🤖Excited to share SLAP, @yijieisabelliu.bsky.social 's new algorithm using RL to provide better skills for planning! Check out the website for code, videos, and pre-trained models: github.com/isabelliu0/S...

Kids spend years playing with blocks, building spatial+arithmetic skills. Today, AI models just read. While AI research often conflates reasoning with language models, block-building lets us study how embodied reasoning might emerge from exploration and trial-and-error learning!

🚨 Excited to announce our #NeurIPS2025 Workshop: Data on the Brain & Mind 📣 Call for: Findings (4- or 8-page) + Tutorials tracks 🎙️ Speakers include @dyamins.bsky.social @lauragwilliams.bsky.social @cpehlevan.bsky.social 🌐 Learn more: data-brain-mind.github.io

Check out @raj-ghugare.bsky.social's new paper on the surprising effectiveness of normalizing flows (NF) in RL 🚀 This project changed my mind in 2 ways: 1/ Diffusion policies, flow-models, and EBMs have become ubiquitous in RL. Turns out NFs can perform as well -- no ODEs/SDEs required!

tldr: increase the depth of your RL networks by several orders of magnitude. Our new paper shows that very very deep networks are surprisingly useful for RL, if you use resnets, layer norm, and self-supervised RL! Paper, code, videos: wang-kevin3290.github.io/scaling-crl/

Excited to share new work led by @vivekmyers.bsky.social and @crji.bsky.social that proves you can learn to reach distant goals by solely training on nearby goals. The key idea is a new form of invariance. This invariance implies generalization w.r.t. the horizon.