arXiv:2603.10938v2 Announce Type: replace-cross Abstract: Safe Reinforcement Learning from Human Feedback (RLHF) typically enforces safety through expected cost constraints, but the expectation captures only a single statistic of the cost distribution and fails to account for distributional uncertainty, particularly under heavy tails or rare catastrophic events. This limitation is problematic when robustness and risk sensitivity are critical. Stochastic dominance offers a principled alternative by comparing entire cost distributions rather than just their averages, enabling direct control over
This research addresses a critical limitation of current Safe RLHF approaches, which are insufficient for real-world applications where catastrophic events are rare but highly impactful.
Improving safety and risk control in AI, especially through methods like stochastic dominance, is crucial for deploying autonomous agents in sensitive and high-stakes environments.
The shift from expectation-based safety to distributional risk control in RLHF introduces a more robust framework for managing uncertainty and rare, severe outcomes in AI systems.
- · AI safety researchers
- · Developers of autonomous systems
- · Industries requiring high-integrity AI (e.g., healthcare, defense)
- · AI developers ignoring advanced risk mitigation
- · Legacy AI safety methodologies
AI systems will be developed with a more sophisticated understanding and control of extreme risks, moving beyond average-case performance.
This improved risk control could accelerate the deployment of autonomous systems in critical infrastructure and high-stakes decision-making.
Enhanced AI safety might lead to greater public trust and broader adoption of AI technologies, potentially influencing regulatory frameworks globally.
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Read at arXiv cs.AI