Навчання з підкріпленням на основі зворотного зв’язку від штучного інтелекту для вирівнювання моделей

Abstract

This paper explores Reinforcement Learning from AI Feedback (RLAIF) as a scalable alternative to human feedback (RLHF) for aligning large language models. It examines the core mechanisms of RLAIF, particularly the Constitutional AI framework, where a superior AI evaluates outputs based on predefined ethical guidelines. Key algorithmic stages and potential challenges like reward hacking are analyzed. The findings highlight RLAIF as an efficient, cost-effective methodology for ensuring AI safety without relying on extensive human annotation.