arXiv:2604.26360v2 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) systems face a compounding alignment challenge: not only are learned reward models uncertain about unseen state-action pairs, but the human preference annotations they are trained on are themselves inconsistent, context-dependent, and noisy. Existing approaches address these uncertainty sources in isolation - epistemic uncertainty is used to guide exploration, while preference uncertainty is absorbed during reward model training but discarded during policy optimization. We introduce Uncertaint
The increasing sophistication and widespread deployment of AI systems, particularly those using reinforcement learning from human feedback (RLHF), necessitates robust solutions for alignment challenges like reward hacking.
This research addresses a fundamental limitation in current AI alignment, promising more reliable and safer AI systems by mitigating the risk of unintended or exploitative behaviors arising from imperfect reward models.
Approaches to AI safety and alignment will evolve to incorporate more sophisticated uncertainty-aware strategies, moving beyond isolated treatments of epistemic and preference uncertainty.
- · AI safety researchers
- · Developers of general-purpose AI
- · Industries deploying RLHF systems
- · Users of AI systems
- · AI systems prone to reward hacking
- · Organizations relying on simplistic reward models
AI systems will become more robust and less susceptible to gaming their reward functions.
Increased trust in AI systems will accelerate their adoption in critical applications.
The development of truly autonomous and aligned AI agents becomes more feasible, potentially leading to breakthroughs in agentic AI capabilities.
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Read at arXiv cs.LG