AWorld: Orchestrating the Training Recipe for Agentic AI
Authors: name: Chengyue Yu; name: Siyuan Lu; name: Chenyi Zhuang; name: Dong Wang; name: Qintong Wu; name: Zongyue Li; name: Runsheng Gan; name: Chunfeng Wang; name: Siqi Hou; name: Gaochi Huang; name: Wenlong Yan; name: Lifeng Hong; name: Aohui Xue; name: Yanfeng Wang; name: Jinjie Gu; name: David Tsai; name: Tao Lin
Published: 2025-08-28 04:04:30

Abstract: The learning from practice paradigm is crucial for developing capable Agentic
AI systems, yet it is severely hampered by inefficient experience generation, a
bottleneck especially pronounced in complex benchmarks like GAIA. To address
this, we introduce AWorld, an open-source system engineered for large-scale
agent-environment interaction. By distributing tasks across a cluster, AWorld
accelerates experience collection by 14.6x compared to standard single-node,
sequential execution. This critical speedup makes extensive reinforcement
learning practical and scalable. Leveraging this capability, we trained a
Qwen3-32B-based agent that significantly outperforms its base model, increasing
its overall GAIA accuracy from 21.59% to 32.23%. On the benchmark's most
challenging levels, our agent achieves a score of 16.33%, surpassing the
performance of leading proprietary models. Our open-source system and resulting
agent provide a practical blueprint for a complete agentic AI training
pipeline, from efficient interaction to demonstrable model improvement.