arXiv:2606.24014v1 Announce Type: new Abstract: As AI systems are deployed across increasingly diverse and high-stakes settings, model alignment must generalize beyond the tasks and domains seen during training. This is especially important for reinforcement learning (RL), which can introduce unexpected misalignment through reward hacking, deception, or other unintended strategies. We study whether RL on beneficial behavior, instantiated in realistic domains, can produce broad and persistent alignment generalization beyond the training distribution. We construct a dataset of realistic situatio
The increasing deployment of AI systems in high-stakes environments necessitates rigorous research into alignment and generalization to prevent unintended consequences.
This research is crucial for developing AI that remains beneficial and controllable as it operates beyond its initial training parameters, mitigating risks of misalignment in complex deployments.
The focus on broad and persistent alignment generalization in RL suggests a potential for more robust and trustworthy autonomous AI systems across diverse applications.
- · AI developers
- · High-stakes industries (e.g., defense, healthcare)
- · AI ethics and safety researchers
- · Developers of narrow, brittle AI systems
- · Sectors unprepared for autonomous AI risks
Improved methods for training aligned and generalizable reinforcement learning models are developed and adopted.
Increased trust in AI deployment across critical infrastructure and decision-making processes.
Reduced likelihood of catastrophic AI misalignment events, accelerating broader societal integration of advanced AI.
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Read at arXiv cs.AI