Reinforcement Learning from Human and AI Feedback for Large Language Model Alignment: A Review

Abstract

Safe and effective deployment of AI requires that large language models (LLMs) generate it in a way that complies with human values and preferences. The application of Reinforcement Learning from Human Feedback (RLHF) has effectively been applied to fine-tune models on human judgment-based categories, enhancing the helpfulness, coherence, and safety. Nonetheless, RLHF suffers certain limitations, such as using high-quality human labels, being costly, slow to iterate, and not being consistent owing to the subjectivity of annotators. The Reinforcement Learning AI Feedback (RLAIF) has become a scalable and effective method of resolving the challenges. The RLAIF provides an opportunity to use AI-generated preferences, revisions, and reward modeling to automatically fine-tune LLMs without violating ethical and safety standards. This will decrease human efforts, enhance reproducibility, and enhance response harmlessness, uniformity, and ethical compliance. Applications of RLAIF have been successful in dialogue generation, summarization, content personalization and automated reasoning. The review summarizes the recent research of feedback-based reinforcement learning, including underlying mechanisms, practical advantages, constraints, and usage of RLAIF. It points out that AI-based feedback offers a systematic and scalable channel of enhancing alignment, robustness and safety of large-scale language models.