Update README.md

README.md

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 This repository contains model checkpoints and artifacts for the paper **How Well Does Generative Recommendation Generalize?**.
 
 <a href="https://huggingface.co/papers/2603.19809"><img src="https://img.shields.io/badge/Paper-ArXiv-red"></a>
-<a href="https://github.com/Jamesding000/MemGen-GR"><img src="https://img.shields.io/badge/Code-GitHub-black"></a>
+<a href="https://github.com/Jamesding000/MemGen-GR"><img src="https://img.shields.io/badge/Code-GitHub-green"></a>
 <a href="https://huggingface.co/datasets/jamesding0302/memgen-annotations"><img src="https://img.shields.io/badge/Data-Hugging%20Face-yellow"></a>
 
 ## Overview
 
-A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. This work provides a systematic way to verify this hypothesis by categorizing data instances into those requiring **memorization** (reusing item transition patterns observed during training) and those requiring **generalization** (composing known patterns to predict unseen item transitions). 
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-The study shows that GR models perform better on instances that require generalization, whereas item ID-based models perform better when memorization is more important. The authors propose a simple memorization-aware indicator that adaptively combines both paradigms to improve overall performance.
+A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. This work provides a systematic way to verify this hypothesis by categorizing data instances into those requiring **memorization** (reusing item transition patterns observed during training) and those requiring **generalization** (composing known patterns to predict unseen item transitions). Our study shows that GR models perform better on instances that require generalization, whereas item ID-based models perform better when memorization is more important. 
 
 ## Folder Structure