Excited to announce that our work on “Discovering state-of-the-art RL algorithms” is finally published in @Nature! In this work, we meta-learned RL algorithms at scale.
Paper: https://t.co/3V4TmPTWm4
Blog: https://t.co/G65ReK2iMs
See thread 👇
🏆1000 Layer Networks for Self-Supervised RL wins a Best Paper Award at #NeurIPS25 !
Proud of @kevin_wang3290 @IJ_Apps@m_bortkiewicz for all the hard work they put into this!
👇for key results and open problems!
What makes RL hard is the _time_ axis⏳, so let's pre-train RL policies to learn about _time_! Same intuition as successor representations 🧠, but made scalable with modern GenAI models 🚀.
Excited to share new work led by @chongyiz1, together with @seohong_park and @svlevine!