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

• URL: http://arxiv.org/abs/2512.19081v1
• Date: Mon, 22 Dec 2025 06:42:46 GMT
• Title: Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning
• Authors: Yanzhi Zhang, Yitong Duan, Zhaoxi Zhang, Jiyan He, Shuxin Zheng,
• Abstract summary: Population-Evolve is a training-free method inspired by Genetic Algorithms to optimize Large Language Models reasoning.<n>We establish a unification framework that interprets existing test-time scaling strategies through the lens of genetic algorithms.
• Score: 10.610329942727699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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