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AReaLgithub.com · Low evidenceAReaL is a large-scale asynchronous reinforcement learning infrastructure designed to bridge foundation model training with modern agent-based applications. Developed by researchers from Tsinghua IIIS and Ant Group, it focuses on efficiency and scalability for training large-scale reasoning and agentic models.
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