Known as model distillation, the method uses the outputs of a ‌powerful AI system to train a smaller model that can perform some of the same tasks with fewer computing resources. The largest AI models, known as frontier models, require enormous amounts of computing power, data and investment to train. Model distillation offers a way to create smaller systems by using a large "teacher" model to train a smaller "student" model. Recent AI systems have increased interest in transferring ​not only final answers but also the steps used to reach ‌them. These "reasoning traces" can show a smaller model how to approach a difficult problem rather than simply what answer to produce.