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.
68b11548b19c540001484981
Chengyue Yu
68b11548b19c540001484982
Siyuan Lu
claimed_verified
2025-08-29T08:01:48.010Z
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/avatars/92bf42710b206a3164b3ace8090443fa.svg
Siyuan Lu
user
IcyFish
68b11548b19c540001484983
Chenyi Zhuang
68b11548b19c540001484984
Dong Wang
68b11548b19c540001484985
Qintong Wu
68b11548b19c540001484986
Zongyue Li
68b11548b19c540001484987
Runsheng Gan
68b11548b19c540001484988
Chunfeng Wang
68b11548b19c540001484989
Siqi Hou
68b11548b19c54000148498a
Gaochi Huang
68b11548b19c54000148498b
Wenlong Yan
68b11548b19c54000148498c
Lifeng Hong
68b11548b19c54000148498d
Aohui Xue
68b11548b19c54000148498e
Yanfeng Wang
68b11548b19c54000148498f
Jinjie Gu
68b11548b19c540001484990
David Tsai
68b11548b19c540001484991
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