ScRPO: From Errors to Insights

• URL: http://arxiv.org/abs/2511.06065v2
• Date: Wed, 12 Nov 2025 01:27:03 GMT
• Title: ScRPO: From Errors to Insights
• Authors: Lianrui Li, Dakuan Lu, Jiawei Shao, Chi Zhang, Xuelong Li,
• Abstract summary: We propose Self-correction Relative Policy Optimization (ScRPO) to enhance large language models on challenging mathematical problems.<n>Our approach consists of two stages: trial-and-error learning stage and self-correction learning stage.<n>Extensive experiments across multiple math reasoning benchmarks, including AIME, AMC, Olympiad, MATH-500, GSM8k, using Deepseek-Distill-Qwen-1.5B and Deepseek-Distill-Qwen-7B.
• Score: 47.828888776503675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Self-correction Relative Policy Optimization (ScRPO), a novel reinforcement learning framework designed to enhance large language models on challenging mathematical problems by leveraging self-reflection and error correction. Our approach consists of two stages: (1) Trial-and-error learning stage: training the model with GRPO and collecting incorrect answers along with their corresponding questions in an error pool; (2) Self-correction learning stage: guiding the model to reflect on why its previous answers were wrong. Extensive experiments across multiple math reasoning benchmarks, including AIME, AMC, Olympiad, MATH-500, GSM8k, using Deepseek-Distill-Qwen-1.5B and Deepseek-Distill-Qwen-7B. The experimental results demonstrate that ScRPO consistently outperforms several post-training methods. These findings highlight ScRPO as a promising paradigm for enabling language models to self-improve on difficult tasks with limited external feedback, paving the way toward more reliable and capable AI systems.

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