提升大模型推理多样性，避免自我迭代导致思路单一。
DIVE: Diversified Iterative Self-Improvement
- 引入样本池扩展与数据选择机制，主动拓展解题路径。
- 在MATH和GSM8k上实现10%至45%的多样性提升，性能不降。
- 适合需要多角度推理的场景，如数学证明与复杂问题求解。
近期大型语言模型的进展表明，迭代自我改进（ISI）技术有效。然而，持续使用自生成数据训练会导致输出多样性下降，这一缺陷在推理任务中尤为关键，因多样解题路径至关重要。本文提出DIVE（多样化迭代自我改进）框架，通过两个核心组件解决此问题：样本池扩展以拓宽解题探索范围，数据选择以在偏好对中平衡多样性与质量。在MATH和GSM8k数据集上的实验显示，DIVE相比原始ISI实现了10%至45%的相对多样性提升，同时保持性能水平。消融实验证明两个组件均对改进效果至关重要。代码已公开于https://github.com/qinyiwei/DIVE。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have demonstrated the effectiveness of Iterative Self-Improvement (ISI) techniques. However, continuous training on self-generated data leads to reduced output diversity, a limitation particularly critical in reasoning tasks where diverse solution paths are essential. We present DIVE (Diversified Iterative Self-Improvement), a novel framework that addresses this challenge through two key components: Sample Pool Expansion for broader solution exploration, and Data Selection for balancing diversity and quality in preference pairs. Experiments on MATH and GSM8k datasets show that DIVE achieves a 10% to 45% relative increase in output diversity metrics while maintaining performance quality compared to vanilla ISI. Our ablation studies confirm both components' significance in achieving these improvements. Code is available at https://github.com/qinyiwei/DIVE.
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