Large language models improve at mathematical reasoning after instruction tuning, reinforcement learning, or knowledge distillation. However, it is unclear whether these improvements result from major changes in the transformer layers or from minor adjustments that preserve the base model’s layer importance structure. We investigate this question through systematic layer-wise ablation experiments, examining base, instruction-tuned, knowledge-distilled, and reinforcement learning with verifiable rewards (RLVR) trained variants on mathematical reasoning benchmarks. Our findings show that mathematical reasoning gives rise to a specific layer importance structure, and this structure persists across all post-training paradigms. Removing such layers causes accuracy drops of up to 80%. In contrast, non-mathematical tasks like factual recall exhibit no such critical layers. This distinction suggests that mathematical reasoning relies on specialized layers that emerge during pre-training and stay unchanged under various post-training methods, whereas other non-reasoning tasks do not exhibit any critical layers.