Update README.md

README.md

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 # Pairwise Reward Model for LLMs (PairRM) from LLM-Blender 
 
 
-
 - Github: [https://github.com/yuchenlin/LLM-Blender](https://github.com/yuchenlin/LLM-Blender)
 - Paper: [https://arxiv.org/abs/2306.02561](https://arxiv.org/abs/2306.02561)
 - Space Demo: [https://huggingface.co/spaces/llm-blender/LLM-Blender](https://huggingface.co/spaces/llm-blender/LLM-Blender)
 
+
 ## Introduction 
 
+Pairwise Reward Model (PairRM) takes an instruction and a **pair** of output candidates as the input, 
+and output a score for each candidate to measure their **relative** quality. 
+Unlike the other RMs that encode and score each candidate respectively, 
+PairRM takes a pair of candidates and compares them side-by-side to indentify the subtle differences between them.
+
+PairRM can be used to (re-)rank a list of candidate outputs and thus can be used an LLM evaluator to efficiently assess the quality of LLMs in local environment.
+PairRM can also be used to enhance the decoding by `best-of-n sampling` (i.e., reranking N sampled outputs). 
+Apart from that, one can also use PairRM to 
+
 
 ## Installation
 Since PairRanker contains some custom layers and tokens. We recommend use PairRM with our llm-blender code API.