README.md

---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
- llama-factory
- full
- generated_from_trainer
datasets:
- rl-research/dr-tulu-sft-data
---

> [!NOTE]
> For full information, go check out the Dr Tulu paper [here](http://allenai-web/papers/drtulu).

<img src="https://huggingface.co/rl-research/DR-Tulu-SFT-8B/resolve/main/dr_tulu_logo.png" alt="Figure 1" width="500"/>


# DR Tulu SFT 8B

This is the SFT checkpoint of DR Tulu, an open deep research agent trained on top of [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).

This model has undergone SFT training on [this dataset](https://huggingface.co/datasets/rl-research/dr-tulu-sft-data).
For more details on DR Tulu please **read our [paper](http://allenai-web/papers/drtulu)**!


# Inference and Usage

**This model has been trained for tool-use using the dr-agent-lib framework**.
As such, running it out of the box with HuggingFace or vLLM will not work well!

See the [our github](https://github.com/rlresearch/dr-tulu) for more details on installation and how to run our model.
Or check out our [demo](https://dr-tulu.github.io/)!

# Evaluation Results

We provide evaluation instructions in [our github](https://github.com/rlresearch/dr-tulu)).


| Benchmark | SQAv2 | HealthBench | ResearchQA | DeepResearch Bench | SimpleQA | 2Wiki | WebWalker | Average |
|:----------|:------:|:----------:|:---------:|:-------------------:|:------:|:-------:|-------:|-------:|
| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (naive rag) | 40.4 | 16.5 | 56.1 | 33.3 | 52.6 | 18.9 | 8.8 | 32.4 |
| [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (our search pipeline) | 57.2 | 5.9 | 46.3 | 18.2 | 70.5 | 44.0 | 27.9 | 38.6 |
| [DR-Tulu-SFT-8B](https://huggingface.co/rl-research/DR-Tulu-SFT-8B) (**this model**) | 72.3 | 38.1 | 68.5 | 39.0 | 75.5 | 66.5 | 31.9 | 56.0 |
| [DR-Tulu-8B](https://huggingface.co/rl-research/DR-Tulu-8B) | **86.7** | **43.7** |  **71.1** | **41.8** | **80.1** | **68.0** | **39.1** | **61.5** |

For more baselines, explanations of this table, and analysis of results, check out the [Dr Tulu paper](http://allenai-web/papers/drtulu)!

# Intended uses & limitations

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use).

## Training

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

For futher details, check out the [Dr Tulu paper](http://allenai-web/papers/drtulu).

# Links
- 📝 [DR Tulu Paper](http://allenai-web/papers/drtulu)
- ⚙️ [DR Tulu demo](https://dr-tulu.github.io/)
- 💻 [DR Tulu code](https://github.com/rlresearch/DR-Tulu)
- 🤖 [DR Tulu collection](https://huggingface.co/collections/rl-research/dr-tulu)


# Citation
```
@article{drtulu,
  title = {{DR Tulu:  Reinforcement Learning with Evolving Rubrics for Deep Research}},
  author = {{Rulin Shao, Akari Asai, Shannon Shen, Hamish Ivison, Varsha Kishore, Jingming Zhuo, Xinran Zhao, Molly Park, Sam Finlayson, David Sontag, Tyler Murray, Sewon Min, Pradeep Dasigi, Luca Soldani, Faeze Brahman, Scott Yih, Sherry Tongshuang Wu, Luke Zettlemoyer, Yoon Kim, Hanna Hajishirzi, Pang Wei Koh}},
  year = {2025},
}
```