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About this paper
Qibin Wang, Pu Zhao, Shaohan Huang, Fangkai Yang, Lu Wang, Furu Wei, Qingwei Lin, Saravan Rajmohan and 1 more
Microsoft researchers introduce Generative Self-Refinement (GSR), a framework designed to explicitly teach large language models the skill of self-refinement through hybrid training. This method allows models to synthesize accurate solutions even when all initial candidate responses are incorrect, enhancing mathematical reasoning performance and robustness.
Key insights
- The 'Refinement Gap' metric demonstrates that parallel self-refinement consistently improves performance beyond base capabilities, and this refinement ability exhibits a clear scaling trend with model size but is only weakly correlated with a model's base reasoning skill.
- Self-refinement is a learnable and transferable inference strategy that can be explicitly activated through training, rather than being solely an emergent property of large models.
- Joint optimization of direct-solving and refinement skills within a single model is essential for achieving optimal performance, as both high-quality candidates and effective synthesis capabilities are required.
Summary by alphaXiv.
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