DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent

TL;DR

This paper introduces DeepResearch-9K, a large-scale challenging dataset for deep-research agents, along with an open-source training framework DeepResearch-R1, to advance multi-step web exploration and question answering capabilities.

Contribution

The paper presents a novel large-scale dataset and an open-source training framework specifically designed for deep-research agents, addressing key bottlenecks in data and tools.

Findings

Agents trained on DeepResearch-9K achieve state-of-the-art results.

DeepResearch-R1 supports multi-turn web interactions and various reinforcement learning approaches.

The dataset includes high-quality search trajectories and verifiable answers.

Abstract

Deep-research agents are capable of executing multi-step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, deep-research agents face two critical bottlenecks: (1) the lack of large-scale, challenging datasets with real-world difficulty, and (2) the absence of accessible, open-source frameworks for data synthesis and agent training. To bridge these gaps, we first construct DeepResearch-9K, a large-scale challenging dataset specifically designed for deep-research scenarios built from open-source multi-hop question-answering (QA) datasets via a low-cost autonomous pipeline. Notably, it consists of (1) 9000 questions spanning three difficulty levels from L1 to L3 (2) high-quality search trajectories with reasoning chains from Tongyi-DeepResearch-30B-A3B, a state-of-the-art deep-research agent, and (3) verifiable answers. Furthermore, we develop an open-source training framework DeepResearch-R1 that supports (1) multi-turn web interactions, (2) different reinforcement learning (RL) approaches, and (3) different reward models such as rule-based outcome reward and LLM-as-judge feedback. Finally, empirical results demonstrate that agents trained on DeepResearch-9K under our DeepResearch-R1 achieve state-of-the-art results on challenging deep-research benchmarks. We release the DeepResearch-9K dataset on https://huggingface.co/datasets/artillerywu/DeepResearch-9K and the code of DeepResearch-R1 on https://github.com/Applied-Machine-Learning-Lab/DeepResearch-R1.