Critique model

To train critique models capable of delivering step-level supervision and constructive feedback for reasoning, we introduce AutoMathCritique—an automated and scalable framework for collecting critique data.
This framework consists of three main stages: flawed reasoning path construction, critique generation, and data filtering. Using AutoMathCritique, we create a dataset containing $76,321$ samples named MathCritique-76k.