1 Introduction

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。

Recently emerged large reasoning models (LRMs), exemplified by OpenAI’s o1 [22], Qwen-QwQ [54] and DeepSeek-R1 [7], employ large-scale reinforcement learning foster impressive long-sequence stepwise reasoning capabilities, offering promising solutions to complex reasoning problems [46, 31, 59, 84, 73, 74, 67]. This advancement has inspired a series of foundational efforts aimed at exploring and reproducing o1-like reasoning patterns, to broaden their application to a wider range of foundational models [49, 19, 77, 80, 71, 25, 45].

It is noteworthy that o1-like reasoning patterns guide LRMs to engage in a slower thinking process [6, 61] by implicitly breaking down complex problems, generating a long internal reasoning chain and then discovering suitable solutions step by step. While this characteristic enhances logical coherence and interpretability of reasoning, an extended chain of thought may cause overthinking [4] and increased risks of knowledge insufficiency [60, 51, 2], where any knowledge gap can propagate errors and disrupt the entire reasoning chain [79, 40, 44, 41].

To address this limitation, we conduct preliminary experiments to assess the frequency of uncertain words decoded by the LRMs due to knowledge gaps. As shown in Figure 1, the extended thinking process leads LRM to frequently decode numerous uncertain terms in challenging reasoning problems, with “perhaps” averaging over 30 occurrences in each reasoning process. Notably, the high specialization of these problems also complicates manual reasoning verification, often incurring significant costs [63]. Consequently, automating the supplementation of knowledge required for the o1-like reasoning process has become a significant challenge, limiting the progress of LRMs in achieving universally trustworthy reasoning.

To shed light on this topic, our core motivation is to enhance the LRMs with o1-like reasoning pattern through autonomous retrieval. We propose Search-o1, which integrates the reasoning process of LRMs with two core components: an agentic retrieval-augmented generation (RAG) mechanism and a knowledge refinement module. This design aims to enable LRMs to incorporate the agentic search workflow into the reasoning process, retrieving external knowledge on demand to support step-wise reasoning while preserving coherence throughout.

Specifically, our results in Figure 1 reveal that traditional problem-oriented RAG techniques do not effectively address the knowledge gaps compared to direct reasoning (Standard RAG vs. Direct Reasoning). This finding aligns with human intuition, as standard RAG retrieves relevant knowledge only once in a problem-oriented manner, while the knowledge required for each step in complex reasoning scenarios is often varied and diverse [83, 41, 11]. Unlike them, Search-o1 employs an agentic RAG technique that guides the model to actively decode search queries when facing knowledge shortages, thereby triggering the retrieval mechanism to obtain relevant external knowledge. Owing to the benefits of this design, our retrieval mechanism can be triggered and iterated multiple times within a single reasoning session to fulfill the knowledge needs of various reasoning steps.

To effectively integrate retrieved knowledge into the LRM’s reasoning process, we further identify two key challenges when directly incorporating retrieved documents into the reasoning chain during practical experiments: (1) Redundant Information in Retrieved Documents. Retrieved documents are often lengthy and contain redundant information, directly inputting them into LRMs may disrupt the original coherence of reasoning and even introduce noise [62, 72, 26]. (2) Limited Ability to Understand Long Documents. Most LRMs have been specifically aligned for complex reasoning tasks during the pre-training and fine-tuning stages. This focus has resulted in a degree of catastrophic forgetting in their general capabilities [39, 10], ultimately limiting their long-context understanding of retrieved documents.

To address these challenges, we introduce the Reason-in-Documents module, which operates independently from the main reasoning chain. This module first conducts a thorough analysis of retrieved documents based on both the current search query and previous reasoning steps, and then produces refined information that seamlessly integrates with the prior reasoning chain.

Figure 1: Analysis of reasoning uncertainty with QwQ-32B-Preview. Left: Examples of uncertain words identified during the reasoning process. Right: Average occurrence of high-frequency uncertain words per output in the GPQA diamond set.

In summary, our contributions are as follows:

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We propose Search-o1, the first framework that integrates the agentic search workflow into the o1-like reasoning process of LRM for achieving autonomous knowledge supplementation.

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To effectively integrate external knowledge during reasoning, Search-o1 combines the reasoning process with an agentic RAG mechanism and a knowledge refinement module. This design enables the LRM to retrieve external knowledge on demand, seamlessly incorporating it into the reasoning chain while maintaining the original logical flow.

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With five complex reasoning domains and six open-domain QA benchmarks, we demonstrate that Search-o1 achieves remarkable performance in the reasoning field while maintaining substantial improvements in the general knowledge. Further quantitative analysis confirms its efficiency and scalability, offering practical guidance for trustworthy reasoning in LRMs.