Update README.md

README.md

@@ -11,7 +11,7 @@ license: llama3
     [Haoxiang Wang*](https://haoxiang-wang.github.io/), [Wei Xiong*](https://weixiongust.github.io/WeiXiongUST/index.html), [Tengyang Xie](https://tengyangxie.github.io/), [Han Zhao](https://hanzhaoml.github.io/), [Tong Zhang](https://tongzhang-ml.org/)
 
 + **Blog**: https://rlhflow.github.io/posts/2024-05-29-multi-objective-reward-modeling/
-+ **Tech Report**: To be released in June 2024
++ **Tech Report**: https://arxiv.org/abs/2406.12845
 + **Model**: [ArmoRM-Llama3-8B-v0.1](https://huggingface.co/RLHFlow/ArmoRM-Llama3-8B-v0.1)
   + Finetuned from model: [FsfairX-LLaMA3-RM-v0.1](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1)
 - **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/
@@ -101,10 +101,10 @@ print(helpsteer_rewards_pred)
 
 If you find this work useful for your research, please consider citing:
 ```
-@misc{wang2024interpretable,
-  title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
-  author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
-  year={2024}
+@article{ArmoRM,
+      title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts}, 
+      author={Haoxiang Wang and Wei Xiong and Tengyang Xie and Han Zhao and Tong Zhang},
+      journal={arXiv preprint arXiv:2406.12845},
 }
 
 @inproceedings{wang2024arithmetic,