Evolutionary System 2 Reasoning: An Empirical Proof
Zeyuan Ma, Wenqi Huang, Guo-Huan Song, Hongshu Guo, Sijie Ma, Zhiguang Cao, Yue-Jiao Gong

TL;DR
This paper introduces an evolutionary framework to enhance reasoning abilities in large language models, demonstrating that even weaker models can develop strong reasoning skills through evolutionary optimization.
Contribution
The paper proposes the ERO framework that evolves LLMs to improve their reasoning ability, showing that simple evolutionary strategies can significantly enhance weaker models.
Findings
GPT-5 shows limited reasoning ability
Weak models can be evolved to strong reasoners
Evolutionary optimization improves reasoning performance
Abstract
Machine intelligence marks the ultimate dream of making machines' intelligence comparable to human beings. While recent progress in Large Language Models (LLMs) show substantial specific skills for a wide array of downstream tasks, they more or less fall shorts in general intelligence. Following correlation between intelligence and system 2 reasoning (slow thinking), in this paper, we aim to answering a worthwhile research question: could machine intelligence such as LLMs be evolved to acquire reasoning ability (not specific skill) just like our human beings? To this end, we propose evolutionary reasoning optimization (ERO) framework which performs survival of the fittest over a population of LLMs to search for individual with strong reasoning ability. Given a reasoning task, ERO first initializes multiple LLMs as a population, after which an evolutionary strategy evolves the population…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Language and cultural evolution
