Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed 事件

Safety Generalization Under Distribution Shift in Safe Reinforcement Learning: A Diabetes Testbed arXiv:2601.21094v2 Announce Type: replace-cross Abstract: Safe Reinforcement Learning (RL) algorithms are typically evaluated under fixed training conditions. We investigate whether training-time safety guarantees transfer to deployment under distribution shift, using diabetes management as a safety-critical testbed. We benchmark safe RL algorithms on a unified clinical simulator and reveal a safet