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Action chunking is a great idea in robotics: by getting a model to produce a short sequence of actions, it _just works better_ for some mysterious reason. Now it turns out this can help in RL too, and it's a bit clearer why: action chunks help explore and help with backups. 🧵👇

Action chunking works really well in imitation learning, and is essential to learning good BC policies in robotics. Can/should we apply the same idea in RL? We find that RL in the action chunk space, when done right (we call it ✨Q-chunking ✨), can be highly efficient🧵👇

Action chunking + expressive action distribution —> Better exploration for RL! This was one of the biggest lessons we learned in DPPO as well

Qiyang Li @ ICML will help present OTTER tomorrow at #ICML2025! A lightweight, instruction-following VLA! See OG post below! 👉Code already released at ottervla.github.io Poster will be presented at West Exhibition Hall B2-B3 #W-409 Tue 15 Jul 11 a.m. PDT — 1:30 p.m. PDT

Check out our poster on Wednesday 4:30p-7p (West Exhibition Hall, #713)!!

Check out our poster 11-1:30 today @ West exhibition hall W-713!

Had so much fun working on this😊 PyTorch and JAX implementations are both out!

Check out our poster on “Reinforcement Learning with Action Chunking” tomorrow 11:45-14:15 @ EXAIT Workshop (Meeting Room 205-207)!

I wrote a fun little article about all the ways to dodge the need for real-world robot data. I think it has a cute title. sergeylevine.substack.com/p/sporks-of-agi