RLAIF, a reinforcement learning technique that utilizes AI feedback to instruct large language models (LLMs), offers a scalable and cost-effective alternative to RLHF, which relies on human feedback.

• • RLAIF demonstrates comparable or superior performance to RLHF across summarization, helpful dialogue generation, and harmless dialogue generation tasks, as evaluated by human evaluators.

• • RLAIF outperforms a supervised fine-tuned baseline even when the LLM preference labeler is the same size as the policy.

• • Directly prompting the LLM for reward scores achieves better performance than the canonical RLAIF setup, where LLM preference labels are first distilled into a reward model.

• • Extensive studies on techniques for generating aligned AI preferences support the effectiveness of RLAIF.

• • Overall, RLAIF offers a promising solution to the scalability limitations of RLHF, enabling the training of LLMs with high-quality feedback without the need for a large number of human annotators.

• [2309.00267] RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (arxiv.org)