Information

HuggingFace ｜ ModelScope

# Introduction We present **Tongyi DeepResearch**, an agentic large language model featuring 30.5 billion total parameters, with only 3.3 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,xbench-DeepSearch, FRAMES and SimpleQA. > Tongyi DeepResearch builds upon our previous work on the [WebAgent](./WebAgent/) project. More details can be found in our  Tech Blog.

## Features - **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. - **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. - **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. - **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. # Model Download You can directly download the model by following the links below. | Model | Download Links | Model Size | Context Length | | :-----------------: | :-----------------------------------------: | :----------: | :--------------: | | Tongyi-DeepResearch-30B-A3B | [HuggingFace](https://huggingface.co/Alibaba-NLP/Tongyi-DeepResearch-30B-A3B)
[ModelScope](https://modelscope.cn/models/iic/Tongyi-DeepResearch-30B-A3B) | 30B-A3B | 128K | # News [2025/09/20]Tongyi-DeepResearch-30B-A3B is now on [OpenRouter](https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b)! Follow the [Quick-start](https://github.com/Alibaba-NLP/DeepResearch?tab=readme-ov-file#6-you-can-use-openrouters-api-to-call-our-model) guide. [2025/09/17]We have released **Tongyi-DeepResearch-30B-A3B**. # Deep Research Benchmark Results

## Quick Start This guide provides instructions for setting up the environment and running inference scripts located in the [inference](./inference/) folder. ### 1. Environment Setup - Recommended Python version: **3.10.0** (using other versions may cause dependency issues). - It is strongly advised to create an isolated environment using \`conda\` or \`virtualenv\`. \`\`\`bash # Example with Conda conda create -n react_infer_env python=3.10.0 conda activate react_infer_env \`\`\` ### 2. Installation Install the required dependencies: \`\`\`bash pip install -r requirements.txt \`\`\` ### 3. Environment Configuration and Prepare Evaluation Data #### Environment Configuration Configure your API keys and settings by copying the example environment file: \`\`\`bash # Copy the example environment file cp .env.example .env \`\`\` Edit the \`.env\` file and provide your actual API keys and configuration values: - **SERPER_KEY_ID**: Get your key from [Serper.dev](https://serper.dev/) for web search and Google Scholar - **JINA_API_KEYS**: Get your key from [Jina.ai](https://jina.ai/) for web page reading - **API_KEY/API_BASE**: OpenAI-compatible API for page summarization from [OpenAI](https://platform.openai.com/) - **DASHSCOPE_API_KEY**: Get your key from [Dashscope](https://dashscope.aliyun.com/) for file parsing - **SANDBOX_FUSION_ENDPOINT**: Python interpreter sandbox endpoints (see [SandboxFusion](https://github.com/bytedance/SandboxFusion)) - **MODEL_PATH**: Path to your model weights - **DATASET**: Name of your evaluation dataset - **OUTPUT_PATH**: Directory for saving results > **Note**: The \`.env\` file is gitignored, so your secrets will not be committed to the repository. #### Prepare Evaluation Data The system supports two input file formats: **JSON** and **JSONL**. #### Supported File Formats: **Option 1: JSONL Format (recommended)** - Create your data file with \`.jsonl\` extension (e.g., \`my_questions.jsonl\`) - Each line must be a valid JSON object with \`question\` and \`answer\` keys: \`\`\`json \{"question": "What is the capital of France?", "answer": "Paris"\} \{"question": "Explain quantum computing", "answer": ""\} \`\`\` **Option 2: JSON Format** - Create your data file with \`.json\` extension (e.g., \`my_questions.json\`) - File must contain a JSON array of objects, each with \`question\` and \`answer\` keys: \`\`\`json [ \{"question": "What is the capital of France?", "answer": "Paris"\}, \{"question": "Explain quantum computing", "answer": ""\} ] \`\`\` **Important Note:** The \`answer\` field contains the **ground truth/reference answer** used for evaluation. The system generates its own responses to the questions, and these reference answers are used to automatically judge the quality of the generated responses during benchmark evaluation. #### File References for Document Processing: - If using the *file parser* tool, **prepend the filename to the \`question\` field** - Place referenced files in \`eval_data/file_corpus/\` directory - Example: \`\{"question": "report.pdf What are the key findings?", "answer": "..."\}\` #### File Organization: \`\`\` project_root/ ├── eval_data/ │ ├── my_questions.jsonl # Your evaluation data │ └── file_corpus/ # Referenced documents │ ├── report.pdf │ └── data.xlsx \`\`\` ### 4. Configure the Inference Script - Open \`run_react_infer.sh\` and modify the following variables as instructed in the comments: * \`MODEL_PATH\` - path to the local or remote model weights. * \`DATASET\` - full path to your evaluation file, e.g. \`eval_data/my_questions.jsonl\` or \`/path/to/my_questions.json\`. * \`OUTPUT_PATH\` - path for saving the prediction results, e.g. \`./outputs\`. - Depending on the tools you enable (retrieval, calculator, web search, etc.), provide the required \`API_KEY\`, \`BASE_URL\`, or other credentials. Each key is explained inline in the bash script. ### 5. Run the Inference Script \`\`\`bash bash run_react_infer.sh \`\`\` --- With these steps, you can fully prepare the environment, configure the dataset, and run the model. For more details, consult the inline comments in each script or open an issue. ### 6. You can use OpenRouter's API to call our model Tongyi-DeepResearch-30B-A3B is now available at [OpenRouter](https://openrouter.ai/alibaba/tongyi-deepresearch-30b-a3b). You can run the inference without any GPUs. You need to modify the following in the file [inference/react_agent.py](https://github.com/Alibaba-NLP/DeepResearch/blob/main/inference/react_agent.py): - In the call_server function: Set the API key and URL to your OpenRouter account’s API and URL. - Change the model name to alibaba/tongyi-deepresearch-30b-a3b. - Adjust the content concatenation way as described in the comments on lines **88–90.** ## Benchmark Evaluation We provide benchmark evaluation scripts for various datasets. Please refer to the [evaluation scripts](./evaluation/) directory for more details. ## Deep Research Agent Family

Tongyi DeepResearch also has an extensive deep research agent family. You can find more information in the following paper: [1] [WebWalker: Benchmarking LLMs in Web Traversal](https://arxiv.org/pdf/2501.07572) (ACL 2025)
[2] [WebDancer: Towards Autonomous Information Seeking Agency](https://arxiv.org/pdf/2505.22648) (NeurIPS 2025)
[3] [WebSailor: Navigating Super-human Reasoning for Web Agent](https://arxiv.org/pdf/2507.02592)
[4] [WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization](https://arxiv.org/pdf/2507.15061)
[5] [WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent](https://arxiv.org/pdf/2508.05748)
[6] [WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents](https://arxiv.org/pdf/2509.13309)
[7] [ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization](https://arxiv.org/pdf/2509.13313)
[8] [WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research](https://arxiv.org/pdf/2509.13312)
[9] [WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning](https://arxiv.org/pdf/2509.13305)
[10] [Scaling Agents via Continual Pre-training](https://arxiv.org/pdf/2509.13310)
[11] [Towards General Agentic Intelligence via Environment Scaling](https://arxiv.org/pdf/2509.13311) ## Misc ## Talent Recruitment We are hiring! Research intern positions are open (based in Hangzhou、Beijing、Shanghai) **Research Area**：Web Agent, Search Agent, Agent RL, MultiAgent RL, Agentic RAG **Contact**：[yongjiang.jy@alibaba-inc.com]() ## Contact Information For communications, please contact Yong Jiang (yongjiang.jy@alibaba-inc.com). ## Citation \`\`\`bibtex @misc\{tongyidr, author=\{Tongyi DeepResearch Team\}, title=\{Tongyi-DeepResearch\}, year=\{2025\}, howpublished=\{\url\{https://github.com/Alibaba-NLP/DeepResearch\}\} \} \`\`\`