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
The increasing deployment of AI in safety-critical applications necessitates robust mechanisms to ensure reliability and safety under real-world conditions, which often involve distribution shifts not present during training.
This research highlights a critical vulnerability in current safe reinforcement learning approaches, demonstrating that lab-tested safety guarantees do not reliably transfer to diverse real-world environments.
The understanding that safety in AI systems cannot solely rely on training-time guarantees, requiring new methods like test-time shielding to maintain safety in deployment.
- · AI Safety Researchers
- · Healthcare AI Developers
- · Regulatory Bodies
- · Patients
- · Developers of 'Lab-Safe' AI Systems
- · Unregulated AI Deployment
- · Traditional RL Evaluation Methods
Immediate emphasis will be placed on developing and integrating test-time safety mechanisms into AI deployments, especially in critical sectors.
Increased regulatory scrutiny and development of certification standards specifically addressing AI generalization and safety under distribution shifts.
This could lead to a 'safety-first' paradigm shift in AI development, prioritizing robust deployment safety over mere training performance, potentially slowing innovation timelines but increasing reliability.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG