  Large Language Models (LLMs) have revolutionized natural language processing,
yet aligning these models with human values and preferences using RLHF remains
a significant challenge. This challenge is characterized by various
instabilities, such as reward hacking and catastrophic forgetting. In this
technical report, we propose two innovations to stabilize RLHF training: 1)
Advantage Model, which directly models advantage score i.e., extra reward
compared to the expected rewards and regulates score distributions across tasks
to prevent reward hacking. 2) Selective Rehearsal, which mitigates catastrophic
forgetting by strategically selecting data for PPO training and knowledge
rehearsing. Our experimental analysis on public and proprietary datasets
reveals that the proposed methods not only increase stability in RLHF training
but also achieve higher reward scores and win rates.
