bibtext: @inproceedings{
  huang2024the,
  title={The N+ Implementation Details of {RLHF} with {PPO}: A Case Study on {TL};{DR} Summarization},
  author={Shengyi Huang and Michael Noukhovitch and Arian Hosseini and Kashif Rasul and Weixun Wang and Lewis Tunstall},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=kHO2ZTa8e3}
  }
abstract: This work is the first to openly reproduce the Reinforcement Learning from Human Feedback (RLHF) scaling behaviors reported in OpenAI's seminal TL;DR summarization work. We create an RLHF pipeline from scratch, enumerate over 20 key implementation details, and share key insights during the reproduction. Our RLHF-trained Pythia models demonstrate significant gains in response quality that scale with model size, with our 2.8B, 6.9B models outperforming OpenAI's released 1.3B checkpoint. Our results highlight best practices in data, training, and evaluation for RLHF.
  We publicly release the trained model checkpoints and code to facilitate further research and accelerate progress in the field at  https://github.com/vwxyzjn/summarize_from_feedback_details
title: The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
authors: Shengyi Huang Michael Noukhovitch Arian Hosseini Kashif Rasul Weixun Wang Lewis Tunstall