  Reinforcement Learning from Human Feedback (RLHF) can be used to capture
complex and nuanced properties of text generation quality. As a result, the
task of text summarization has been identified as a good candidate for this
process. In this paper, we explore how preference agreement impacts the
efficacy of RLHF for summarization. We show that sampling human preferences to
include a range of annotator agreement results in (1) higher accuracy reward
models and (2) alters the characteristics of quality captured. We additionally
show improvements in downstream generation when using a reward model trained
with a range of preference agreements. Our contributions have implications for
the design of synthetic datasets as well as the importance of considering
quality differentials in comparison-based data.
