Update README.md

README.md

@@ -18,8 +18,7 @@ library_name: transformers
 
 # 🚀 Can we cast reward modeling as a reasoning task?
 
-**RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating structured rubrics or reasoning traces—then emitting its preference.  
-Compared to traditional scalar or generative reward models, RM-R1 delivers **state-of-the-art performance** on public RM benchmarks while offering fully interpretable justifications.
+**RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating structured rubrics or reasoning traces—then emitting its preference. Compared to traditional scalar or generative reward models, RM-R1 delivers **state-of-the-art performance** on public RM benchmarks while offering fully interpretable justifications.
 
 
 ## 🧠 TL;DR
@@ -37,7 +36,8 @@ Compared to traditional scalar or generative reward models, RM-R1 delivers **sta
 ## 🔍 Demo Code 
 
 Try the model with this example. Full demo notebook available at:
-📎 GitHub: demo/demo.ipynb
+
+📎 [Official Demo Link](https://github.com/RM-R1-UIUC/RM-R1/blob/main/demo/demo.ipynb)
 
 ### 🧾 Prompt Template 
 
@@ -149,4 +149,15 @@ completion = tokenizer.decode(
 )
 
 print(completion)
+```
+
+## Citations
+
+```bibtex
+@article{chen2025rm,
+  title={RM-R1: Reward Modeling as Reasoning},
+  author={Chen, Xiusi and Li, Gaotang and Wang, Ziqi and Jin, Bowen and Qian, Cheng and Wang, Yu and Wang, Hongru and Zhang, Yu and Zhang, Denghui and Zhang, Tong and others},
+  journal={arXiv preprint arXiv:2505.02387},
+  year={2025}
+}
 ```