87 lines
3.5 KiB
Markdown
87 lines
3.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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datasets:
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- rl-research/dr-tulu-sft-data
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---
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> [!NOTE]
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> For full information, go check out the Dr Tulu paper [here](https://arxiv.org/abs/2511.19399).
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<img src="https://huggingface.co/rl-research/DR-Tulu-SFT-8B/resolve/main/dr_tulu_logo.png" alt="Figure 1" width="500"/>
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# DR Tulu SFT 8B
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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).
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This model has undergone SFT training on [this dataset](https://huggingface.co/datasets/rl-research/dr-tulu-sft-data).
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For more details on DR Tulu please **read our [paper](https://allenai.org/papers/drtulu)**!
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# Inference and Usage
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**This model has been trained for tool-use using the dr-agent-lib framework**.
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As such, running it out of the box with HuggingFace or vLLM will not work well!
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See [our github](https://github.com/rlresearch/dr-tulu) for more details on installation and how to run our model.
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Or check out our [demo](https://dr-tulu.github.io/)!
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# Evaluation Results
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We provide evaluation instructions in [our github](https://github.com/rlresearch/dr-tulu).
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| Benchmark | SQAv2 | HealthBench | ResearchQA | DeepResearch Bench | SimpleQA | 2Wiki | WebWalker | Average |
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|:----------|:------:|:----------:|:---------:|:-------------------:|:------:|:-------:|-------:|-------:|
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| [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 |
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| [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 |
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| [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 |
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| [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** |
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For more baselines, explanations of this table, and analysis of results, check out the [Dr Tulu paper](https://allenai.org/papers/drtulu)!
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# Intended uses & limitations
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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).
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## Training
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The following hyperparameters were used during training:
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- learning_rate: 4e-05
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- train_batch_size: 1
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- total_eval_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 5
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For futher details, check out the [Dr Tulu paper](https://allenai.org/papers/drtulu).
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# Links
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- 📝 [DR Tulu Paper](https://allenai.org/papers/drtulu)
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- ⚙️ [DR Tulu demo](https://dr-tulu.github.io/)
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- 💻 [DR Tulu code](https://github.com/rlresearch/DR-Tulu)
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- 🤖 [DR Tulu collection](https://huggingface.co/collections/rl-research/dr-tulu)
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# Citation
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```
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@article{shao2025dr,
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title={DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research},
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author={Shao, Rulin and Asai, Akari and Shen, Shannon Zejiang and Ivison, Hamish and Kishore, Varsha and Zhuo, Jingming and Zhao, Xinran and Park, Molly and Finlayson, Samuel G and Sontag, David and others},
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journal={arXiv preprint arXiv:2511.19399},
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year={2025}
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}
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``` |