This paper introduces Loop Scaling Laws, the first scaling framework that jointly models recurrence and sparsity in looped Mixture-of-Experts transformers. The work demonstrates that combining recurrence and MoE sparsity delivers complementary efficiency gains: ~3x active-parameter efficiency from sparsity and ~2x total-parameter efficiency from recurrence on reasoning tasks, with practical validation at trillion-token scale.