58 lines
2.7 KiB
Markdown
58 lines
2.7 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen3-4B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- arxiv:2602.04634
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metrics:
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- accuracy
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model-index:
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- name: WideSeek-R1-4B
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results:
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- task:
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type: WideSearch
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dataset:
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type: WideSearch
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name: WideSearch
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metrics:
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- type: accuracy
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value: 40.0
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---
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# WideSeek-R1-4B
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<div align="center">
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[**🌐 Project Page**](https://wideseek-r1.github.io/) | [**📄 Paper**](https://arxiv.org/abs/2602.04634) | [**📖 Doc**](https://rlinf.readthedocs.io/en/latest/rst_source/examples/agentic/wideseek_r1/index.html) | [**💻 Code**](https://github.com/RLinf/RLinf/tree/main/examples/agent/wideseek_r1) | [**📦 Dataset**](https://huggingface.co/datasets/RLinf/WideSeek-R1-train-data) | [**🤗 Models**](https://huggingface.co/RLinf/WideSeek-R1-4b)
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</div>
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## Overview
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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.
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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.
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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.
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For more details, see our [project page](https://thu-nics.github.io/WideSeek-R1/)
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## Citation
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If you use this model in your research, please cite our paper:
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```bibtex
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@article{xu2026wideseek,
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title = {WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning},
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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},
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journal = {arXiv preprint arXiv:2602.04634},
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year = {2026},
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}
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```
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