  As language models become more powerful, training and evaluation are
increasingly bottlenecked by the data and metrics used for a particular task.
For example, summarization models are often trained to predict human reference
summaries and evaluated using ROUGE, but both of these metrics are rough
proxies for what we really care about -- summary quality. In this work, we show
that it is possible to significantly improve summary quality by training a
model to optimize for human preferences. We collect a large, high-quality
dataset of human comparisons between summaries, train a model to predict the
human-preferred summary, and use that model as a reward function to fine-tune a
summarization policy using reinforcement learning. We apply our method to a
version of the TL;DR dataset of Reddit posts and find that our models
significantly outperform both human reference summaries and much larger models
fine-tuned with supervised learning alone. Our models also transfer to CNN/DM
news articles, producing summaries nearly as good as the human reference
without any news-specific fine-tuning. We conduct extensive analyses to
understand our human feedback dataset and fine-tuned models We establish that
our reward model generalizes to new datasets, and that optimizing our reward
model results in better summaries than optimizing ROUGE according to humans. We
hope the evidence from our paper motivates machine learning researchers to pay
closer attention to how their training loss affects the model behavior they
actually want.
