Update README

README.md

@@ -1,24 +1,29 @@
 <div align="center">
 
-<a href="https://openpipe.ai"><picture>
-<img alt="ART header" src="https://github.com/openpipe/art/raw/main/assets/ART_header.png" width="100%">
+<a href="https://art.openpipe.ai"><picture>
+<img alt="ART logo" src="assets/ART_logo.png" width="160px">
 </picture></a>
 
 <p align="center">
-  <a href="https://colab.research.google.com/github/openpipe/art/blob/main/examples/2048/2048.ipynb"><img src="https://github.com/openpipe/art/raw/main/assets/Train_pill.png"  width="150"  alt="Train on Colab"></a>
-  <a href="https://discord.gg/zbBHRUpwf4"><img src="https://github.com/openpipe/art/raw/main/assets/Discord_pill.png" width="180" alt="Join Discord"></a>
-  <a href="https://art.openpipe.ai"><img src="https://github.com/openpipe/art/raw/main/assets/Documentation_pill.png" width="180" alt="Docs"></a>
+  <h1>Agent Reinforcement Trainer</h1>
 </p>
 
-### Train GRPO-powered RL agents for real-world tasks!
+<p>
+Train multi-step agents for real-world tasks using GRPO.
+</p>
+
+[![PRs-Welcome][contribute-image]][contribute-url]
+[![Downloads][downloads-image]][pypi-url]
+[![Train Agent](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openpipe/art/blob/main/examples/2048/2048.ipynb)
 
-![](https://github.com/openpipe/art/raw/main/assets/Header_separator.png)
+[![Join Discord](https://img.shields.io/badge/Join%20Discord-5865F2?style=plastic&logo=discord&logoColor=white)](https://discord.gg/zbBHRUpwf4)
+[![Documentation](https://img.shields.io/badge/Documentation-orange?style=plastic&logo=gitbook&logoColor=white)](https://art.openpipe.ai)
 
 </div>
 
-# Agent Reinforcement Trainer (ART)
+## Overview
 
-ART is an open-source reinforcement training library for improving LLM performance in agentic workflows. ART utilizes the powerful GRPO reinforcement learning algorithm to train models from their own experiences. Unlike most RL libraries, ART allows you to execute agent runs **in your existing codebase** while offloading all the complexity of the RL training loop to the ART backend. Read about the [ training loop](#training-loop-overview). Then try out one of the notebooks below!
+ART is an open-source reinforcement training library for improving LLM reliability in agentic workflows. ART utilizes the powerful GRPO reinforcement learning algorithm to let models learn from experience. Read about the [training loop](#training-loop-overview). Then run one of the notebooks below to train your own agent!
 
 ## 📒 Notebooks
 
@@ -29,6 +34,21 @@ ART is an open-source reinforcement training library for improving LLM performan
 | **Tic Tac Toe**   | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art/blob/main/examples/tic_tac_toe/tic-tac-toe.ipynb)     | Qwen 2.5 3B learns to play Tic Tac Toe    | <img src="/assets/benchmarks/tic-tac-toe-local/accuracy-training-progress.svg" height="72"> [benchmarks](/examples/tic_tac_toe/benchmark_tic_tac_toe.ipynb) |
 | **Codenames**     | [🏋️ Train agent](https://colab.research.google.com/github/openpipe/art/blob/main/examples/codenames/Codenames_RL.ipynb)      | Qwen 2.5 3B learns to play Codenames      | <img src="/assets/benchmarks/codenames/win_rate_over_time.png" height="72"> [benchmarks](/examples/codenames/Codenames_RL.ipynb)                            |
 
+## Why ART?
+
+- ART provides convenient wrappers for introducing RL training into **existing applications**. We abstract the training server into a modular service that your code doesn't need to interface with.
+- **Train from anywhere.** Run the ART client on your laptop and let the ART server kick off an ephemeral GPU-enabled environment, or run on a local GPU.
+- Integrations with hosted platforms like W&B, Langfuse, and OpenPipe provide flexible observability and **simplify debugging**.
+- ART is customizable with **intelligent defaults**. You can configure training parameters and inference engine configurations to meet specific needs, or take advantage of the defaults, which have been optimized for training efficiency and stability.
+
+## Installation
+
+ART agents can be trained from any client machine that runs python. To add to an existing project, run this command:
+
+```
+pip install openpipe-art
+```
+
 ## 🤖 ART•E Agent
 
 Curious about how to use ART for a real-world task? Check out the [ART•E Agent](https://openpipe.ai/blog/art-e-mail-agent) blog post, where we detail how we trained Qwen 2.5 14B to beat o3 at email retrieval!
@@ -90,3 +110,8 @@ ART stands on the shoulders of giants. While we owe many of the ideas and early
 - [SkyPilot](https://github.com/skypilot-org/skypilot)
 
 Finally, thank you to our partners who've helped us test ART in the wild! We're excited to see what you all build with it.
+
+[pypi-url]: https://pypi.org/project/openpipe-art/
+[contribute-url]: https://github.com/openpipe/art/blob/main/CONTRIBUTING.md
+[contribute-image]: https://img.shields.io/badge/PRs-welcome-blue.svg
+[downloads-image]: https://img.shields.io/pypi/dm/openpipe-art?color=364fc7&logoColor=364fc7