Scaling laws for reward model overoptimization

AI Impact Summary

This CAPABILITY update formalizes scaling laws for reward-model overoptimization, likely affecting RLHF-style pipelines where reward models guide policy optimization. As scale increases, the marginal gains from tuning reward signals and data may shift, potentially altering convergence behavior and the stability of aligned outputs. Teams should expect new guidance on the interaction between model capacity, dataset size, and reward signal strength, and plan experiments to identify optimal operating points to avoid diminishing returns or reward gaming.