Come find us at the poster session!
📅 Wed, Jul 8, 2026 • 10:30 PM to 12:15 AM PDT
📍 Hall A
🌐 Project page: https://t.co/xZw4qk9pN7
Huge thanks to my coauthors Kai Fukazawa and @ImanSoltaniPhD for letting me be part of this project.
Excited to share that our paper "RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization" has been accepted to ICML 2026!
What if a policy performs well on average but still produces rare catastrophic outcomes? 🧵
Our work takes a closer look at a standard but often implicit ingredient in offline RL: behavior regularization as a mechanism for OOD control. Beyond behavior matching, our analysis shows it helps bound OOD action probability and stabilize lower tail optimization under shift.
We introduce RAMAC, which uses lower tail return information to suppress risky behavior modes, while behavior regularization keeps the policy close to data supported behavior.
At the same time, how can we control out of distribution (OOD) actions without sacrificing the multiple valid behaviors captured by expressive diffusion or flow policies in offline RL?