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RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (2309.00267v3)

Published 1 Sep 2023 in cs.CL, cs.AI, and cs.LG

Abstract: Reinforcement learning from human feedback (RLHF) has proven effective in aligning LLMs with human preferences, but gathering high-quality preference labels is expensive. RL from AI Feedback (RLAIF), introduced in Bai et al., offers a promising alternative that trains the reward model (RM) on preferences generated by an off-the-shelf LLM. Across the tasks of summarization, helpful dialogue generation, and harmless dialogue generation, we show that RLAIF achieves comparable performance to RLHF. Furthermore, we take a step towards "self-improvement" by demonstrating that RLAIF can outperform a supervised fine-tuned baseline even when the AI labeler is the same size as the policy, or even the exact same checkpoint as the initial policy. Finally, we introduce direct-RLAIF (d-RLAIF) - a technique that circumvents RM training by obtaining rewards directly from an off-the-shelf LLM during RL, which achieves superior performance to canonical RLAIF. Our results suggest that RLAIF can achieve performance on-par with using human feedback, offering a potential solution to the scalability limitations of RLHF.

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In the field of AI, specifically with LLMs, one of the challenges is aligning the behavior and responses of these models with human preferences. Traditionally, this is achieved through Reinforcement Learning from Human Feedback (RLHF), which relies on human-provided labels to guide the learning process. However, obtaining large quantities of high-quality human labels is both time-consuming and costly. As a solution, researchers have explored an alternative called Reinforcement Learning from AI Feedback (RLAIF), which utilizes a powerful, pre-trained LLM to generate these labels instead of relying on human annotators.

The paper in question examines the effectiveness of RLAIF compared to the traditional RLHF by evaluating their performance on three text generation tasks: summarization, helpful dialogue generation, and harmless dialogue generation, as judged by human evaluators. The results demonstrate that RLAIF is either comparable or superior to RLHF in these tasks. Notably, RLAIF surpassed RLHF in creating harmless dialogue, and matched its helpfulness in dialogue generation and summarization, indicating the potential of AI-generated feedback to scale the training process without significant loss in quality.

Furthermore, the paper investigates whether RLAIF can still enhance the performance of a fine-tuned LLM when the label-generating LLM is of the same size as the policy network itself, rather than significantly larger. Even in this scenario, RLAIF managed to improve upon the policy, a finding that suggests the approach doesn't rely on having a larger, more knowledgeable LLM for the labeling process. In a variant of RLAIF, it was found that directly prompting the LLM for reward scores during reinforcement learning surpassed the performance of setups where LLM-generated preferences were first distilled into a separate reward model.

The paper also explores methods to get the best alignment with human preferences by generating AI labels. It was discovered that soliciting chain-of-thought reasoning consistently improves alignment, whereas other techniques like detailed preambles and few-shot in-context learning showed mixed benefits, depending on the task. Additionally, the researchers conducted a paper on the connection between the size of the LLM labeler and its ability to align with human preferences, observing a positive correlation between LLM size and alignment accuracy.

In conclusion, RLAIF was shown to be a promising alternative to traditional RLHF that could significantly reduce both the time and financial costs associated with aligning LLMs to human preferences, with plenty of room for further exploration and optimization of the technique. The findings of this research offer a path toward more efficiently training AI models that are well-aligned with human values and preferences, and thereby more trustworthy and effective in the real world.

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Authors (11)
  1. Harrison Lee (8 papers)
  2. Samrat Phatale (6 papers)
  3. Hassan Mansoor (8 papers)
  4. Thomas Mesnard (18 papers)
  5. Johan Ferret (24 papers)
  6. Kellie Lu (1 paper)
  7. Colton Bishop (5 papers)
  8. Ethan Hall (2 papers)
  9. Victor Carbune (11 papers)
  10. Abhinav Rastogi (29 papers)
  11. Sushant Prakash (15 papers)
Citations (275)
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