Update README.md

README.md

@@ -167,9 +167,11 @@ Because they wanted someone who could communicate complex ideas without making a
 ```
 
 ### Use case 3: RLHF 
-PairRM has been trained on various high-quality and large-scale dataset with human preference annotations and exhibits great correlation with human preferences with an extremly small model size (0.4B), approching the performance of GPT-4. 
-We believe PairRM will power the alignment of LLM in an efficient and effective way.
-With a `blender.compare()` function, you can easily apply PairRM to poopular RLHF toolkits like [trl](https://huggingface.co/docs/trl/index). 
+PairRM has been trained on various high-quality and large-scale datasets with human preference annotations 
+and shown great correlation with human preferences with an extremely small model size (0.4B), 
+approching the performance of GPT-4. 
+PairRM will better help the future alignment of LLMs in a more efficient and effective way.
+With a `blender.compare()` function, you can apply PairRM to popular RLHF toolkits such as [trl](https://huggingface.co/docs/trl/index). 
 
 **🔥 Check more details on our example jupyter notebook usage: [`blender_usage.ipynb`](https://github.com/yuchenlin/LLM-Blender/blob/main/blender_usage.ipynb)**
 
@@ -184,7 +186,7 @@ Learn more in our LLM-Blender Github [README.md](https://github.com/yuchenlin/LL
 ### Context length
 |  PairRanker type  | Source max length | Candidate max length | Total max length |
 |:-----------------:|:-----------------:|----------------------|------------------|
-| [pair-ranker](https://huggingface.co/llm-blender/pair-ranker)               | 128               | 128                  | 384              |
+| [pair-ranker](https://huggingface.co/llm-blender/pair-ranker)  (our previous version)             | 128               | 128                  | 384              |
 | [PairRM](https://huggingface.co/llm-blender/pair-reward-model/) (This model) | 1224              | 412                  | 2048             |