AWorld：構建智能代理AI的訓練配方

摘要

從實踐中學習的範式對於開發具備能力的自主AI系統至關重要，然而，這一過程卻因經驗生成效率低下而嚴重受阻，這一瓶頸在GAIA等複雜基準測試中尤為明顯。為解決這一問題，我們推出了AWorld，這是一個專為大規模智能體與環境交互而設計的開源系統。通過將任務分佈在集群中執行，AWorld相比標準的單節點順序執行方式，將經驗收集速度提升了14.6倍。這一關鍵的加速使得大規模強化學習變得切實可行且可擴展。利用這一能力，我們訓練了一個基於Qwen3-32B的智能體，其表現顯著超越了基礎模型，在GAIA上的總體準確率從21.59%提升至32.23%。在該基準測試最具挑戰性的級別上，我們的智能體取得了16.33%的成績，超越了領先的專有模型。我們的開源系統及其產生的智能體，為從高效交互到可證明的模型改進的完整自主AI訓練流程提供了一個實用的藍圖。

English

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：構建智能代理AI的訓練配方

AWorld: Orchestrating the Training Recipe for Agentic AI

摘要

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