Weak-to-Strong Generalization

Weak-to-strong generalization is the phenomenon where more capable AI models can learn to perform better than their less capable supervisors by generalizing beyond the supervisor’s demonstrated abilities. This concept addresses the alignment challenge of training advanced AI systems using weaker oversight, where the supervisory signal comes from less capable models or limited human feedback. The approach leverages the strong model’s inherent capabilities while using weak supervision for guidance, enabling performance that exceeds the supervisor’s baseline. Implementation techniques include reward modeling where weak models provide training signals for stronger ones, constitutional AI methods that use simple rules to guide complex behavior, and iterative amplification where weak models help train stronger successors. This paradigm is crucial for superalignment research, addressing how to maintain AI safety and alignment as models become more capable than their human supervisors. For AI agents, weak-to-strong generalization enables scalable oversight methods and safety alignment strategies essential for deploying increasingly sophisticated autonomous systems.