README.md

---
license: apache-2.0
language:
  - en
base_model:
  - Qwen/Qwen3-4B
library_name: transformers
pipeline_tag: text-generation
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


## 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.