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Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
arXiv – CS AI|Yueqi Song, Ketan Ramaneti, Zaid Sheikh, Ziru Chen, Boyu Gou, Tianbao Xie, Yiheng Xu, Danyang Zhang, Apurva Gandhi, Fan Yang, Joseph Liu, Tianyue Ou, Zhihao Yuan, Frank Xu, Shuyan Zhou, Xingyao Wang, Xiang Yue, Tao Yu, Huan Sun, Yu Su, Graham Neubig|
🤖AI Summary
Researchers introduce Agent Data Protocol (ADP), a standardized format for unifying diverse AI agent training datasets across different formats and tools. The protocol enabled training on 13 unified datasets, achieving ~20% performance gains over base models and state-of-the-art results on coding, browsing, and tool use benchmarks.
Key Takeaways
- →Agent Data Protocol (ADP) serves as an interlingua to standardize fragmented AI agent training data across heterogeneous formats.
- →The protocol supports diverse tasks including API/tool use, browsing, coding, software engineering, and general agentic workflows.
- →Researchers unified 13 existing agent training datasets using ADP format for scalable training pipelines.
- →Models trained on ADP-standardized data showed average 20% performance improvement over base models.
- →The standardized approach achieved state-of-the-art results on multiple benchmarks without domain-specific tuning.
#ai-agents#machine-learning#data-standardization#llm-training#supervised-finetuning#agent-protocols#open-source#benchmarking
Read Original →via arXiv – CS AI
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