arxiv preprint – Secrets of RLHF in Large Language Models Part I: PPO

In this episode we discuss Secrets of RLHF in Large Language Models Part I: PPO
by Rui Zheng, Shihan Dou, Songyang Gao, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Limao Xiong, Lu Chen, Zhiheng Xi, Yuhao Zhou, Nuo Xu, Wenbin Lai, Minghao Zhu, Rongxiang Weng, Wensen Cheng, Cheng Chang, Zhangyue Yin, Yuan Hua, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang. The paper discusses the challenges in implementing reinforcement learning with human feedback (RLHF) in large language models (LLMs) for the development of artificial general intelligence. The authors analyze the Proximal Policy Optimization (PPO) algorithm and propose an advanced version called PPO-max to improve training stability. They compare RLHF abilities with other models and find that LLMs trained using their algorithm have better understanding of queries and provide more impactful responses.