{"ID":2849274,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.24702","arxiv_id":"2510.24702","title":"Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents","abstract":"Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneous formats, tools, and interfaces. To this end, we introduce the agent data protocol (ADP), a light-weight representation language that serves as an \"interlingua\" between agent datasets in diverse formats and unified agent training pipelines downstream. The design of ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic workflows, while remaining simple to parse and train on without engineering at a per-dataset level. In experiments, we unified a broad collection of 13 existing agent training datasets into ADP format, and converted the standardized ADP data into training-ready formats for multiple agent frameworks. We performed SFT on these data, and demonstrated an average performance gain of ~20% over corresponding base models, and delivers state-of-the-art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.","short_abstract":"Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this work, we argue that the bottleneck is not a lack of underlying data sources, but that a large variety of data is fragmented across heterogeneo...","url_abs":"https://arxiv.org/abs/2510.24702","url_pdf":"https://arxiv.org/pdf/2510.24702v2","authors":"[\"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\"]","published":"2025-10-28T17:53:13Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}