arXiv:2605.25267v1 Announce Type: new Abstract: Safe in-context reinforcement learning (ICRL) adapts online from interaction history without test-time parameter updates while controlling episode cost under a safety budget. Under out-of-distribution (OOD) deployment shifts, pretraining-only safe ICRL can give poor reward-safety tradeoffs because the remaining budget affects behavior only through frozen policy conditioning, not an explicit action-level check against predicted future cost. We propose a latent Q-Barrier shield that learns a context representation, latent dynamics, and an ensemble
The increasing focus on deploying AI in real-world, safety-critical environments necessitates robust methods for safe adaptation and control.
This research advances the crucial field of safe reinforcement learning, enabling AI systems to operate reliably and budget costs even in novel or out-of-distribution scenarios.
The explicit action-level checks and latent Q-Barrier shielding fundamentally change how safety budgets are managed in in-context reinforcement learning, moving beyond frozen policy conditioning.
- · AI developers
- · Robotics industry
- · Safety-critical AI applications
- · Healthcare AI
- · Developers of unstable AI models
- · Legacy safety control systems
AI systems gain enhanced capabilities to learn and adapt safely in real-world, dynamic environments.
Increased trust and adoption of autonomous AI agents in sensitive applications due to better safety assurances.
Reduced regulatory friction for AI deployment as safety mechanisms become more sophisticated and provable.
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Read at arXiv cs.LG