<ul><li>Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences.</li><li>This paper explores data-driven bottlenecks in RLHF performance scaling, focusing on reward hacking and decreasing response diversity.</li><li>The hybrid reward system, combining reasoning task verifiers (RTV) and a generative reward model (GenRM), is introduced to mitigate reward hacking.</li><li>The novel prompt-selection method, Pre-PPO, is proposed to maintain response diversity and enhance learning effectiveness.</li></ul>

Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback

Discover more