深研探索：为小型语言模型解锁长程信息检索与综合能力

Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs

September 28, 2025

作者: Shreyas Singh, Kunal Singh, Pradeep Moturi

cs.AI

摘要

工具集成推理已成为实现智能应用的关键焦点。其中，深度研究代理因其在复杂、开放式信息检索任务中的卓越表现而备受关注。我们推出了Fathom-DeepResearch，这是一个由两个专用模型组成的智能系统。首先是Fathom-Search-4B，这是一个基于Qwen3-4B训练的深度搜索模型，专为通过实时网络搜索和定向网页查询进行基于证据的调查而优化。其训练结合了三大创新：(i) DUETQA，一个通过多智能体自博弈生成的5K样本数据集，强制严格的网络搜索依赖性和异质来源锚定；(ii) RAPO，作为GRPO的零开销扩展，通过课程剪枝、奖励感知优势缩放和每提示重放缓冲区，稳定了带有可验证奖励的多轮强化学习；(iii) 可引导的步骤级奖励，按认知行为和边际效用对每次工具调用进行分类，实现对搜索轨迹广度、深度和范围的显式控制。这些改进使得在必要时工具调用可可靠地扩展至20次以上。其次是Fathom-Synthesizer-4B，同样基于Qwen3-4B训练，它将多轮深度搜索轨迹转化为结构化的、引用密集的深度研究报告，实现全面综合。在深度搜索基准（SimpleQA、FRAMES、WebWalker、Seal0、MuSiQue）和DeepResearch-Bench上的评估显示，该系统在开放权重类别中达到了最先进的性能，同时展示了对包括HLE、AIME-25、GPQA-Diamond和MedQA在内的多样化推理任务的强大泛化能力。

English

Tool-integrated reasoning has emerged as a key focus for enabling agentic applications. Among these, DeepResearch Agents have gained significant attention for their strong performance on complex, open-ended information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch model trained from Qwen3-4B and optimized for evidence-based investigation through live web search and targeted webpage querying. Its training combines three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent self-play that enforces strict web-search dependence and heterogeneous source grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes multi-turn Reinforcement Learning with Verifiable Rewards through curriculum pruning, reward-aware advantage scaling, and per-prompt replay buffers; and (iii) a steerable step-level reward that classifies each tool call by cognitive behavior and marginal utility, enabling explicit control over search trajectory breadth, depth, and horizon. These improvements enable reliable extension of tool-calling beyond 20 calls when warranted. The second is Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn DeepSearch traces into structured, citation-dense DeepResearch Reports for comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES, WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves state-of-the-art performance in the open-weights category while demonstrating strong generalization to diverse reasoning tasks including HLE, AIME-25, GPQA-Diamond, and MedQA.