S imple D eep S earcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025)

Abstract: Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. However, existing approaches face critical limitations that lack high-quality training trajectories or suffer from the distributional mismatches in simulated environments and prohibitive computational costs for real-world deployment. This paper introduces SimpleDeepSearcher, a lightweight yet effective framework that bridges this gap through strategic data engineering rather than complex training paradigms. Our approach synthesizes high-quality training data by simulating realistic user interactions in live web search environments, coupled with a multi-criteria curation strategy that optimizes the diversity and quality of input and output side. Experiments on five benchmarks across diverse domains demonstrate that SFT on only 871 curated samples yields significant improvements over RL-based baselines. Our work establishes SFT as a viable pathway by systematically addressing the data-scarce bottleneck, offering practical insights for efficient deep search systems. Our anonymous code is available at https://github.com/RUCAIBox/SimpleDeepSearcher

## introduction in recent years, retrieval-augmented generation (rag) methods have significantly enhanced llms by incorporating external knowledge retrieval mechanisms @xcite @xcite . recent advancements have extended these capabilities to complex deep search scenarios that demand multi-step reasoning with iterative information retrieval and synthesis @xcite . traditional rag systems typically treat retrieval as an external auxiliary * equal contributions. to address the complex reasoning demands in deep search scenarios, early research explored prompt-based strategies that guide models to decompose questions, generate queries, and retrieve information iteratively @xcite @xcite . other studies have attempted to improve model performance through supervised fine-tuning (sft) @xcite ; however, there is currently a lack of high-quality trajectory data of reasoning and search interactions for training @xcite . to further enhance the model's autonomous search capabilities, reinforcement learning (rl) @xcite is considered as a promising solution to train models through real-time interaction with the environment @xcite @xcite . however, most rl-based approaches operate within artificial environments using static document corpora, creating a distributional mismatch with real-world web dynamics. moreover, the inherent computational intensity of rl training escalates exponentially when interfacing with live search apis @xcite . given the overhead and complexity of rl-based training, we hypothesize that sft remains a viable pathway for building efficient deep search systems. while sft offers a streamlined training process, it faces the critical challenge of lacking high-quality training data in deep search scenarios. on the one hand, existing qa datasets often lack the diversity and complexity of questions and search-oriented purposes on the web, which are essential for deep search training. on the other hand, traditional answer annotations omit the crit-ical reasoning traces (search operations, evidence synthesis, and efficient decision paths) required for teaching search-integrated reasoning strategies. in this paper, we propose simpledeepsearcher, an efficient search-with-think framework that utilizes strategic data engineering rather than complex training paradigms. our core methodology centers on a three-fold process for constructing high-quality training data. first, we develop a data synthesis framework grounded in real web search environments, simulating realistic user search behaviors to generate multi-turn reasoning trajectories. second, we propose a diversity-aware query sampling strategy to optimize domain coverage, semantic complexity, and knowledge unit density. moreover, we adopt a four-dimensional response curation that enforces format standardization, reasoning efficiency, question difficulty, and search effectiveness. by systematically addressing both query and response-side quality through automated pipelines, simpledeepsearcher can obtain highquality supervised signals based on real web search for complex reasoning to facilitate the sft process. experimental results show that our sft method significantly boosts model performance on five representative benchmarks with only 871 high-quality training samples. compared to prompt-based methods, simpledeepsearcher achieves a 48.3% improvement, and compared to rl-based rag methods achieves a 24.9% improvement. this demonstrates that our framework effectively balances performance and efficiency, providing a simple yet powerful approach to enhancing deep search capabilities. furthermore, our framework is highly extensible that can be combined with other types of training data, the framework is also applicable to rl-based training. our main contributions are as follows: ## method in this section, we propose simpledeepsearcher for complex deep search tasks by leveraging multistage data construction strategies. ## further analysis 4.1 ablation study to validate the effectiveness of the proposed simpledeepsearcher, we conduct a comprehensive ablation analysis using qwen2.5-7b-instruct on the bamboogle and gaia datasets. we conduct detailed ablation studies on three main aspects: (1) query sampling: w/o dd removes domain diversity filter, w/o kd removes keyword diversity ## conclusion in this work, we present simpledeepsearcher, a lightweight yet effective framework for deepsearch tasks, addressing the limitations of existing rag methods that rely heavily on complex training paradigms or suffer from distributional mismatches. by leveraging realistic web search simulations and a multi-criteria data curation strategy, we construct high-quality training trajectories that enable efficient supervised fine-tuning. despite using only 871 curated samples, our method achieves substantial gains over rl-based baselines across diverse in-domain and out-of-domain benchmarks. our results highlight the potential of strategic data engineering to empower deep search reasoning.