Making offline RL more honest, reproducible, and robust.
🌹 Today we're releasing Unifloral, our new library for Offline Reinforcement Learning! We make research easy: ⚛️ Single-file 🤏 Minimal ⚡️ End-to-end Jax Best of all, we unify prior methods into one algorithm - a single hyperparameter space for research! ⤵️
2
10
1
106
Jakob Foerster retweeted
Introducing Nested Learning: A new ML paradigm for continual learning that views models as nested optimization problems to enhance long context processing. Our proof-of-concept model, Hope, shows improved performance in language modeling. Learn more: goo.gle/47LJrzI @GoogleAI
Words of wisdom.
Bach is so timeless because he wasn't writing for people, he was writing for a higher power. Try writing your next paper for God. Imagine how many rubbish papers we wouldn't see anymore. Your audience sees your every thought and intention. There would be no ego, no pretense.
1
21
Jakob Foerster retweeted
In many industry frontier labs, there’s a perceived tension between breadth and depth. It’s often missed that breadth *enables* meaningful depth. It may seem like you are advancing a frontier, but you are in fact in a myopic echo chamber. ML theory suffers badly from this effect.
8
8
2
155
Goat
Leaving Meta and PyTorch I'm stepping down from PyTorch and leaving Meta on November 17th. tl;dr: Didn't want to be doing PyTorch forever, seemed like the perfect time to transition right after I got back from a long leave and the project built itself around me. Eleven years at Meta. Nearly all my professional life. Making many friends for life. Almost eight years leading PyTorch, taking it from nothing to 90%+ adoption in AI. Walking away from this was one of the hardest things I've ever done. But I'm leaving with a full heart. PyTorch handles exascale training now. It powers foundation models that are redefining intelligence. It's in production at virtually every major AI company. It's taught in classrooms from MIT to rural India. The tools I dreamed about making accessible? They are. The barrier to entry I wanted to lower? It's almost gone. To be clear, there’s so much more to do. As long as AI evolves at a breakneck pace, PyTorch will continue to play catch up. Obsessing over the yet-to-come sometimes makes us forget how much we’ve already done. To everyone who built this with me—who believed research should be joyful, that tools should be elegant, that open source changes everything—thank you. This wasn't my journey. It was ours. What's next for me? Something small. Something new. Something I don't fully understand yet. Something uncomfortable. I could have moved to something else inside Meta. But I needed to know what's out there. I needed to do something small again. I couldn't live with the counterfactual regret of never trying something outside Meta. It's very hard to leave. I probably have one of the AI industry’s most leveraged seats, I lead the software layer that powers the entire AI industry. Every major AI company and hardware vendor are on a speed dial. This kind of power is really hard to give up. But curiosity ultimately won out in my head. Keep making AI delicious and accessible. I'll be watching. Probably filing issues. Definitely staying involved. Is PyTorch going to be okay? I don't want to be doing PyTorch forever. I don't want to be like Guido or Linus— bound to a single thing for decades. Last November, coinciding with the birth of my daughter, I started planning my exit with Aparna. My goal was to leave PyTorch in a good and stable place. By this August, during the second half of my parental leave, I knew: Edward, Suo, Alban, Greg, John, Joe and Jana were ready. The team faced hard people, product, technical and organizational problems and didn’t feel the need to lean back on me to solve these for them (unlike in the past). The product story they crafted for the PyTorch Conference was coherent—really coherent. The things I'd flagged red were turning healthy. The project didn't need me anymore. Unlike 2020-2022 (when I stepped down to go do robotics and came back when Lin, Dima and Dwarak left), I have strong confidence that this time PyTorch is truly resilient. The most aligned culture carriers of PyTorch – Greg, Alban, Ed, Jason and Joe are at the decision table now, and people with strong value alignment – Suo, John and Jana have joined them at the table. And there’s a long list of equally value-aligned people willing to sit at the table should any of these people leave. There are many little things that make up my confidence on the people – John worked on Julia and open-source for a very long time (in fact we hacked a Torch.jl in 2015), Suo has been the strongest systems builder and strategic partner I’ve had for the past two years, and Jana worked on resilient core systems for a very long time, I’ve had long technical and organizational discussions with her over the past few months that give me confidence. And the product lineup and execution in 2025 should be sufficient evidence for any remaining doubt. I’m confident that this band of PyTorchers are going to do exceptionally well. PyTorch might change in flavor because I no longer impose my own taste from the top, but I’m confident that the values are going to stay intact and the product is going to be awesome. My time at Meta The early years of FAIR were absolutely magical. I was part of a small family of absolutely brilliant people building state-of-the-art AI out in the open. From working on GANs with Emily Denton, Rob Fergus, Leon Bottou, Martin Arjovsky and the (now legendary) Alec Radford to building Starcraft bots with Gabriel Synnaeve, to building the first FAIR Cluster with Howard Mansell, to working on object detection with Adam Lerer and Piotr Dollar, to building PyTorch. It was more fun than I can describe in words. 2015 and 2016 were probably the most productive and professionally enjoyable years of my life. I’ll probably romanticize this period of my life forever. When I joined FAIR, I had massive impostor syndrome, and the first 3 months were very very difficult. I can’t credit Andrew Tulloch enough for being the most thoughtful, kind and welcoming mentor, without whom I wouldn’t have made it. I’m so damn bullish for Meta just from the fact that he’s back. --- My time on PyTorch was special. I loved every part of building it—designing it, managing it, being the PM, TL, comms lead, doc engineer, release engineer, squashing bugs, growth hacking, turning it into a coherent product with hundreds of people, transitioning it to industry stakeholdership – the whole nine yards. To the core PyTorch team at Meta: the engineers, researchers, open-source maintainers, docs writers, CI infrastructure folks, hardware partners, the community builders. To the hundreds more inside and outside Meta—thank you. You turned a library into a movement. There are too many people to credit and thank, but I can't not mention Adam Paszke, Sam Gross, Greg Chanan, Joe Spisak, Alban Desmaison, Edward Yang, Richard Zou, Tongzhou Wang, Francisco Massa, Luca Antiga, Andreas Köpf, Zach DeVito, Zeming Lin, Adam Lerer, Howard Mansell and Natalia Gimelshein. And Schrep. They made the launch happen. And so many more people became centrally important later: Lu Fang, Xiaodong Wang, Junjie Bai, Nikita Shulga, Horace He, Mark Saroufim, Jason Ansel, Dmytro Dzhulgakov, Yangqing Jia, Geeta Chauhan, Will Constable, Briah Hirsh, Jane Xu, Mario Lezcano, Piotr Balecki, Yinghai Lu, Less Wright, Andrew Tulloch, Bruce Lin, Woo Kim, Helen Suk, Chris Gottbrath, Peng Wu, Joe Isaacson, Eli Uriegas, Tristan Rice, Yanan Cao, Elias Ellison, Animesh Jain, Peter Noordhuis, Tianyu Liu, Yifu Wang, Lin Qiao and hundreds more. It’s criminal of me to not take the space to list out everyone else I should be mentioning here. PyTorch is nothing without its people ❤️. The most joyful moments of building PyTorch was meeting users eager to share their happiness, love and feedback. I remember a grad student coming to me at Neurips 2017, in a slurring emotional voice he said he’d been trying to make progress on his research for 3 years but within 3 months of using PyTorch he made so much progress that he was ready to graduate. That moment made it tangible that what we do matters, a lot, to a lot of people, even if you don't constantly hear from them. I do miss the intimacy of the PyTorch community, with a 300 person conference that felt like an extended family gathering, but I feel that’s a small price to pay considering the scale of impact PyTorch is truly having today – yes the Conference is now 3,000 people where market-moving deals get brokered, but it’s helping orders of magnitude more people to do their best AI work. I miss the intimacy, but I'm proud of that growth. --- To Mark Zuckerberg and Mike Schroepfer, who believed that open-sourcing is fundamentally important and is a sound business strategy. This is so hard to understand for most people within the course of business, but we’ve run lock-step on this strategy without ever having to discuss it. Without you two, neither FAIR nor PyTorch would’ve happened. And those mean so much to me. To Yann LeCun and Rob Fergus, for building the magical early FAIR that I so revere. To Aparna Ramani, a leader that I find so rare at Meta in her ability to hold a really high bar for the org, technically brilliant with the span to discuss deep infra systems and industry-strategy within the same conversation and for being an absolute execution-machine! I’ve learned so much from you. To Santosh, Kaushik, Delia, Oldham and Ben for being so welcoming to Infra. For someone coming over from FAIR with a wildly different culture, you all made me feel at home and made me part of the family, and thank you for that. To all my managers who've championed me through the PSC video game – Serkan, Howard, Jerome, Abhijit, Yoram, Joelle, Aparna and Damien – I owe you a lifetime of drinks. --- Signing off for now. —Soumith
1
38
Jakob Foerster retweeted
Our new @NeurIPSConf paper: Measuring What Matters📄 We reviewed 445 LLM benchmarks from top AI conferences and found systematic weaknesses in: 1️⃣ Statistical rigour 2️⃣ Concept definition 3️⃣ Dataset construction blog + paper 👇 oxrml.com/measuring-what-mat…
A long time ago I was going to start an "offline café", i.e. coffee shop inside a faraday cage. Someone please finally do this.
I need to take more 14 hour flights without internet. Carefully read three new (amazing!) papers, wrote two letters, and watched two movies. Need a faraday cage at home.
3
46
Predicting the future is: boring in the short term, impossible in the medium term, and interesting in the long term.
3
16
I haven't read the paper but I like the ambition. The end of history has been declared too often
There is absolutely no fundamental reason we build AI the way we do today. There certainly is a radically different approach that is orders of magnitude more energy efficient. I’m going to find it before I die arxiv.org/abs/2510.23972
4
2
32
One of the most important deadlines of the year for anyone looking to do a phd in cooperative AI (which is in turn one of the most important challenges of the field!!)
Applications are now open for the 2026 Cooperative AI PhD Fellowship! Benefits include research support, conference and compute budgets, and for outstanding candidates, up to $40,000 per year to cover living expenses.
1
2
31
Monday night 10:30pm, slides for keynote next morning at 10:30am (in London) not finished. Choose your adventure.
41% go to bed, get sleep
59% stay up, fininsh slides
123 votes • Final results
41% go to bed, get sleep
59% stay up, fininsh slides
123 votes • Final results
3
2
if you go to the gym to stay fit you should be willing to think to stay sharp
3
5
1
60
Talent Density X Agency = Fun @FLAIR_Ox
1
2
2
70
Jakob Foerster retweeted
great paper from Yushi and team
🚨 New preprint! Agentic Reinforcement Learning for Search is Unsafe. We show two simple attacks cut refusal rates by ~60% and collapse safety by ~82% across Qwen & Llama. 👉Safe search must be part of Agentic RL training, not an afterthought. #AISafety #AgenticAI #RL
1
10
Great post, but this off-hand comment at the end of the post is a a bit... strange? "Obviously, all of the video generation above is not limited to evaluation. It can be used to perform large-scale reinforcement learning in closed-loop to achieve superhuman performance." Big if true (and I hope true), but conventional wisdom has it that this is not trivial (e.g. you now need a reward function and you need to stop the agent from exploiting any inaccuracies of the world model). Oh, you also at some point need to think about superhuman self-play vs a superhuman best response to human behaviour (I.e. a SFD update now makes your world model stale 🤯 ). And then (if you succeed and there are loads of cars like this on the road) you need to consider exploitability of your policies by other (e.g. human) actors - if not, you'll start changing equilibrium behaviour. E.g. do Tesla's honk to punish bad behaviour? And once you have done all of this, please tell Elon I TOLD YOU SO back in 2016. His response: we'll have so much data, supervised learning will be enough.
17
Google brain around 2016 also was a very special place. People were pursuing a ton of diverse, exploratory and ambitious directions to push the field forward. Here's a section of @JeffDean's Google Brain "2017 Look-back", see if you can spot the transformer :) The full document is in the link below and is full of wisdom. It also features many of the ideas that are now finally becoming mainstream and some alternative approaches that have been forgotten by the community. Needless to say that many of the current "big shots" in AI were at brain during that period (or had just left, @ilyasut!), often as interns (like me) or AI residents.
6
25
3
329
Yuandong was my manager during my first stint at FAIR. A fantastic researcher. Thank you for everything you have done for FAIR, Meta, and beyond (..and for taking any residual awkwardness out of being layed off by big tech..)
Several of my team members + myself are impacted by this layoff today. Welcome to connect :)
2
8
2
322