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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Introduction
+
+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 BrowserComp-EN, BrowserComp-ZH, GAIA, Humanity's Last Exam, xbench-DeepSearch, and WebWalkerQA.
+
+
+## Key Features
+
+- ⚙️ **Fully automated synthetic data generation pipeline**: Covers both the pre-training stage (data creation, filtering, and scaling) and the post-training stage (evaluation, refinement, and filtering).
+- 🔄 **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.
+
+## Download
+
+You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/DeepResearch.
+
+
+```bibtex
+@misc{tongyidr,
+  author={Tongyi DeepResearch Team},
+  title={Tongyi-DeepResearch},
+  year={2025},
+  howpublished={\url{https://github.com/Alibaba-NLP/DeepResearch}}
+}
+```