Dual Active Learning for Reinforcement Learning from Human Feedback

Understand

Aligning large language models (LLMs) with human preferences is critical to recent advances in generative artificial intelligence.

• Reinforcement learning from human feedback (RLHF) is widely applied to achieve this objective.
• A key step in RLHF is to learn the reward function from human feedback.
• However, human feedback is costly and time-consuming, making it essential to collect high-quality conversation data for human teachers to label.