SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909831243235328 |
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| author | Sun, Shuang Song, Huatong Wang, Yuhao Ren, Ruiyang Jiang, Jinhao Zhang, Junjie Bai, Fei Deng, Jia Zhao, Wayne Xin Liu, Zheng Fang, Lei Wang, Zhongyuan Wen, Ji-Rong |
| author_facet | Sun, Shuang Song, Huatong Wang, Yuhao Ren, Ruiyang Jiang, Jinhao Zhang, Junjie Bai, Fei Deng, Jia Zhao, Wayne Xin Liu, Zheng Fang, Lei Wang, Zhongyuan Wen, Ji-Rong |
| contents | 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 code is available at https://github.com/RUCAIBox/SimpleDeepSearcher. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16834 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis Sun, Shuang Song, Huatong Wang, Yuhao Ren, Ruiyang Jiang, Jinhao Zhang, Junjie Bai, Fei Deng, Jia Zhao, Wayne Xin Liu, Zheng Fang, Lei Wang, Zhongyuan Wen, Ji-Rong Computation and Language Artificial Intelligence Information Retrieval 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 code is available at https://github.com/RUCAIBox/SimpleDeepSearcher. |
| title | SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2505.16834 |