Project Title

ART — Reinforcement Learning Framework for Training Multi-Step Agents

Overview

ART is an open-source reinforcement learning (RL) framework designed to train multi-step agents for real-world tasks using GRPO. It is particularly focused on improving agent reliability by allowing large language models (LLMs) to learn from experience. ART provides a user-friendly interface for integrating GRPO into any Python application, making it easier for developers to implement and train their agents.

Key Features

Serverless RL training with W&B Training
Integration of GRPO into Python applications
Support for popular LLMs like Qwen2.5, Qwen3, and Llama
Notebooks for hands-on introduction and learning

Use Cases

Training agents for real-world tasks using reinforcement learning
Improving agent reliability through experience-based learning
Integrating GRPO into existing Python applications for enhanced agent capabilities
Experimenting with different LLMs for various tasks

Advantages

Reduces training costs by 40% through multiplexing on shared production-grade inference clusters
Accelerates training by 28% by scaling to 2000+ concurrent requests across multiple GPUs
Eliminates infrastructure management headaches with fully managed infrastructure
Provides instant deployment and access to every checkpoint via W&B Inference

Limitations / Considerations

The project's license is currently unknown, which may affect its usage in certain scenarios
As an open-source project, it relies on community contributions for maintenance and updates
The effectiveness of the framework may vary depending on the specific use case and agent being trained

RLlib: A scalable and flexible reinforcement learning library that supports a wide range of algorithms. Unlike ART, RLlib focuses more on scalability and flexibility across various algorithms.
Stable Baselines3: A set of improved implementations of reinforcement learning algorithms that integrate well with PyTorch. Stable Baselines3 differs from ART in its focus on providing stable and high-performance implementations of standard RL algorithms.
Coach: A reinforcement learning agent development and training framework that supports various environments and algorithms. Coach differs from ART in its broader scope, including support for multiple environments and a wider range of algorithms.

Basic Information

GitHub: https://github.com/OpenPipe/ART
Stars: 7,817
License: Unknown
Last Commit: 2025-11-13

📊 Project Information

Project Name: ART
GitHub URL: https://github.com/OpenPipe/ART
Programming Language: Python
⭐ Stars: 7,817
🍴 Forks: 602
📅 Created: 2025-03-10
🔄 Last Updated: 2025-11-13

🏷️ Project Topics

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📚 Documentation

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ART

Project Description