| 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}, | |
| } | |
| ``` | |