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Model: RLinf/WideSeek-R1-4b
Source: Original Platform

README.md

@@ -0,0 +1,57 @@
+---
+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/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)
+
+</div>
+
+## Overview
+
+![image](fig/scaling.png)
+
+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},
+}
+```