AReaL

AReaL: A Large-Scale Asynchronous Reinforcement Learning System

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AReaL is an open-source fully asynchronous reinforcement learning training system for large reasoning and agentic models, developed by members from Tsinghua IIIS and the AReaL Team at Ant Group. Built upon the open-source project ReaLHF, we are fully committed to open-source principles by providing the training details, data, and infrastructure required to reproduce our results, along with the models themselves. AReaL aims to help everyone build their own AI agents easily and affordably. Our team loves milk tea because it's delicious, customizable, and affordable—we hope you enjoy our project just as much as you'd enjoy real milk tea. Cheers!

AReaL Highlights

• ⚡ Flexibility: Seamless customization for

agentic RL and online RL training 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.

📰 News

\[2026/03/02\] We provide a complete example to train your own 🦞 OpenClaw agent by simply replacing the base_url and api_key with AReaL's RL service - no complicated dependencies, no code changes, works with any agentic runtime!

\[2026/02/06\] We are delighted to introduce AReaL-SEA, a self-evolving data synthesis engine. Combined with RL training on AReaL, the 235B MoE model surpasses GPT 5 and achieves comparable performance with Gemini 3.0 Pro on $\\tau^2$-bench! Check out the paper, model, data, and code.

\[2026/01/15\] Congrats to our friends at CAMEL-AI for open-sourcing SETA, their terminal agent RL project trained with AReaL! Check out their training workflow and the announcement on X.

📋 Previous Releases