  Reinforcement learning from human feedback (RLHF) has emerged as an effective
approach to aligning large language models (LLMs) to human preferences. RLHF
contains three steps, i.e., human preference collecting, reward learning, and
policy optimization, which are usually performed serially. Despite its
popularity, however, (fixed) reward models may suffer from inaccurate
off-distribution, since policy optimization continuously shifts LLMs' data
distribution. Repeatedly collecting new preference data from the latest LLMs
may alleviate this issue, which unfortunately makes the resulting system more
complicated and difficult to optimize. In this paper, we propose reward
learning on policy (RLP), an unsupervised framework that refines a reward model
using policy samples to keep it on-distribution. Specifically, an unsupervised
multi-view learning method is introduced to learn robust representations of
policy samples. Meanwhile, a synthetic preference generation approach is
developed to simulate high-quality preference data with policy outputs.
Extensive experiments on three benchmark datasets show that RLP consistently
outperforms the state-of-the-art. Our code is available at
\url{https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/rlp}.
