Reasoning traces are more effective for training when customized to the student model's capabilities and selected based on how well they help recover answers, not just whether they're correct.
This paper presents ReDiST, a method for creating better reasoning traces to teach AI models. Instead of just showing correct answers, it constructs traces tailored to what a specific student model can learn—revising correct attempts and fixing wrong ones using evidence.