Update README.md

README.md

@@ -20,6 +20,7 @@ with the K-wise maximum likelihood estimator proposed in [this paper](https://ar
 less harmful will get the highest reward score. Note that since the preference dataset [berkeley-nest/Nectar](https://huggingface.co/berkeley-nest) is based on GPT-4 preference, the reward model is likely to be biased
 towards GPT-4's own preference, including longer responses and certain response format. 
 
+For more detailed discussions, please check out our [blog post](starling.cs.berkeley.edu), and stay tuned for our upcoming code and paper!
 
 
 - **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
@@ -35,16 +36,15 @@ towards GPT-4's own preference, including longer responses and certain response
 - **Blog:** https://starling.cs.berkeley.edu/
 - **Paper:** Coming soon!
 - **Code:** Coming soon!
-- 
+
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-Please use the following code for running inference with the reward model.
+Please use the following code for inference with the reward model.
 
 ```
-# Load in the reward model
-
-
+## Define the reward model function class
+Test.
 ```