Add library name, pipeline tag and project page link

README.md

@@ -1,19 +1,20 @@
 ---
-license: apache-2.0
-datasets:
-- nics-efc/R2R_Router_Training
-- nics-efc/R2R_query
 language:
 - en
-metrics:
-- accuracy
-base_model:
-- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-- deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+license: apache-2.0
+task_categories:
+- text-generation
+tags:
+- code
+- math
+- QA
+- token-classification
 ---
 
-This is the default router from the paper [R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing](https://arxiv.org/abs/2505.21600). 
+This is the training and validation set used by the paper [R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing](https://huggingface.co/papers/2505.21600). This dataset contains token-level routing labels generated to train a lightweight router that selectively uses a Large Language Model (LLM) for critical, path-divergent tokens during inference, improving efficiency without sacrificing accuracy.
+
+Roads to Rome (R2R) is a neural token router that efficiently combines Large Language Models (LLMs) and Small Language Models (SLMs) by selectively routing only critical, reasoning-divergent tokens to the large model.
 
-Roads to Rome (R2R) is a neural token router that efficiently combines Large Language Models (LLMs) and Small Language Models (SLMs) by selectively routing only critical, reasoning-divergent tokens to the large model. 
+Please visit our [GitHub repo](https://github.com/thu-nics/R2R) for more information.
 
-Please visit our [GitHub repo](https://github.com/thu-nics/R2R) for more information.
+Project page: [https://fuvty.github.io/R2R_Project_Page/](https://fuvty.github.io/R2R_Project_Page/)