Update README.md

README.md

@@ -12,7 +12,7 @@ library_name: transformers
 ---
 
 # CodeRM-NT
-[Paper](https://github.com/THUDM/CodeRM-NT/blob/main/assets/CodeRM-NT.pdf) | 
+[Paper](https://aclanthology.org/2026.findings-acl.2150/) | 
 [Github](https://github.com/THUDM/CodeRM-NT)
 
 Providing accurate reward signals for code generated by LLMs is a significant challenge in applying reinforcement learning (RL) to code generation. Existing methods rely on unit tests, which are expensive to curate and unreliable when automatically synthesized.
@@ -65,4 +65,25 @@ Training with CodeRM-NT consistently outperforms synthetic unit tests and other
 
 ## Citation
 
-TODO
+If you find our work helpful, please kindly cite our paper:
+
+```
+@inproceedings{xia-etal-2026-coderm,
+    title = "{C}ode{RM}-{NT}: Reward Model for Code {RL} without Unit Tests",
+    author = "Xia, Xiao  and
+      Zhang, Dan  and
+      Sun, Tianrui",
+    editor = "Liakata, Maria  and
+      Moreira, Viviane P.  and
+      Zhang, Jiajun  and
+      Jurgens, David",
+    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
+    month = jul,
+    year = "2026",
+    address = "San Diego, California, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2026.findings-acl.2150/",
+    pages = "43316--43333",
+    ISBN = "979-8-89176-395-1"
+}
+```