The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization
This provides an open-source reproduction and insights for researchers working on RLHF, though it is incremental as it builds on prior work.
This work reproduces OpenAI's RLHF scaling behaviors for TL;DR summarization, creating an open-source pipeline and identifying over 20 key implementation details, with their 2.8B and 6.9B models outperforming OpenAI's 1.3B checkpoint in response quality.
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. We publicly release the trained model checkpoints and code to facilitate further research and accelerate progress in the field (\url{https://github.com/vwxyzjn/summarize_from_feedback_details}).