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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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# Introduction |
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We present **Tongyi DeepResearch**, an agentic large language model featuring 30 billion total parameters, with only 3 billion activated per token. Developed by Tongyi Lab, the model is specifically designed for **long-horizon, deep information-seeking** tasks. Tongyi-DeepResearch demonstrates state-of-the-art performance across a range of agentic search benchmarks, including Humanity's Last Exam, BrowserComp, BrowserComp-ZH, WebWalkerQA, GAIA, xbench-DeepSearch and FRAMES. |
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More details can be found in our 📰 [Tech Blog](https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research). |
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## Key Features |
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- ⚙️ **Fully automated synthetic data generation pipeline**: We design a highly scalable data synthesis pipeline, which is fully automatic and empowers agentic pre-training, supervised fine-tuning, and reinforcement learning. |
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- 🔄 **Large-scale continual pre-training on agentic data**: Leveraging diverse, high-quality agentic interaction data to extend model capabilities, maintain freshness, and strengthen reasoning performance. |
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- 🔁 **End-to-end reinforcement learning**: We employ a strictly on-policy RL approach based on a customized Group Relative Policy Optimization framework, with token-level policy gradients, leave-one-out advantage estimation, and selective filtering of negative samples to stabilize training in a non‑stationary environment. |
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- 🤖 **Agent Inference Paradigm Compatibility**: At inference, Tongyi-DeepResearch is compatible with two inference paradigms: ReAct, for rigorously evaluating the model's core intrinsic abilities, and an IterResearch-based 'Heavy' mode, which uses a test-time scaling strategy to unlock the model's maximum performance ceiling. |
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## Download |
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You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/DeepResearch. |
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```bibtex |
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@misc{tongyidr, |
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author={Tongyi DeepResearch Team}, |
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title={Tongyi DeepResearch: A New Era of Open-Source AI Researchers}, |
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year={2025}, |
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howpublished={\url{https://github.com/Alibaba-NLP/DeepResearch}} |
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} |
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``` |
