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To overcome these limitations, we propose **SuperCorrect**, a novel two-stage framework that uses a large teacher model to *supervise* and *correct* both the reasoning and reflection processes of a smaller student model. In the first stage, we extract hierarchical high-level and detailed thought templates from the teacher model to guide the student model in eliciting more fine-grained reasoning thoughts. In the second stage, we introduce cross-model collaborative direct preference optimization (DPO) to enhance the self-correction abilities of the student model by following the teacher's correction traces during training. This cross-model DPO approach teaches the student model to effectively locate and resolve erroneous thoughts with error-driven insights from the teacher model, breaking the bottleneck of its thoughts and acquiring new skills and knowledge to tackle challenging problems.
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Extensive experiments consistently demonstrate our superiority over previous methods. Notably, our **SuperCorrect-7B** model significantly **surpasses powerful DeepSeekMath-7B by 7.8%/5.3% and Qwen2.5-Math-7B by 15.1%/6.3%** on MATH/GSM8K benchmarks, achieving new SOTA performance among all 7B models.
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## Introduction
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To overcome these limitations, we propose **SuperCorrect**, a novel two-stage framework that uses a large teacher model to *supervise* and *correct* both the reasoning and reflection processes of a smaller student model. In the first stage, we extract hierarchical high-level and detailed thought templates from the teacher model to guide the student model in eliciting more fine-grained reasoning thoughts. In the second stage, we introduce cross-model collaborative direct preference optimization (DPO) to enhance the self-correction abilities of the student model by following the teacher's correction traces during training. This cross-model DPO approach teaches the student model to effectively locate and resolve erroneous thoughts with error-driven insights from the teacher model, breaking the bottleneck of its thoughts and acquiring new skills and knowledge to tackle challenging problems.
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Extensive experiments consistently demonstrate our superiority over previous methods. Notably, our **SuperCorrect-7B** model significantly **surpasses powerful DeepSeekMath-7B by 7.8%/5.3% and Qwen2.5-Math-7B by 15.1%/6.3%** on MATH/GSM8K benchmarks, achieving new SOTA performance among all 7B models.
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</details>
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## Introduction
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