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README.md

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 license: mit
 base_model:
 - Qwen/QwQ-32B
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+---
+- 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.