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 </div>
 
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-RL4CO has been accepted as an oral presentation at the [NeurIPS 2023 GLFrontiers Workshop](https://glfrontiers.github.io/)! 🎉
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 An extensive Reinforcement Learning (RL) for Combinatorial Optimization (CO) benchmark. Our goal is to provide a unified framework for RL-based CO algorithms, and to facilitate reproducible research in this field, decoupling the science from the engineering.
 
 
@@ -37,7 +30,7 @@ RL4CO is built upon:
 ![RL4CO Overview](https://github.com/ai4co/rl4co/assets/34462374/4d9a670f-ab7c-4fc8-9135-82d17cb6d0ee)
 
 
-We provide several utilities and modularization. For autoregressive policies, we modularize reusable components such as _environment embeddings_ that can easily be swapped to [solve new problems](https://github.com/ai4co/rl4co/blob/main/examples/3-creating-new-env-model.ipynb).
+We provide several utilities and modularization. For example, we modularize reusable components such as _environment embeddings_ that can easily be swapped to [solve new problems](https://github.com/ai4co/rl4co/blob/main/examples/3-creating-new-env-model.ipynb).
 
 ![RL4CO Policy](https://github.com/ai4co/rl4co/assets/48984123/ca88f159-d0b3-459e-8fd9-89799be9d1b0)
 
@@ -187,6 +180,11 @@ If you find RL4CO valuable for your research or applied projects:
 }
 ```
 
+Note that a [previous version of RL4CO](https://openreview.net/forum?id=YXSJxi8dOV) has been accepted as an oral presentation at the [NeurIPS 2023 GLFrontiers Workshop](https://glfrontiers.github.io/). Since then, the library has greatly evolved and improved!
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 ## Join us
 [![Slack](https://img.shields.io/badge/slack-chat-611f69.svg?logo=slack)](https://join.slack.com/t/rl4co/shared_invite/zt-1ytz2c1v4-0IkQ8NQH4TRXIX8PrRmDhQ)