arXiv:2512.13788v3 Announce Type: replace Abstract: Safety-critical learning requires policies that improve performance without leaving the safe operating regime. We study constrained policy learning where model parameters must satisfy rollout-based safety constraints that can be evaluated but not differentiated analytically. We propose SCPO, a sampling-based weight-space projection method that enforces safety directly in parameter space without requiring gradient access to the constraint functions. SCPO constructs a local safe region by combining rollout-based safety evaluations with smoothne
The increasing sophistication and deployment of AI in real-world, safety-critical applications necessitates robust methods for constrained policy learning to guarantee safe operation.
Ensuring the safety of AI systems, particularly in autonomous decision-making scenarios, is paramount for public acceptance, regulatory approval, and scalable deployment of advanced AI agents.
This research introduces a novel method to enforce safety constraints in parameter space for AI policies without relying on gradient access, enabling safer and more practical AI deployments.
- · AI Safety Researchers
- · Robotics Developers
- · Autonomous Systems Industry
- · Healthcare AI
- · Inflexible AI Development Methodologies
- · Companies with Poor Safety Standards
Safer reinforcement learning algorithms allow for the deployment of AI in more sensitive and critical domains.
Increased trust in AI systems could accelerate the adoption of autonomous technologies across various sectors, including manufacturing and logistics.
Enhanced safety frameworks become a competitive differentiator for AI-driven products, influencing market consolidation and regulatory standards globally.
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