Learning to Summarize with Human Feedback

Maya and Leo dig into OpenAI's Learning to Summarize from Human Feedback — the paper where pairwise human picks replaced reference matching as the training target. They walk the pipeline as three stations (the Two-Card Choice, the Borrowed Judge, the Tether), stage the real fight between preference optimization and cheap reproducible metrics, and end on the over-optimization curve where the judge's score keeps climbing while human preference falls. Sources: • Learning to Summarize from Human Feedback: https://arxiv.org/pdf/2009.01325 • Learning to summarize with human feedback: https://openai.com/index/learning-to-summarize-with-human-feedback/ • summarize-from-feedback: https://github.com/openai/summarize-from-feedback • ROUGE: A Package for Automatic Evaluation of Summaries: https://aclanthology.org/W04-1013/