5 Conclusion

本文为论文《Search-o1：让推理模型学会边思考边检索》「论文原文」板块的对应章节，按 arXiv 官方全文逐字保留，层级与「中文全文翻译」板块一一对应。

In this work, we present Search-o1, a framework that addresses the knowledge insufficiency inherent in large reasoning models (LRMs) by integrating an agentic retrieval-augmented generation mechanism alongside a Reason-in-Documents module. Our approach enables LRMs to autonomously retrieve and seamlessly incorporate external knowledge during the reasoning process, thereby enhancing both the accuracy and coherence of their long-step reasoning capabilities. Comprehensive experiments across diverse complex reasoning tasks in science, mathematics, and coding, as well as multiple open-domain QA benchmarks, demonstrate that Search-o1 consistently outperforms existing retrieval-augmented and direct reasoning methods. Notably, Search-o1 not only surpasses baseline models in handling intricate reasoning challenges but also achieves performance levels comparable to or exceeding human experts in specific domains. These findings underscore the potential of Search-o1 to significantly improve the reliability and versatility of LRMs, paving the way for more trustworthy and effective intelligent systems in complex problem-solving scenarios.