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DeepResearch (Tongyi DeepResearch) is an open-source “deep research agent” developed by Alibaba’s Tongyi Lab designed for long-horizon, information-seeking tasks. It’s built to act like a research agent: synthesizing, reasoning, retrieving information via the web and documents, and backing its outputs with evidence. The model is about 30.5 billion parameters in size, though at any given token only ~3.3B parameters are active. It uses a mix of synthetic data generation, fine-tuning and reinforcement learning; supports benchmarks like web search, document understanding, question answering, “agentic” tasks; provides inference tools, evaluation scripts, and “web agent” style interfaces. The aim is to enable more autonomous, agentic models that can perform sustained knowledge gathering, reasoning, and synthesis across multiple modalities (web, files, etc.).
Features
- Fully-automated synthetic data generation pipeline for agentic pre-training, supervised fine-tuning, and reinforcement learning
- Large-scale continual pre-training on “agentic data” to maintain freshness and improve reasoning performance
- Reinforcement learning using a customized algorithm with token-level policy gradients, leave-one-out advantage estimation
- Dual inference paradigms: supports “ReAct” as well as a heavier “IterResearch-based ‘Heavy’ mode”
- Very long context length model to handle large documents, sustained dialogues
- Benchmark performance across multiple “agentic search / research style” benchmarks
Project Samples
