Learning to summarize with human feedback
OpenAI Blog
Researchers trained language models to summarize text more effectively by using reinforcement learning guided by human feedback rather than traditional supervised learning methods. The approach involved collecting human preferences on model-generated summaries and using those preferences to refine the training process. This method produced models that better aligned with human judgments about summary quality compared to models trained with standard techniques.
Why it matters
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.