Multi-Layer GRPO: Enhancing Reasoning and Self-Correction in Large Language Models

• URL: http://arxiv.org/abs/2506.04746v1
• Date: Thu, 05 Jun 2025 08:27:34 GMT
• Title: Multi-Layer GRPO: Enhancing Reasoning and Self-Correction in Large Language Models
• Authors: Fei Ding, Baiqiao Wang, Zijian Zeng, Youwei Wang,
• Abstract summary: We propose MGRPO (Multi-layer GRPO) to foster reasoning and self-correction abilities.<n>MGRPO significantly outperforms standard GRPO, achieving superior performance by fostering both reasoning and self-correction abilities.
• Score: 3.0763741715155666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Group Relative Policy Optimization (GRPO) algorithm has demonstrated considerable success in enhancing the reasoning capabilities of large language models (LLMs), as evidenced by DeepSeek-R1. However, the absence of intermediate supervision in GRPO frequently leads to inefficient exploration dynamics. A single error in a complex reasoning chain can invalidate the entire solution, resulting in abrupt reward vanishing and compromising training stability.To address these challenges, we propose MGRPO (Multi-layer GRPO). MGRPO operates in two layers: the first layer employs standard GRPO to generate an initial response. This response, along with the original query, is then fed into a second-layer GRPO process. This second layer is specifically trained to identify and correct errors in the initial response, effectively creating a self-correction loop. This mechanism provides implicit process-level supervision by rewarding successful error correction, without requiring an explicit, densely-annotated reward model. Experimental results on several mathematical reasoning benchmarks demonstrate that MGRPO significantly outperforms standard GRPO, achieving superior performance by fostering both reasoning and self-correction abilities.

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