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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
library_name: transformers
pipeline_tag: text-generation
tags:
- arxiv:2602.04634
metrics:
- accuracy
model-index:
- name: WideSeek-R1-4B
results:
- task:
type: WideSearch
dataset:
type: WideSearch
name: WideSearch
metrics:
- type: accuracy
value: 40.0
---
# WideSeek-R1-4B
<div align="center">
[**🌐 Project Page**](https://wideseek-r1.github.io/) | [**📄 Paper**](https://arxiv.org/pdf/2602.04634) | [**💻 Code**](https://github.com/RLinf/RLinf/tree/main/examples/wideseek_r1) | [**📦 Dataset**](https://huggingface.co/datasets/RLinf/WideSeek-R1-train-data) | [**🤗 Models**](https://huggingface.co/RLinf/WideSeek-R1-4b)
</div>
## Overview
Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability.
In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks.
Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0\% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.
For more details, see our [project page](https://thu-nics.github.io/WideSeek-R1/)
## Citation
If you use this model in your research, please cite our paper:
```bibtex
@article{xu2026wideseek,
title = {WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning},
author = {Xu, Zelai and Xu, Zhexuan and Zhang, Ruize and Zhu, Chunyang and Yu, Shi and Liu, Weilin and Zhang, Quanlu and Ding, Wenbo and Yu, Chao and Wang, Yu},
journal = {arXiv preprint arXiv:2602.04634},
year = {2026},
}
```
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