AWorld: Orchestrating the Training Recipe for Agentic AI
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.
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Chengyue Yu
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2025-09-01T07:53:21.807Z
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yuchengyue
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yuyuzuicool
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Siyuan Lu
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2025-08-29T08:01:48.010Z
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Siyuan Lu (SII)
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IcyFish
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Chenyi Zhuang
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2025-09-01T07:53:23.689Z
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chenyi zhuang
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chengle
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Dong Wang
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Qintong Wu
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2025-09-01T07:53:30.472Z
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Qing Wu
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qingw-dev
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Zongyue Li
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Runsheng Gan
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Chunfeng Wang
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Siqi Hou
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2025-09-01T07:53:28.427Z
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Hou
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Szeki
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Gaochi Huang
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Wenlong Yan
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2025-09-01T07:53:32.475Z
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Yan
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JimYaa
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Lifeng Hong
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Aohui Xue
claimed_verified
2025-09-01T07:53:26.215Z
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xue
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huiao
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Yanfeng Wang
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2025-09-01T07:53:35.385Z
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wang yan feng
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wangyeng
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Jinjie Gu
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David Tsai
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Tao Lin
AWorld: Orchestrating the Training Recipe for Agentic AI
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.
AWorld, an open-source system for large-scale agent-environment interaction, accelerates experience collection and enhances reinforcement learning, leading to significant improvements in agentic AI performance on complex benchmarks.
reinforcement learning Qwen3-32B GAIA benchmark agent-environment interaction experience generation distributed tasks cluster sequential execution agentic AI model improvement