Will RLHF increase training costs?

The core cost drivers involve recruiting, managing, and compensating skilled human annotators to create preference pairs or rank model outputs. This process is inherently time-consuming and expensive, especially as dataset size and complexity grow. Costs also scale with the number of RLHF tuning iterations needed and the underlying computational expense of running multiple reinforcement learning cycles. Ensuring annotator consistency and quality control further adds to the overhead.

While costly, RLHF aims to enhance AI alignment and usefulness. To manage expenses, strategies include leveraging partial automation tools for data generation, prioritizing data quality over sheer quantity, and carefully evaluating the alignment benefits against the incurred costs for the specific application.