Does RLHF Scale? Exploring the Impacts From Data, Model, and Method

Understand

This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs).

• Although RLHF is considered an important step in post-training of LLMs, its scaling potential is still largely unknown.
• We systematically analyze key components in the RLHF framework--model size, data composition, and inference budget--and their impacts on performance.
• Our findings show that increasing data diversity and volume improves reward model performance, helping process-supervision models scale better.