Non-monotonic scaling laws in reinforcement learning evaluation

This analysis demonstrates that the asymptotic performance of reinforcement learning algorithms lacks a monotone relationship between performance rankings and data regimes. The study introduces theoretical foundations for scaling laws to improve evaluation and design paradigms.