Weak to strong

Weak to strong is an AI alignment research paradigm developed by OpenAI where weaker AI models are used to supervise and train stronger AI systems, addressing the fundamental challenge of how humans can maintain oversight over superintelligent AI that may eventually surpass human capabilities in specific domains. This approach explores whether weak supervisors can effectively guide more capable models to perform tasks correctly even when the supervisor cannot fully evaluate the stronger model’s outputs or reasoning processes. Weak to strong supervision involves training a weak model on limited data, then using it to generate training signals for a stronger model that has access to more computational resources, parameters, and data while maintaining alignment with desired behaviors and values. The paradigm utilizes techniques including constitutional AI, recursive reward modeling, scalable oversight methods, and iterative improvement processes that enable weak supervisors to provide effective guidance despite capability gaps between supervisor and student models. Enterprise applications of weak to strong principles inform AI safety protocols, model alignment strategies, and governance frameworks for organizations developing advanced AI systems that may exceed human expertise in specific domains.

Advanced weak to strong implementations incorporate verification mechanisms, confidence estimation, human oversight protocols, and safety measures that ensure stronger AI systems remain beneficial, controllable, and aligned with organizational objectives while enabling capability scaling that maintains human agency and strategic control over AI decision-making processes.