AReaL

项目介绍

AReaL is a reinforcement learning (RL) infrastructure designed to bridge foundation model training with modern agent-based applications. It was originally developed by researchers and engineers from Tsinghua IIIS and the AReaL Team at Ant Group.

Built on a fully asynchronous RL training paradigm, AReaL is optimized for efficiency and scalability, making it particularly well-suited for training large-scale reasoning and agentic models.

AReaL’s mission is to make building AI agents accessible, efficient, and cost-effective for a broad community of developers and researchers.

Like milk tea - customizable, scalable, and enjoyable - we hope AReaL brings both flexibility and delight to your AI…