Privately Aligning Language Models with Reinforcement Learning
Fan Wu Huseyin A. Inan Arturs Backurs Varun Chandrasekaran Janardhan Kulkarni Robert Sim
Positioned between pre-training and user deployment, aligning large language
models (LLMs) through reinforcement learning (RL) has emerged as a prevailing
strategy for training instruction following-models such as ChatGPT. In this
work, we initiate the study of privacy-preserving alignment of LLMs through
Differential Privacy (DP) in conjunction with RL. Following the influential
work of Ziegler et al. (2020), we study two dominant paradigms: (i) alignment
via RL without human in the loop (e.g., positive review generation) and (ii)
alignment via RL from human feedback (RLHF) (e.g., summarization in a
human-preferred way). We give a new DP framework to achieve alignment via RL,
and prove its correctness. Our experimental results validate the effectiveness
of our approach, offering competitive utility while ensuring strong privacy
protections.