Agent Data Protocol: Unifying Datasets for Diverse, Effective
  Fine-tuning of LLM Agents
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.
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Yueqi Song
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Ketan Ramaneti
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Zaid Sheikh
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Ziru Chen
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Boyu Gou
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2025-10-30T14:40:28.796Z
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Boyu Gou
BoyuNLP
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BoyuNLP
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Tianbao Xie
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Yiheng Xu
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2025-11-17T10:32:21.381Z
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Yiheng Xu
ranpox
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ranpox
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Danyang Zhang
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Apurva Gandhi
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Fan Yang
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Joseph Liu
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Tianyue Ou
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Zhihao Yuan
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Frank Xu
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Shuyan Zhou
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Xingyao Wang
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Xiang Yue
claimed_verified
2025-12-16T22:33:20.684Z
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Xiang Yue
yuexiang96
user
yuexiang96
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Tao Yu
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Huan Sun
69022de3646208eac0d1f71d
Yu Su
69022de3646208eac0d1f71e
Graham Neubig
Agent Data Protocol: Unifying Datasets for Diverse, Effective
  Fine-tuning of LLM Agents
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.
The agent data protocol (ADP) standardizes diverse agent training datasets, enabling improved performance across various tasks without domain-specific tuning.
agent data protocol ADP supervised finetuning AI agents agent datasets API/tool use browsing coding software engineering agentic workflows unified agent training pipelines SFT state-of-the-art near-SOTA coding benchmarks browsing benchmarks tool use benchmarks research benchmarks