RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Ren Lu, Thomas Mesnard, Johan Ferret, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi

Primary Area: generative models

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Keywords: reinforcement learning, rlhf, rlaif, nlp, large language models, llm, nlp, machine learning

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TL;DR: We show that RL from AI feedback (RLAIF) can achieves results to RL from human feedback (RLHF)

Abstract: Reinforcement learning from human feedback (RLHF) is an effective technique for aligning large language models (LLMs) to human preferences, but gathering high-quality human preference labels is a critical bottleneck. RL from AI Feedback (RLAIF) is an alternative solution that generates preferences labels using an off-the-shelf LLM in lieu of human annotators. We compare RLAIF and RLHF, and we find that RLAIF achieves improvements on par with RLHF, with both RL policies outperforming the baseline supervised fine-tuning policy by approximately 70\% for summarization and 60\% for helpful dialogue generation, as rated by human evaluators. Furthermore, when asked to rate RLAIF against RLHF in a head-to-head comparison, both are equally preferred. These results suggest that RLAIF can achieve human-level performance, offering a potential solution to the scalability limitations of RLHF.

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Submission Number: 3762