Open Source Agent Reinforcement Trainer Framework

Agent Reinforcement Trainer (ART) is an open-source framework designed for training multi-step agents in real-world tasks using GRPO. It enables agents to learn from experience, improving their reliability and performance in various applications.

Key features include:

• Serverless RL - A fully managed infrastructure that automates training and inference, reducing operational overhead.
• Cost Efficiency - Achieves up to 40% lower costs by multiplexing on a shared production-grade inference cluster.
• Faster Training - Scales to over 2000 concurrent requests across multiple GPUs, resulting in 28% faster training times.
• Instant Deployment - Every model checkpoint is instantly available for use.

ART integrates seamlessly with various platforms and provides customizable training parameters, making it suitable for developers looking to implement reinforcement learning in their applications.