Model editing aims to update large language models (LLMs) with new knowledge without full retraining, but existing methods mainly focus on fact-level knowledge. Rules that support mathematical and scientific reasoning must remain consistent across formulas, descriptions, and instances, making localized updates less effective for rule-level editing. In this paper, we construct RuleEdit-200, a manually verified benchmark of 200 mathematical and physical rules, each instantiated in formula, description, and instance forms. Using this benchmark, we conduct fine-grained causal tracing on four LLMs and identify a form-specific layer-wise separation of rule knowledge: formulas and descriptions are associated with earlier layers, whereas instances rely on a distinct set of middle-layer representations. This finding suggests that rule-level editing requires separate updates for different forms. Based on this insight, we propose Distributed Multi-Layer Editing (DMLE), which updates formulas, descriptions and instances at their corresponding layer ranges. Experiments on RuleEdit-200 show that DMLE remains competitive on standard editing metrics while substantially improving rule-level editing, with average gains of 18.22% and 61.00% in instance portability and rule understanding over the best baseline on each model.