Improve model card: add metadata, paper abstract, and code link

README.md

@@ -1,6 +1,9 @@
 ---
-license: apache-2.0
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
 # 🌐 WebThinker-R1-14B
 
 <div align="left" style="line-height: 1;">
@@ -13,11 +16,35 @@ license: apache-2.0
   <a href="https://huggingface.co/papers/2504.21776" target="_blank" style="margin: 2px;">
     <img alt="Paper" src="https://img.shields.io/badge/Paper-Hugging%20Face-yellow?logo=huggingface" style="display: inline-block; vertical-align: middle;"/>
   </a>
-  <a href="https://opensource.org/licenses/Apache-2.0" target="_blank" style="margin: 2px;">
-    <img alt="License" src="https://img.shields.io/badge/LICENSE-Apache_2.0-green.svg" style="display: inline-block; vertical-align: middle;"/>
+  <a href="https://opensource.org/licenses/MIT" target="_blank" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green.svg" style="display: inline-block; vertical-align: middle;"/>
   </a>
 </div>
 
+## WebThinker: Empowering Large Reasoning Models with Deep Research Capability
+
+Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate
+impressive long-horizon reasoning capabilities. However, their reliance on
+static internal knowledge limits their performance on complex,
+knowledge-intensive tasks and hinders their ability to produce comprehensive
+research reports requiring synthesis of diverse web information. To address
+this, we propose WebThinker, a deep research agent that empowers LRMs
+to autonomously search the web, navigate web pages, and draft research reports
+during the reasoning process. WebThinker integrates a Deep Web
+Explorer module, enabling LRMs to dynamically search, navigate, and extract
+information from the web when encountering knowledge gaps. It also employs an
+Autonomous Think-Search-and-Draft strategy, allowing the model to
+seamlessly interleave reasoning, information gathering, and report writing in
+real time. To further enhance research tool utilization, we introduce an
+RL-based training strategy via iterative online Direct Preference
+Optimization (DPO). Extensive experiments on complex reasoning benchmarks
+(GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive)
+demonstrate that WebThinker significantly outperforms existing methods and
+strong proprietary systems. Our approach enhances LRM reliability and
+applicability in complex scenarios, paving the way for more capable and
+versatile deep research systems.
+
+
 ## Overview
 
 WebThinker-R1-14B is part of the WebThinker series that enables large reasoning models to autonomously search, explore web pages, and draft research reports within their thinking process. This 14B parameter model provides deep research capabilities through:
@@ -65,9 +92,8 @@ This model can be used for:
 
 ## License
 
-This model is released under the Apache License 2.0.
+This model is released under the MIT License.
 
 ## Contact
 
-For any questions or feedback, please reach out to us at [[email protected]](mailto:[email protected]).
-
+For any questions or feedback, please reach out to us at [[email protected]](mailto:[email protected]).