README.md

---
base_model:
- nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16
datasets:
- OpenResearcher/OpenResearcher-Dataset
library_name: transformers
license: mit
pipeline_tag: text-generation
---

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  🤗 <a href="https://huggingface.co/collections/TIGER-Lab/openresearcher" target="_blank">HuggingFace</a> ｜
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## OpenResearcher-30B-A3B Overview
OpenResearcher-30B-A3B is an agentic large language model designed for long-horizon deep research, presented in the paper [OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis](https://huggingface.co/papers/2603.20278).

It is fine-tuned from [NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16) on 96K [OpenResearcher dataset](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset) with **100+** turns. The dataset is derived by distilling GPT-OSS-120B with [native browser tools](https://docs.vllm.ai/projects/recipes/en/latest/OpenAI/GPT-OSS.html#usage:~:text=Limitation%20section%20below.-,Tool%20Use,-%C2%B6). More info can be found on the dataset card at [OpenResearcher dataset](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset).

The model achieves an impressive **54.8%** accuracy on [BrowseComp-Plus](https://huggingface.co/spaces/Tevatron/BrowseComp-Plus), surpassing performance of `GPT-4.1`, `Claude-Opus-4`, `Gemini-2.5-Pro`, `DeepSeek-R1` and `Tongyi-DeepResearch`.
<div align="center">
  <img src="imgs/teaser.png" alt="OpenResearcher Teaser" width="100%" style="max-width: 850px; border-radius: 8px; box-shadow: 0 4px 10px rgba(0,0,0,0.1);">
</div>

## Deep Research Benchmark Results

<div align="center">
  <img src="https://raw.githubusercontent.com/TIGER-AI-Lab/OpenResearcher/main/assets/imgs/main_table.png" alt="Deep Research Benchmark Results" width="100%">
</div>

## Evaluate OpenResearcher-30B-A3B
We evaluate OpenResearcher-30B-A3B across a range of deep research benchmarks, including BrowseComp-Plus, BrowseComp, GAIA, xbench-DeepSearch. Please find more details in [GitHub](https://github.com/TIGER-AI-Lab/OpenResearcher?tab=readme-ov-file#-benchmark-openresearcher).


## Sample Usage

The following example demonstrates how to use `OpenResearcher-30B-A3B` for deep research within its agentic environment. This requires the tools and environment setup provided in the [official GitHub repository](https://github.com/TIGER-AI-Lab/OpenResearcher).

```python
import asyncio
from deploy_agent import run_one, BrowserPool
from utils.openai_generator import OpenAIAsyncGenerator

async def main():
    # Initialize generator and browser
    generator = OpenAIAsyncGenerator(
        base_url="http://localhost:8001/v1",
        model_name="OpenResearcher/OpenResearcher-30B-A3B",
        use_native_tools=True
    )
    browser_pool = BrowserPool(search_url=None, browser_backend="serper")

    # Run deep research
    await run_one(
        question="What is the latest news about OpenAI?",
        qid="quick_start",
        generator=generator,
        browser_pool=browser_pool,
    )

    browser_pool.cleanup("quick_start")

if __name__ == "__main__":
    asyncio.run(main())
```

## Citation 
```bibtex
@article{li2026openresearcher,
  title={{OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}},
  author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Yu Zhang and Wenhu Chen},
  journal={arXiv preprint arXiv:2603.20278},
  year={2026}
}
```