The N+ Implementation Details Of RLHF With PPO: A Case Study On TL;DR Summarization

Huang Shengyi, Noukhovitch Michael, Hosseini Arian, Rasul Kashif, Wang Weixun, Tunstall Lewis. Arxiv 2024

[Paper] [Code]    
Agentic Applications Has Code Reinforcement Learning

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}).

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