RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
This addresses scalability issues in aligning large language models for researchers and practitioners, though it is incremental as it builds on existing RLHF methods.
The paper tackles the high cost of gathering human preference labels for reinforcement learning from human feedback (RLHF) by introducing reinforcement learning from AI feedback (RLAIF), which uses an off-the-shelf LLM to generate preferences, achieving comparable performance to RLHF on tasks like summarization and dialogue generation, with direct-RLAIF further improving results.
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (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.