Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911331890757632 |
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| author | Zhang, Yanzhi Duan, Yitong Zhang, Zhaoxi He, Jiyan Zheng, Shuxin |
| author_facet | Zhang, Yanzhi Duan, Yitong Zhang, Zhaoxi He, Jiyan Zheng, Shuxin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning Zhang, Yanzhi Duan, Yitong Zhang, Zhaoxi He, Jiyan Zheng, Shuxin Artificial Intelligence 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. |
| title | Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.19081 |