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arXiv · 2512.19081

Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning

Yanzhi Zhang·Yitong Duan·Zhaoxi Zhang·Jiyan He·Shuxin Zheng
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Abstract

Test-time scaling has emerged as a promising direction for enhancing the reasoning capabilities of Large Language Models in last few years. In this work, we propose Population-Evolve, a training-free method inspired by Genetic Algorithms to optimize LLM reasoning. Our approach maintains a dynamic population of candidate solutions for each problem via parallel reasoning. By incorporating an evolve prompt, the LLM self-evolves its population in all iterations. Upon convergence, the final answer is derived via majority voting. Furthermore, we establish a unification framework that interprets existing test-time scaling strategies through the lens of genetic algorithms. Empirical results demonstrate that Population-Evolve achieves superior accuracy with low performance variance and computational efficiency. Our findings highlight the potential of evolutionary strategies to unlock the reasoning power of LLMs during inference.

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Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang, Jiyan He, Shuxin Zheng. 2025-12-22. Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning. https://arxiv.org/abs/2512.19081

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