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

arXiv:2601.21094v2 Announce Type: replace 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 safety generalization gap: policies satisfying constraints during training frequently violate safety requirements on unseen patients. We demonstrate that test-time shielding, which filters unsafe actions using

Source: arXiv cs.LG — read the full report at the original publisher.

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