Poster session: Wed, Jul 8, 2026 • 10:30 PM to 12:15 AM PDT, Hall A 🌐 Project page: kaifukazawa.github.io/ramac-project/ 📄 Paper: arxiv.org/abs/2510.02695 💻 Code: github.com/KaiFukazawa/... Thanks to my coauthors Kai Fukazawa and Prof. Iman Soltani for letting me part of this project.

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I am in Seoul at ICML to present our newest paper "Commit to the Bit: Reactive Reinforcement Learning Done Right". We show that finding reactive policies in POMDPs is easier than previously thought and that the ubiquitous 𝑞⋆-realizability assumption is stronger than necessary. 🧵

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I'm excited to share our latest work “Representation Learning Enables Scalable Multitask Deep Reinforcement Learning”. In this work, we revisit a fundamental question in reinforcement learning: What if representation learning was the key ingredient behind scalable RL? 1/🧵

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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?

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In case you missed it-- check out our Paper Skygest preprint! ⤵️

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