构建复杂深度研究任务数据集，让大模型真正学会分步推理与证据整合。
Open Data Synthesis For Deep Research
- 用双智能体递归生成研究树，将网页信息转为需多层推理的问题
- 产出超5万条训练样本，在浏览类任务中3B模型超越32B大模型
- 保留中间步骤和检索标签，支持高级优化策略，适合研究型AI开发
大型语言模型正被期待从简单问答转向需要分解问题、多步推理和跨源证据整合的深度研究任务。本文将此类可验证答案的任务形式化为层次化约束满足问题（HCSP），区别于传统单约束或多跳框架。现有基准（如Natural Questions、HotpotQA）难以捕捉其复杂性，而合成数据常存在捷径推理或知识泄露。为此，我们提出InfoSeek——一个可扩展的复杂深度研究任务生成框架。该框架通过双智能体系统从大规模网页递归构建研究树，将中间节点模糊化为有效子问题，并转化为需遍历完整层级的自然语言问题。其可快速扩展，生成超过5万条训练样本、精炼测试集及通过拒绝采样生成的推理轨迹。实验表明，基于InfoSeek训练的模型持续优于强基线：在挑战性基准BrowseComp-Plus上，3B模型性能超越更大规模的32B模型和轻量级商业API（如Gemini2.5-Flash），并接近更强API（如Gemini2.5-Pro）水平。通过保留中间步骤和检索标签，InfoSeek还支持复合奖励设计与轨迹级探索等先进优化策略。代码与数据已开源。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly expected to go beyond simple factual queries toward Deep Research-tasks that require decomposing questions into sub-problems, coordinating multi-step reasoning, and synthesizing evidence from diverse sources. We formalize Deep Research tasks with verifiable answers as Hierarchical Constraint Satisfaction Problems (HCSPs), which are fundamentally different from single-constraint, multi-hop, or flat CSP formulations. However, existing benchmarks (e.g., Natural Questions, HotpotQA) fail to capture this complexity, while recent synthetic datasets often introduce shortcut reasoning, knowledge leakage, or lack sufficient structural depth. To address this gap, we introduce InfoSeek, a scalable framework for synthesizing complex Deep Research tasks. InfoSeek uses a dual-agent system to recursively build a Research Tree from large-scale webpages, blurring intermediate nodes into valid sub-problems, and converting these trees into natural language questions that require traversing the full hierarchy. It also enables rapid scaling, yielding over 50K training examples, a curated test set, and reasoning trajectories generated via reject sampling. Experiments show that models trained on InfoSeek consistently outperform strong baselines. On a challenging benchmark BrowseComp-Plus, 3B LLMs optimized with InfoSeek surpass much larger 32B models and lightweight commercial APIs (e.g., Gemini2.5-Flash), while achieving performance comparable to stronger APIs (e.g., Gemini2.5-Pro). By preserving meta-information such as intermediate steps and retrieval labels, InfoSeek further supports advanced optimization strategies, including compound reward design and trajectory-level exploration. We provide our codes and datasets in \href{https://github.com/VectorSpaceLab/InfoSeek}{this repository}.
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