arXiv 2412.077792024-11-24cs.NEcs.AI

思维进化：通过多目标优化实现多样化高质量推理

Evolution of Thought: Diverse and High-Quality Reasoning via Multi-Objective Optimization

Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Mathematics, Harbin Institute of Technology（哈尔滨工业大学数学学院）；Department of Control Science and Engineering, Harbin Institute of Technology（哈尔滨工业大学控制科学与工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）

Biqing Qi, Zhouyi Qian, Yiang Luo, Junqi Gao, Dong Li, Kaiyan Zhang, Bowen Zhou

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AI总结：

针对多模态大语言模型推理路径多样性不足、信息共享低效的问题，提出融合非支配排序遗传算法II与凝聚-聚合机制的多目标框架EoT，在多项推理任务上实现了更优的性能与效率，为启发式推理框架设计提供新思路。

摘要：

随着多模态大语言模型（MLLMs）日益广泛地应用于复杂推理任务，推理路径的多样性与质量成为影响其性能的关键因素。现有方法虽试图通过路径扩展提升推理质量，却往往忽视推理路径的多样性及有效的信息共享，导致陷入局部最优且效率低下。为应对这些挑战，我们提出思维进化（Evolution of Thought, EoT）——一个旨在通过培育高质量且多样化的推理路径来提升推理能力的多目标框架。具体而言，我们引入用于多目标优化的非支配排序遗传算法II（Non-dominated Sorting Genetic Algorithm II），利用交叉和变异算子提升推理解的多样性。此外，我们提出凝聚-聚合（Condensation-Aggregation）机制，对冗余路径进行聚类与消除，促进父节点间更高效的信息共享，最终提升推理过程的效率与质量。在多项视觉-语言与语言推理任务上的验证实验表明，与其他有竞争力的基线方法相比，EoT取得了更优的推理性能与效率。本研究为MLLMs启发式推理框架的设计提供了全新视角。

英文摘要：

As multi-modal large language models (MLLMs) are increasingly applied to complex reasoning tasks, the diversity and quality of reasoning paths become crucial factors affecting their performance. Although current methods aim to enhance reasoning quality through path expansion, they often neglect the diversity of reasoning paths and effective information sharing, leading to local optima and inefficiency. To address these challenges, we propose Evolution of Thought (EoT), a multi-objective framework designed to improve reasoning by fostering both high-quality and diverse reasoning paths. Specifically, we introduce the Non-dominated Sorting Genetic Algorithm II for multi-objective optimization, utilizing crossover and mutation operators to promote greater diversity in reasoning solutions. Additionally, we propose a Condensation-Aggregation mechanism to cluster and eliminate redundant paths, facilitate improved information sharing among parent nodes, and ultimately enhance both the efficiency and quality of the reasoning process. Validation experiments on various vision-language and language reasoning tasks demonstrate that EoT achieves superior reasoning performance and efficiency compared to other competitive baselines. Our study provides a novel perspective on the design of heuristic reasoning frameworks for MLLMs.

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