scalable algorithm to sample unnormalized densities and fine-tune generative models 📄: arxiv.org/abs/2512.21829 With @brianlee_lck @msalbergo (1/5)

Our paper is @ NeurIPS!! TLDR: we study how inductive bias in score matching shapes sample geometry - Argue score approximation ≈ density smoothing in log-domain - Prove log-domain smoothing preserves data geometry - Different kernels create different interpolating geometries

Also, please share 🤓: I'll be at NeurIPS Dec 4-8. I am hiring PhD students and postdocs this year to start at @Harvard @KempnerInst. We work across problems in ML, applied math, probability, and biology, with the goal of all learning from each other. Find me at @NeurIPSConf, DM me, or shoot me an email! For a flavor of recent topics, see: malbergo.me/papers.html malbergo.me/research-theme…