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

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.