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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 development experience. Cheers!
AReaL Highlights
- ⚡ Flexibility: Seamless customization for
agentic RL and
online RL training
for black-box agent applications by simply replacing the
base_url. - 📈 Scalability: Stable fully asynchronous RL training with industry-leading speed.
- ✨ Cutting-Edge Performance: State-of-the-art math, coding, search, and customer service agents.
