Alibaba-NLP/WebDancer-32B

This model was presented in the paper WebDancer: Towards Autonomous Information Seeking Agency.

You can download the model then run the inference scipts in https://github.com/Alibaba-NLP/WebAgent.

• Native agentic search reasoning model using ReAct framework towards autonomous information seeking agency and Deep Research-like model.
• We introduce a four-stage training paradigm comprising browsing data construction, trajectory sampling, supervised fine-tuning for effective cold start, and reinforcement learning for improved generalization, enabling the agent to autonomously acquire autonomous search and reasoning skills.
• Our data-centric approach integrates trajectory-level supervision fine-tuning and reinforcement learning (DAPO) to develop a scalable pipeline for training agentic systems via SFT or RL.
• WebDancer achieves a Pass@3 score of 61.1% on GAIA and 54.6% on WebWalkerQA.