AI RESEARCH
An Information-Theoretic Analysis of OOD Generalization in Meta-Reinforcement Learning
arXiv CS.LG
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ArXi:2510.23448v2 Announce Type: replace In this work, we study out-of-distribution (OOD) generalization in meta-reinforcement learning from an information-theoretic perspective. We begin by establishing OOD generalization bounds for meta-supervised learning under two distinct distribution shift scenarios: standard distribution mismatch and a broad-to-narrow