Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
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 F. Xu
Shuyan Zhou
Xingyao Wang
Xiang Yue
Tao Yu
Huan Sun
Yu Su
Graham Neubig
Venue: ICLR 2026 Oral (2026)
Keywords: agent
training
data
standardization
Primary area: datasets and benchmarks
TL;DR: We propose Agent Data Protocol (ADP), a lightweight "interlingua" schema that standardizes heterogeneous agent trajectories so datasets can plug into multiple agent SFT pipelines without per-dataset engineering.

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 supervised finetuning on the unified data, and demonstrated an average performance gain of $\sim$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.

Published as a conference paper at ICLR 2026
AGENT DATA PROTOCOL: UNIFYING DATASETS FOR DIVERSE, EFFECTIVE FINE-TUNING OF LLM AGENTS
Yuegi Song', Ketan Ramaneti', Zaid Sheikh', Ziru Chen', Boyu Gou?, Tianbao Xie", Yiheng Xu", Danyang Zhang', Apurva Gandhi', Fan Yang². Joseph Liu', Tianyue Ou', Zhihao Yuan-, Frank Xu², Shuyan Zhou', Xingyao Wang", Xiang Yuet, Tao Yu?, Huan Sun?, Yu Su², Graham Neubig
Caanegic Mellon University, Thhe Ohio State University, University of Hong Kong, 'Duke University, Fujjtsu Research, 6 Alll Hands Al {yueqis s, gneubig)@cs. cmu, edu
httpes//agentddttarrrocoo..c
ABSTRACT
Public research results on large-scale supervised finetuning of Al agents remain relatively rare, since the collection of agent training data presents unique chal- lenges. this work, we argue that the bottleneck is not lack underlying data sources, but that large variety of data is fragmented across heterogeneous for- mats, tools, and interfaces. To this end, we introduce the Agent Data Protocol (ADP), light-weight representation language that serves tween agent datasets in diverse formats and unified agent training pipelines down- stream. The design of ADP is expressive enough to capture large variety of tasks, including API/tool use, browsing, coding, software engineering, and gen- eral agentic workflows, while remaining simple to parse and train on without en- gineering at a per-dataset level. In experiments, we unified broad collection of 13 existing agent training datasets into ADP format, and converted the standard- ized ADP data into training-ready formats for multiple agent frameworks. We performed supervised finetuning on the unified data, and demonstrated an average performance gain of ~20% over corresponding base modcls, and delivers state- of-the-art art or near-SOTA performance on standard coding, browsing, tool use, and research benchmarks, without domain-specific tuning. All code and data re- leased publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.
"interlingua" be-
INTRODUCTION
Pre-training large language models (LLMs) benefits from abundant, readily available Internet-scale data. In contrast, post-training presents much harder challenge: high-quality task-specific data must be carefully curated. While creative strategies have emerged for collecting data in relatively simple settings, such as single-turn user interactions like code generation (Nijkamp et al., 2023),
question answering (Rajpurkar et al. 2016), and sentiment analysis (Maas al. 201 1). many real- world tasks are far more complex.
particularly difficult case agent applications, where models must take sequential actions and interact with the world iteratively. Building datasets for such scenarios requires recording and struc- turing trajectories of agent behavior, much more challenging than collecting static input-output pairs.
Despite these difficulties, growing body of work has explored different approaches for creating agent datasets. These efforts vary in methodology, from manual curation (Rawles et al. 2023 Xu
et al. 2024a), to synthetic data generation (Ou et al. 2024 Zheng al. 2024a), to recorded agent rollouts (Pan et al. 2025 Yang et al.] 2025b). The resulting datasets span a wide range of tasks, including web navigation (Deng et al. 2023 Lü et al. 2024), software development (Yang al.
20256, Pan et al.) 2025), visual interlace control (Rawles et al. 2023 Kapoor et al. 2024), and
general tool use (Zeng al. 2023 Liu al. 2024a (an overview of these datasets in
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Agent Data Protocol
OpenHands SFT
Agentinstrutt CodesAatinstuuct
Action
Observation
SWE Agent
SWF Gym
Action
Text Ooservation
MindZWWeb
Code Action
Web Observation
AgentLab SFT
Massage Action
APlAction
gotolet lggoogge dom
innrtonugino kwargn=)urt google comj
WobChreevvtionn unlggoodkucomm himp-cttrtio... Googlo
diderype homil <ites
Trajectory idrosompit. lentof
CodiAAtioon
pytfor pinit-allo World")
beegugeeusytton cortor--prim"Hokk Woric"
TextObservatio conte in Hili World so counoooonteoomom
Exccutton rssult: Hello World
MessageAction
How crnn lneeo you/?
can you?
Figure 1: Overview of the Agent Data Protocol (ADP). Raw data from diverse sources such as SWE- Gym converted into a standardized ADP format. ADP unifies data into Trajectory objects, which include two core components: Actions (API action, code action, message action) and Observations (text observation, web observation), enabling seamless integration with various agent SFT formats. Example conversions show how heterogeneous raw data is normalized for training agentic models.
However, despite the availability of such data, large-scale supervised fine-tuning (SFT) of agents remains rare in academic research. A few notable projects, such as Zeng al. (2023) and Mitra et al. (2024), have demonstrated their potential, but remain exceptions rather than the norm. Why has this not become standard practice? We argue that the issue is not lack of data, but rather lack of standardization. Existing datasets are fragmented, with inconsistent formats and representations, making it diffieult to combine, share, and leverage them effectively, thus they remain underutilized.
To address this gap, we introduce the Agent Data Protocol (ADP), a standardized expressive repre- sentation language for agent data. By converting heterogencous datasets into ADP, it makes it simple to generate large-scale and diverse data for variety of downstream training pipelines (Figure I). Technically, ADP is implemented as Pydantic Ssceemas that express actions and observations corre- sponding to common agent use cases such as communicating, browsing, coding, and miscellaneous tool calling, coupled with strict automated validation to maintain high data quality.
As first step to demonstrate the practical utility of ADP, we implement converters from 13 pre- existing datasets into ADP, and converters from ADP to 3 different agent architectures, demonstrat- ing its generality. Based on this, we create and release the largest publicly available dataset for agent training, consisting of 1.3M training trajectories, dubbed the ADP Dataset VI.
Our experiments show training agents using ADP leads to significant performance improvements across diverse domains, including coding (SWE-Bench Verified), web browsing (WebArena), re- search (GAIA), and agentic tool use (AgentBench), as shown in m 6 Notably, these results improve by an average of 20% over base models, and are competitive with or superior to other state-of-the-an art results from similarly-sized models. We also identify significant benefits from cross-task transfer, with training on the ADP data improving significantly over training on individual datasets. Be- yond performance, ADP enables systematic cross-dataset analysis, revealing trends and areas for improvement in publicly available data.
Finally, we release all code and datasets in open source foster community adoption and encourage contributions of new datasets. We believe ADP will unlock new wave of progress fine-tuning by providing the standardization needed make large-scale supervised agent training practical and scalable.
in agentic model
2 RELATED WORK
The development of effective LLM-based agents critically depends on high-quality training data that could capture the complexity of multi-step reasoning, tool usage, and environmental interaction
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Yao et al. 2022b Schick al. 2023 Deng al. 2023 Masterman et al. 2024). This section
reviews existing methods for agent data collection and the challenges that motivate ADP.
2.1
AGENT DATA COLLECTION METHODS
Existing approaches span manual creation (human experts creating step-by-step demonstrations of desired agent behaviors) (Nakano et al. [021 Yao et al., 2022a). synthetic generation (leverages existing LLMs to create agent trajectories through prompting or structured generation) (Luo et al.) 2023 Xu et al. 2024b), and recorded agent rollouts (captures trajectories from existing agent sys- tems during task execution) (Wang et al. 2024a Pan et al. 2025), etc, resulting abundant agent
training data, a representative set of which listed
Table
Table 1: Overview of Existing Agent Training Datasets. C=Coding, S=Software Engineering, T=API/Tool Use, W=Web Browsing.
Dataset
Variety
Count
Source
Note
AgentInstruct Zeng et all
2023
CTW
1.9K
synthetic
Mixture of Browsing, Database, OS, etc.
Code-Feedback (Zlenn
2024g
66.4K
Code generation with runtime feedback loops
CodeAActnnstrucf (Wang et 01
20.46
7.1K
synthetic
Code generation and tool use with exccution
Go-Browse Gandiii
W
9.5K
rollout
Struetured exploration
rollouts
Mind2Web [Don et 2025
W
1.0K
manual
Human web demos on real websites
Nobius SWE Trajectrriss
13,4K
rollout
SWE-agent trajectories from Nobius relying
Golubev et al. 2024)
solely on open-weight models
NNeuNav-Live
W
5.0K
rollout
Retroactively labeled live web exploration
NNetNav-wa /ulity aral 2024
W
4.2K
rollout
Retroactively labeled WebArena exploration
spenhands-feddbaae
0.2K
rollout
Recorded OpenHands agent trajoctories with hu- man feedback
Alnds AI, (022)
Agentimstruct Mitra 2024
1046.1K
synthetic
Large-scale synthetic tool-use instructions data
SWE-Gym (Pan et of 2025
0.5K
rollout
Agent trajectories solving real GitHub repo tasks
SWE-smith
5.0K
synthetic Trajectories agents synthesized bug-fix
Synatra On 20 al. 2024
W
99.9K
synthetic Synthetically created web demos of tutorials
We also group each dataset into a coarse task category.
Coding: generally includes fundamental programming tasks, such command line code genera- tion, algorithm implementation, code completion, code translation, and code repair, etc.
Software Engineering: often consists of repository-level software engineering tasks, such as bug fixing, feature implementation, code refactoring, and dependency management, etc.
API/Tool Use: usually requires agents to use external APis/tools effectively to solve tasks. Com- mon tools include file manipulation, database queries, and customized APIs, etc.
Web Browsing: commonly encompasses tasks including web navigation, online shopping, and social media interactions, etc, requiring agents to understand GUIs.
2.2 CHALLENGES AND LIMITATIONS
Despite abundant existing agent training datasets, several fundamental challenges prevent effective large-scale utilization these resources:
Complexity of Data Curation: Creation of high-quality agent training data requires significant
resources and expertise (Paullada et 2021 Bhardwaj et al. 2024 Zhha al. 2025). Manual curation is expensive and requires domain knowledge; synthetic generation faces challenges in verifying data quality; recorded agent rollouts are fundamentally constrained by the capabilities of existing baseline agents, limiting the diversity and complexity of trajectories. While recent efforts have scaled trajectory collection (Song et al., 2024 Mitra et al.) 2024), the fundamental challenge of balancing quality, diversity, and scale across different curation approaches remains.
Existing agent training datasets each employ its own represen- tation format, action spaces, and observation structures (Ning et al. 2025 Luo et al. 2025). For example, some web datasets use HTML while some use accessibility tree structures (de Chezelles et al. 2025). Existing efforts have noted and begun addressing data standardization (Zhang et al.
Heterogeneity of Dataset Format:
2024 Chen al. 2024 Mohammadi al., 2025 Xi al. 2025 Zhang al. 2025). but they mostly focused on proposing task-specific or agent- specifcc unification rather than community- wide standardization of data representation, limiting plug-and-play with other datasets agents,
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where significant engineering effort is still required to utilize multiple datasets together, hindering integration across different data sources.
The diverse structures of existing datasets also makes difficult to perform systematic comparisons or quantitative analysis across different data sources (Putrama & Martinek, 2024), limiting researchers' ability to understand the relative usefulness, coverage, and quality of different datasets, hindering data-driven selection improvements.
Difficulty of Analysis and Comparison:
THE AGENT DATA PROTOCOL
To overcome these challenges and limitations, and make good use existing data resources, we propose the Agent Data Protocol (ADP). ADP establishes unified schema that bridges the gap between existing heterogeneous agent training datasets and large-scale supervised agent fine-tuning.
3.1 DESIGN PRINCIPLES
We design ADP around the following core principles:
Simplicity: ADP maintains a simple and intuitive structure. This directly addresses the complexity of data curation challenge by providing straightforward framework that eliminates the need for specialized per-dataset engineering, making large-scale agent data utilization accessible to researchers without extensive adaptation effort.
Standardization: ADP is designed to provide a unified representation that unifies existing agent training datasets of various different formats to a standardized format, addressing the challenge of heterogeneous dataset formats.
Expressiveness: ADP is designed to ensure that complex agentic trajectories could be accurately expressed with no loss of critical information. This directly addresses the difficulty of analysis and comparison challenge because ADP is expressive enough to cover the broad variety of existing agent datasets across different domains, enabling researchers to put these diverse datasets under the same conditions and context.
By addressing the fundamental challenges in utilization agent data, ADP aims push the progress in agent training, making large-scale agent SFT more accessible the broader research community.
3.2 ARCHITECTURE
The ADP schema is implemented Pydantic schemas, and simple yet expressive in design. Each ADP standardized agent trajectory is represented Tra jectory object.
Trajectory consists of (1) id: trajectory id, (2) content: an alternating sequence of actions and observations representing the agent's interaction with the user/environmennt, (3) details: A flexible metadata dictionary for dataset-specific information (e.g., dataset source URLs).
Action represents agents' decisions and behaviors. We categorize actions into three types:
API Actions: Function calls with structured parameters and outputs capturing tool use. Each API action includes: (1) function. name of tool call, (2) kwargs: dictionary of function argu- ments, and (3) descri pt i ion: optional reasoning or explanation for the action. For example, with ADP, a web navigation call goto (trl=https: / wwwgle..oo......o..oo..o........ com) represented as AP PeActiion (function-gon
kwargs wwwpt:tttt:.... www htt.ttppp/////..ggggll.....oooo gom)
Code Actions: Code generation and execution across programming languages. Each code action specifies: (I) language: the programming language (e.g., python), (2) content: the code to
execute, and (3) descriptions optional reasoning explanation for the action. For example, the ADP representation of python code block 'python print ("Hello World") is CodeAction (language-=pythool content-print ("Hello World").
Message Actions: Natural language communications between agents and users, each containing a content field, documenting agents' explanations, clarifications, and responses. For example, MessageAct (content=How can I help you?).

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Observation represents agents' perceptions from the environment, categorized into two types:
Captures the text information from various sources, including user instruc- tions and environmental feedback. Each text observation includes: (1) source: the origin of the observation ("user" "environment"), and (2) content: the observed text. For example, python execution output Execution result: Hello World, will be converted to ADP format TextObservate on (content=Hellen World, scurce=envi ronment)).
Text Observations:
Represent the state and content webpages. Each observation includes: (1) html: raw HTML content, (2) axt axtr accessibility tree of the webpage, (3) url: current page URL, (4) vi viewport_st ze: browser viewport dimensions, and (5) www.ggeobbevvaate. optional screenshot data. Web observations enable ADP to support complex browsing scenarios.
Web Observations:
The core insight behind ADP is that despite the surface-level diversity in agent datasets, most agen- tic interactions can be decomposed into sequence of actions taken by the agent and observations received from the environment. By standardizing these fundamental components, ADP directly ad- dresses each challenge identified S 2.2 while preserving the rich semantics of the original data. This unified representation enables researchers to combine datasets that were previously incompati- ble, facilitating large-scale training across diverse domains.
3.3 CONVERSION PIPELINE
we implemented a three-stage conversion pipeline with ADP that transforms heterogeneous datasets into training-ready agentic formats.
As shown in Figure I]
1. Raw to Standardized: This stage unifies original dataset formats into the ADP standardized schema. Each dataset is extracted in its raw format, and then converted to the ADP schema by mapping each dataset-specific actions and observations to the ADP's standardized action and observation space. For example, web browsing task with HTML representations is converted pairs of APIAction and WebObservation while coding task with execution output is mapped to CodeAction and Textobservation pairs.
2. Standardized to SFT: This stage converts ADP standardized trajectories into supervised fine- tuning (SFT) format suitable for training language models. Different agent frameworks oper- ate with distinct actions spaces, observations formats, etc. For example, OpenHands employs IPython execution with web browsing capabilities, SWE-Agent uses structured bash commands and file operations, while Agentl.a focuses DOM-based web interactions. Rather than train- ing only one generic action model, we recognize that effective agent training requires adaptation to each framework's 's specific scaffolding and interactions formats. For each agent harness, the conversion process uses one agent-specific script that translates each type of action and observa- tion into the target agent's action and observation space based on the agent's framework. This stage handles context management, specifies system prompts, and formats conversations to create SFT-ready instruction rerpuutttioo-esosseee pairs, optimized for the particular agent architecture.
Quality Assurance: This stage ensures data correctness and consistency alignment with agent format, tool use, and conversation structure through automated validation. Example quality checks include verifying tool call formats, ensuring mos tool calls are paired with an English thought, and checking whether the conversation ends properly, etc.
3.4 PRACTICAL IMPACT OF ADP ON AGENT TRAINING RESEARCH
The two-direction pipeline (Raw ADP and ADP +FF) cleanly separates responsibilities and eliminates redundant engineering FFigure 2). In practice:
Dataset conversion (once per dataset). Contributors convert each raw dataset the ADP schema exactly once. From then on, the dataset is a standardized resource usable by any agent harness.
Agent-specific conversion (once per agent). Each agent maintains single script for ADP->SFT; no dar-dataset engineering needed. Adding new datasets requires no change to agent-side scripts.
We set this threshold he 80%, but it be changed hased demand.
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Without ADP: Quadratic Effort
With ADP: Linear Effort
Agentinstruct
OpanHHands SFT
Agentinetruct
Openitandss SFT
Codee-ieedoase
Code Focdbook
Go-Srowee
SWE-Agent
SWE-Agent
ADP
Standardized Data
Mind2Well
Mind7/Web
Agentiab
SWE smith
Agontiab SFT
Figure 2: ADP collapses many-to-mane conversions into hub-and-spoke pipeline. Left: With- out ADP, each of D-many datasets needs a custom Raw-SFF converter for each of A-many agentic formats (quadratic ODD A) effort), causing duplicated code and efforts. Right: With ADP, each dataset is converted once RDww>ADP) and each agent only requires one converter yielding linear O(D+ A) effort. New datasets agents plug in immediately the rest of ADP.
ADP amortizes conversion cost across the community, accelerates adoption of new datasets, and en- sures that a single ADP-SFT script instantly unlocks the entire pool of ADP-standardized data agent framework. Without ADP, researchers must write Raw converter for each dataset-agent pair, duplicating effort across groups and making large-scale data integration brittle and slow. More discussion could be found in 6.3
CROSS DATASET ANALYSIS
Table Dataset Stats and Trajectory Analysis. A=APIAction, C=CodeAction, M-MessageAction.
Table 2
shows analysis on 13 ADP stan- dardized datasets, revealing significant di- versity across datasets.
AVG.
% Actions
% Func
Dataset
Rounds
(A/C/M)
Thought
AgentInstruet
8.2
64/10/26
100.0
Code-Feedhack
4.0
0/58/42
82.8
Trajectory Length. Trajectory rounds vary dramatically across datasets, from I to 26.8 turns, with an average of 10.1 turns. SWE datasets consistently exhibit longer length, reflecting the inherent com- plexity of repo-level programming tasks.
CodeActInstruct
4.0
0/65/35
98.6
Go-Browse
3.9
70/0/30
100.0
Mind2Web
9.7
90/0/10
0.0
Nebius SWE-Agent
16.2
67/27/6
100.0
NNetNav-live
8.2
80/0/20
99.9
NNetNav-wa
10.1
89/0/11
99.9
OpenHands
18.3
11/73/16
91.7
Action Distribution. Clear domain- specific preferences emerge from the action distributions after standardization with ADP, Web datasets (e.g. Mind2Web) heavily favor API actions with minimal code execution, reflecting their focus on interface interaction. Conversely, coding datasets (e.g., CodeAActnsttruct) show high code usage with no API usage, emphasizing direct programming activities. SWE datasets (e.g., SWE-smith) demonstrate mixed patterns, which relies on API actions like file writes while using code actions for code generation.
Orca AgentInstruct
1.3
0/15/85
84.0
SWE-Gym
19.7
61/25/14
42.0
SWE-smith
26.8
56/40/4
90.1
Synatra
1.0
100/00
99.9
Overall
10.1
53/24/23
83.8
F Function Reasoning Analysis. A striking finding is the high function thought coverage (2 90% for most datasets), indicating that these training datasets consistently provide explanations for actions. This is particularly valuable for interpretability and training agents with reasoning abilities. Impor- tantly, high reasoning coverage appears across all task varieties, suggesting that function thoughts represent general characteristic of well-documented datasets rather than domain-specific behavior.
EXPERIMENTAL SETUP
5.1 TRAINING SETUP
To evaluate ADP's effectiveness in training across diverse data sources, we utilize comprehensive collection of 13 agent training datasets, spanning coding, SWE, AP1/tool user, and browsing, as

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documented in Table These datasets represent broad spectrum of heterogeneity challenges that ADP addresses, including varied data creation methodologies (synthetic generation, manual curation, agent rollouts), different complexity (from simple complex multi-step workflows), and diverse environments (command-line interfaces, web GUIs, Jupyter Notehooks, API calls).
The selected datasets collectively contain over 1.3M instances, ranging from smaller ones like Mind2 to larger-scale ones like Orea AgentInstruct. To ensure balanced representation across domains and prevent any single large dataset from dominating the training process, we subsample from larger datasets while using smaller datasets in their entirety. Full details of our data sampling and mixture weights are Appendix
We use Qwen2.5-Coe models, with agent frameworks for comprehensive evaluation across multiple benchmarks. We fine-tuned all models using the same SFT pipeline from LLaMA--aatory (Zheng et al., 2024b). These experiments focus on each framework's s specialized domain to demonstrate targeted effec- tiveness. Each agent has unique architectures, tool interfaces, and interaction environments. This diversity allows us to validate that ADP-standardized data can be readily and easily converted to different agent formats, demonstrating the protocol's utility across various agent implementations.
model family (Qwen Team 2024 Hui et al. 2024) as the base
OpenHands (Wang et al., (025) is an open platform for building generalist Al agents that operate like software developers: writing code, using command lines, and browsing the web. It provides sandboxed execution environments, tool coordination, and benchmark evaluation.
AgentLab DDrouin et al.) 2024 de Chezelles et al. 2025) is open-source framework for de- veloping, testing, and benchmarking web agents across diverse tasks, emphasizing scalability and reproducibility. It supports suite of evaluation benchmarks like WebArena and WorkArena.
SWE-Agent (Yang et al., 2024) introduces a custom Agent-Computer Interface (ACI) that enables language model agents to autonomously perform software engineering tasks by navigating code- bases, editing and running code, viewing files, and executing tests.
5.2 EVALUATION BENCIMARKS
We evaluated these agents across 4 benchmarks (based the availability of benchmark evalua- tion code and specialization of agents) that span different domains. This comprehensive evaluation demonstrates ADP's expressiveness in preserving critical information across diverse tasks.
SWE-Bench (Jimenez et al. 2024) evaluates agents on real-world software engineering tasks. Given Github codebase and a bug report, agents must generate patches that satisfy existing unit tests. We used the SWE-Bench Verified subset for evaluation (Chowdhury et al. 2024).
WebArena (Zhou et al. 2024) provides realistic, self-hosted web environment composed of fully functional websites in domains like e-commerce, forums, and map navigation, requiring agents to interpret high-level natural language commands and perform concrete web interactions.
AgentBench (Liu et al., 2024b) evaluates agents across different environments, such as operating systems, databases, and web browsing. It emphasizes multi-turn reasoning, decision making, and adaptability across domains.
GAIA Mialon al. 2023) is benchmark for general AI assistants featuring human-annotated tasks that combine reasoning, tool use, and multi-step problem solving, often with multimodal input. Tasks vary in difficulty by number of steps and required tools.
6 EXPERIMENTAL RESULTS
6.1 ADP DATA RESULTS IN HIGHLY EFFECTIVE AGENTS ACROSS DIVERSE TASKS
ADP fine-tuning consistently improves performance across models, benchmarks, and agent harnesses. As shown Table 3 Table 4 and Table 3 training on standard- ized ADP data yields substantial gains across 7B. 14B, and 32B models several popu- lar evaluation benchmarks. On SWE-Bench (Verified), ADP training delivers remarkable im- improves from 0.4% to 20.2% (+19.8%)
provements: 5-1n--2------
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Table 3: Comparison of SOTA and our Best 7-8B ADP-trained agents' results across benchmarks. Shaded rows are our ADP-tuned models. Other rows are collected from previous works.
Agent
Model
Training Data
Accuracy
Bench (Verified) Jimenez et al.
2024 Chowdhury et al. 2024
SWE-Agent
Owen-2. 2eannn2.-11000 1o------Co----- -nst ruct
0.4%
YYngg al. 2024
Owen 2.5 7B Coder Instruet
SWE-smith Yang et al 2025b
15.2% (+14.8%
Claude 3 Opus (Anhhropic Team
15.8%
Owen-2. Coooooor--------11ar
ADP Data
20.2% (+19.8%)
OpenHands Agennt
Cwen-2. Co---rrdrr-rttteee Owen 2.5 75 Coder Instruct
2.8%
(Wngg et 2025
SWE-Gym (ann et al. 2025 ADP Dan
10.6% (47.8%)
Qwen-2.5-78 7g-Code Instruet
20.4% (+17.6%%
rena (hhow ot at. 2024
BrowserGym
Llama 3.1 8B Wwen-2.5-7BInnee 1lama-3.1-88 Men-2.5-755-nsstrun
1.0%
de Chezelles 2025
8.3%
NNv Murry al. 2024
16.3% (+15.3%)
Go-Bresse (andhii Nethin 2025
21.7% (+1.3.4%)
AgcntLab (Drouin et (de Chezelles at am 2025
2024
Qwen 7B Coder Instruct
4.5%
2wen-2.5-78-0 78----- Instruct
ADP Dala
21.0% (+16.5%)
AgouBeech OS (Li et al. 2024b
AgentLM
Llama chat 73
8.3%
(Lu et al 2024b
Llama-2 -lama-2-chat-73
Agentinstruct Zeng et al 2023
17.4% (+9.1%)
Open Aggent
3.5%
(Wang et
2025
27.1 (+23.6%%
GAIA (Mialon al. 2023
OWL Agent [u al. 2025
20en-2.5-78-Inte
4.8%
OpenHands CodeAAtAgent
7.3%
(Wang et 2025
Owen 2.5 75 Instruct
ADP Data
9.1% (+1.8%)
Table 4: Comparison of SOTA and our Best 13-14B ADP-trained agents' results across benchmarks. Shaded rows are our ADP-tuned models. Other rows are collected from previous works.
Agent
Model
Training Data
Accuracy
SWE-Bench (Verified) Jimenez et af.
2024
Chowdluury et al. 2024
SWE-Agent
Inst Claude 3.5 Sonnet Ammhropic Team
Qwen-2. 2.5 5-------------
2.0%
Yang al. 2024
33.6%
1oen-2.5-14R-0000 TTst ruct
ADP Data
34.4%% (+32.45)
OpenHands CodeActAgent
Qwea 2.5 14B Coder Inatruct (wea-2.5-14B-oodee
5.8%
(Wang et 2025
SWE-Gym (Pn
2025
16.4% (+10.6%)
ADP Dala
30.6% (+24.8%)
Weistrena Zhou et al.
2024
Agentl ah Droain et al
2024
Owan-2.5-14PM tntt
5.5%
(de Chezelles
2.5-14B--0- woder-ddeee--o.ooo Instruct
ADP Data
22.2% (+16.7%)
AgenBBench OS Lit et
2024b
AgeniL.
tlama-2-chat-11
9.0%%
(Liu et 2024b
1lama-2-chat-138
AgentInstruct
Zeng et al. 2023
(49.19)
OpenHands CodeActAeett
Qwea-2.5-142 Inntructt Qwen-2.5-14B Coder Instruct
2.8%
(Wang et al. 2025
ADP Data
20.8% (+18.0%)
with SWE-Agent and from 2.8% 20.4% (+17.6%) with OpenHands. At 14B scale, Qwen-2. 1-w.5---o1---e-1nen1 Inst ruct achieves 34.4% (+32.4%) with SWE-Agent and 30.6%
(+24.8%) with OpenHands. The 32B model reaches 40.3% (+38.1%) with SWE-Agent and 36.8%
(+26.2%) with OpenHands, matching or exceeding Claude 3.5 Sonnet with SWE-Agent's 33.6% performance. On WebArena, ADP training shows consistent gains across model sizes: 7B achieves 21.0% (+16.5%), 14B reaches 22.2% (+16.7%), and 32B attains 22.9% (+12.0%). On AgentBench OS, the improvements are substantial: the 7B model improves from 3.5% to 27.1% (+23.6%), the 14B model improves from 2.8% to 20.8% (+18.0%) and 32B models from 27.8% to 34.7% (+6.9%) Finally, on GAIA, the 7B model improves from 7.3% to 9.1% 1 (+1.8%).
These gains, spanning both coding and browsing settings, show that unified, cross-domain ADP training corpus can deliver SOTA or near-SOTA performance without domain-specific tuning and is effective across models, action spaces, and agent harnesses. Figuree and Figure 4 also show clear
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Published as a conference paper at ICLR 2026
Table 5: Comparison of SOTA and Best 32B ADP-trained agents' results across benchmarks.
Shaded rows are our ADP-tuned models. Other rows are collected from previous works.
Agent
Model
Training Data
Accuracy
BWEcBench (Vérified) (Jimenez et ol. 2024
Chowdhary et al. 2024
SWE--ppnt
Owen-2.5- (nnt:---oder--ntttttee 5-3n-22--32B-oodee
2.2%
Yang 2024
SWE-smith Yang al.
2025h
20.2% (+3880%)
Toen-2-5-328-0tderr-1
ADF Dala
40.3% (+38.1
OpenHands AcAent
Swen-2..5-22--0cde---nn
10.6%
WWnn et 2025
Gym
(+10.0%)
ADP Data
36.8% (+26.2%)
WehAnona Zhou et al.
2024
AgentLab Lb Drouin al.
2024
10.9%
Chezelles
Swen-22-5-328--0dde--111
(+12.0%)
AgeniBench OS Lia
A
Llama-2-chas-702
9.0%
[Liu et al. 2024h
blama-2-cha--700
AgentInstruct (Zegg et al. 2023
21.5% (+12,5%)
OpenHands Actennt
5wen-22.5322--Crde---ta Instrrr---ntrttt
27.8%
Wang et al. 2025
Qwen men-22.5328- 300-32BBCooo -BB-er Instract
ADP Data
34.7% (46.9%)
monotonic gains with model size and consistent boosts from ADP training across agents and tasks, with ADP-trained models outperforming their base counterparts at every scale.
6.2 DIVERSE DATA RESULTS IN CROSS-TASK TRANSFER
Table 6: Cross-task transfer with diverse task-specific data. For each benchmark, compare the same made under task-specific "only" tuning and training on ADP corpus.
Agent
Model
Training Data
Accuracy
SWE- BWecBench (Verified) (Jinenez al.
2024 Chowshurr 2024
OpenHands CodeActAgens
Owen 2.5 7B Instruct
SWE-smith Only
1.0%
Wang et al. 2025
Qwen-2.5-3 5-BB-IInnstruc
ADP Data
10.4%
Qwen-3-88
CodeActlinsrucc + Code-Feedback
0.2%
Qwen-3-83
SWE-smith Only
11.0%
Owen 8-83
ADP Data
16.6%
renArena Zhou al.
2024
Lab Drouin et al.
2024
Owen 2- 10--7tnnnntrtt
Go-Browse Only ADP Data
Chezelles et al, 2025
Qwen-2.5-7B-
20.1
AgentBench OS (Liu et al.
2024b
OpenHunds CodeActAgent
Cwen 3-83
AgentInstruct Only ADP Data
21.5%
WWan al. 2025
Cwen 3-83
25.7%
GAI (Midon et al. 2023
OpenHands CodeActAgent
Qwen-2 2. Thst ruct Owen 2. 11--nsruuct
AgendIntrruct Only ADP Data
0.6%
(Wan al. 2025
9.1 %
We study whether data diversity helps agents generalize across tasks. Holding the agent setup and evaluation fixed, we compare training with different data mixtures: (i) Base (no tuning), (ii) Task- specific only fine-tuning (e.g., SWE-smith Only, etc.), and (iii) ADP Data (as detailed in $ 5). mixed, cross-domain corpus. As shown in Table 6) ADP consistently outperforms task-specific tuning the target task and, critically, avoids the negative transfer that single-domain tuning often induces on other tasks (Mueller et al. 2024 Kotha et al. 2024 Li al. 2024).
Concretely, SWE-Bench, ADP trained Qwen-2.5 7B Inst ruct achieves 10.4%, versus 1.0% with SWE-smith Only; for Owen-3-82 (Yang et al.] (025a)), ADP reaches 16.6% versus 0.2% with CodeActInstruct Code- Code-Feedback and 11.0% with SwE-smith Only. On WebArena, ADP trained Owen-2 2. 5- 7----B--nntttt AgentBench OS, ADP lifts Qwen 3-8B to 25.7% % versus 21.5% with AgentInstruct Only. On GAIA,
ruct ruct attains 20.1% versus 16.0% with Go-Browse Only. On

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Published as a conference paper at ICLR 2026
AgentInstruct Only results in 0.6% accuracy, while ADP improves it to 9.1%. Overall, mixed ADP tuning yields stronger in-domain accuracy and cross-task generalization than single-domain tuning.
6.3 ADP EASES ADAPTATION TO NEW AGENT HARNESSES
Table 7: LOC for converting datasets to ADP.
Table demonstrates the lines of code (LOC the authors and community contributors used convert 13 datasets from distinct sources to the ADP schema. A single Raw->DD converter per dataset performs the same nor- malization work (schema mapping, tool/action alignment, conversation formatting) that a tra- ditional Raw-SFT converter would do for specific agent harness. Therefore, LOC statis- ties in Table 7 are reasonable proxy for the per-agent harness effort without ADP.
Dataset
Total LOC
AgentInstruct
~ 1500
Code-Feedback
134
Actessttntttutt
269
Go-Browse
335
Mind2Web
476
Nebius SWE-Agent Trajectories
260
NNetNav (live+wa)
290
openhands-feedback
879
Orca AgentInstruct
155
SWE-Gym
221
Without ADP. Using this proxy, the cost of converting D-many datasets to A-many har- nesses without ADP is Costmo-ADPPA, D)
SWE-smith
228
Synatra
145
Total
4892
El LOC RDw Thus the total con- version cost across the community is quadratic (O(D A) effort), as depicted in Figure In our data, El LOC; ADP 4892 LOC across 13 datasets, for example for A 100 harnesses the total cost is COS ~ 100 x 4892 = 489, 200 LOC.
With ADP. The total cost becomes COSTADe D) se == LOCi,Ri-ADD + Σ LOCADE-SFT. with ADP. Thus, as shown in Figure 2. the total conversion cost across the community now becomes linear with ADP (O(D + +A) effort). [Tall demonsttaates that con- verting ADP standardized data to agent harness format takes an average of 77 LOC. Across the 13 we used, COSt D) 4892 77 100 12, 592 for A = 100, greatly less than the no-ADP setting, Additionally, adding a new harness only require writing one script converting ADP standardized data to SFT, greatly easing adaptation new agent harnesses. Hence, ADP substantially reduces the community's collective effort required to de- velop scalable, reproducible agents.
Table 8: LOC for ADP SFT converters.
Agent Haresss
Total LOC
OpenHands CodeActAgent
155
SWE-Agent
50
AgentLab
30
Average
777
CONCLUSION AND FUTURE WORK
ADP provides a practical, lightweight "interlingua" that unifies heterogeneous datasets into a sin- gle schema consumable by many agent harnesses, turning today's fragmented data landscape into scalable training pipeline. Looking ahead, we see three immediate directions.
(i) Multimodality: extending ADP beyond text to images, screen recordings, and other modalities to capture richer agent-environment interactions.
(ii) Standardized evaluation: applying the same standardized "protocol" idea to evaluation and environment settings so that datasets, agents, and evaluations com- pose cleanly. (fii) Community growth and data quality: continuing open-source releases, stronger automated validation even automated dataset conversion, to sustain scale while preserving qual- ity. We believe that, by lowering integration costs and enabling systematic and scalable training and analysis across sources, ADP can catalyze the next wave of agent-training research and practice.
REPRODUCIBILITY STATEMENT.
We provide clear pointers to enable independent reproduction of all results. We describe the ADP schema and conversion pipeline 3). allowing others to regenerate the training corpus from raw sources. We list the datasets and their characteristics in 2.1 The exact training and evaluation
All LOC exclude prompt (e.g., system prompts); only converter code is counted.
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Published as a conference paper at ICLR 2026
setup-including base models, agent harnesses, SFT pipeline, the evaluation benchmarks and protocol-is specified in s 5) Finally, we release all code and data open source, including the ADP schemas, converters, and scripts referenced above.
ACKNOWLEDGEMENT
This work was supported in part by grant from Fujitsu. The training is supported by the CMU FLAME Center. The authors thank Professor Daniel Fried for his insightful feedback. The authors thank NeuLab members and participants of the LTI ICLR Paper Clinic for helpful suggestions.
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoe Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awal- gaonkar, Rithesh N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, and Caiming Xiong. xLAM: A family of large action models to em- power AI agent systems. In Luis Chiruzzo, Alan Ritter, and Lu Wang (cds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computa- tional Linguistics: Human Language Technologies ( (Volume I: Long Papers), pp. 11583-11597, Albuquerque, New Mexico, April 2025. Association for Computational Linguistics. ISBN 979- 1-89176-189-6, doi: 10.18653/v1/2222 http:/225..aaa--loog55777777 URL org/2025. naacl .
Tianyu Zheng. Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. OpenCodeenteeppreeer: Integrating code generation with execution and refine- ment. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Findings of the Associa- fion for Computational Linguistics: ACL 2024, pp. 12834- 12859, Bangkok, Thailand, August 2024a. Association for Computational Linguistics. doi: 10. 10.1633/11/2024 findings-act.762. URL https: www.aanttoooooy.orr//22222244
wttp:///00222 findings acl. 762/
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Pro- ceedings of the 62nd Annual Meeting of the Association for Computational Linguisties (Volume 3: System Demonstrations), Bangkok, Thailand, 2024b. Association for Computational Linguis- abs 13372
tics. htlhttpp//arxiii
Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo. Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig. Webarena: A real- istic web environment for building autonomous agents. In The Twelfth International Confer- ence on Learning Representations, 2024. URL https: https:://ppezzeview net/ wttpr//wwwwwe.eeeeooouuummeeeeee forum?id oc6ytt
A USE OF LLMS
We used LLMs aid and polish writing for style and presentation. Specifically, LLMs were employed to:
polish wording, tighten paragraphs, and improve clarity/ftow;
improve latex presentation (e.g., table/figure captions)
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B ADP EXAMPLE CONVERSION
The conversion pipeline: Raw - ADP + SFT enables scalable training across diverse agent archi- tectures while maintaining data provenance and semantic structure.
This section demonstrates an example conversion from raw agent interaction data of the Code Feed- back dataset (Zheng al. 2024a) the standardized ADP format. The transformation process extracts structured observations and actions from raw conversation between the user and the agent.
B.
Raw FORMAT EXAMPLE
The raw format typically contains conversational messages with roles and content:
Listing I: Raw Format Example from Code Feedback
"id": 13461, "messages":
"role": "user", "content"; "Write Python script to calculate statistical measures..."
9
" "role": "assistant",
II
"content": "You'rr re correct. Let me optimize the code... \n 'pyyhonn'iimppa math'nindef calculate_statisticc (x, : Implementation details... return coefffiiinn,, rejusted___squared
r s_squared, Antt
12
13
14
"role": "user",
"content"; "Execution result: \nR-squared: 0.6) nCorrelation: 3. 3.87\xadfusted R-squared: 0.47"
17
19
B.2
STANDARDIZED ADP FORMAT EXAMPLE
The standardized format structures the same interaction into typed observations and actions:
Listing 2: Standardized ADP Format Example
3
"id": "13461", "content";
"class_" "; (text_observationn
"content": "Write Python script to calculate statistical measures...",
"name": null,
9
"source"; "user"
11
" "class_ ": "code_action"
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" "language": "python",
14
"coteett" "import mathla\ndef galculate_sratisticn (x, y)
in # Implementation details... return r_squard,,
adjusted_r_squaret
15
"description"; "You' re correct. Let me optimize the code by calculating values once and reusing them..."
16
18
"class_" ": "text_observation"
19
" "content"; "R-squared: 0.6 Annorrrelato 3.87snedjs R- squared: 0.47", "name": null,
20
"source"; "environment
22
24
"class_": "nessage_action", " "content"; "<finish> The code executed successfully with statistical results...",
"description": null
27
},
"details"; 0
30
The conversion process applies several key transformations:
Raw messages are classified into observations and actions based on Message Classification:
content analysis.
Code Extraction: Code blocks within assistant messages are extracted as code_action entries. "user",
Source Attribution: User inputs become wexttooseevatiine with source: execution results with source: "environment"
Thought Preservation: Original function thoughts are preserved in description fields while structured contents are extracted.
Action Classes: Different classes of agent actions (code execution, messaging, tool usage) are explicitly categorized
This standardization enables systematic analysis of agent behaviors, tool usage patterns, and inter- action dynamics across different agent implementations and domains.
B.3 SFT FORMAT EXAMPLE
The standardized ADP format can be further converted to training-ready formats for specific agent frameworks. Here's the example in OpenHands (Wang et al. 2025) SFT format:
Listing 3: OpenHands SFT Format Example
2
"id": "13461", "conversations"; [
5
"from": "human",
7
"value": "Write a Python script to calculate statistical measures..."
8
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" "from": "gpt",
11
"value": "You' re correct. Let me optimize the code... Anine
function=execute ieyttppttteeeeeeeee col__lllaaaaaaatttrrrreeee
nimport math\n\ndet calculatesstatiisiicss :):hn
Implementation details... return r_squared, correlation_coefficchi paraneter>\o</Eunctionn
12
14
" "from": "human",
15
"val ue": "EXECUTION RESULT of (exeaute_iyythonccell AnR- R squared: 0. 6''oorrrllaainn 3.87\nAdjuster R-squared: 47"
17
" "from": "gpt", "value": "<cmmcccoor.iaaaaatrrrrreseerees code executed successfully with statistical results... An</ parameter>\n<//inetioo>>
19
},
"system": "You are OpenHands agent, helpful AI assistant..."
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C DATA SAMPLING FOR BALANCED TRAINING
To balance domains and reduce over-represented sources, resample each dataset with per- dataset multiplier wa. For dataset with na raw trajectories, we draw my = [ na] examples per epoch; if wa 1 sample without replacement (downsample), and if wa 1 we sample with replacement (upsample). This yields an effective mixture proportional to wy across datasets (and therefore across domains), while keeping the overall epoch size stable.
Table 9: Per-dataset sampling multipliers wa. we indicates downsampling: wa > indicates upsampling.
Dataset
Wa
Direction
Sgentcuninggalffnen fworld
2
up
agentcuning db
2
up
agenttuning.kg
2
sgenttuniigg.iindweee
2
agentcuning.os
2
up
agent:uningg.webshog
2
up
codo_feedback
0.1
down
codcactinstruct
1
neutral
go-browsa-wa
1
neutral
mind2web
1
neutral
nebius.SWE
0.2
down
nnetnav-live
I
neutral
nnetnav-wa
1
neutral
openhands
1
neutral
orca.agentinstrruct
0.001
down
bee-ghn.eeeehandddaaaaaeeeeeeeee swe-smith synatra
3
up
I
neutral
0.01
down
In practice, we fix random seed for reproducibility and shuffle the union of sampled examples across datasets each epoch. This scheme targets more balanced distribution
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coding, SWE, tool-use, and web-browsing sources by attenuating very large corpora (e.g.. orca_agentinstzuct at wy=0.001) and amplifying under-represented ones (e.g,, swe-gym openhands sampled trajectories wy=3).
C.I DOMAIN- SPECCICC DATA FILTERING
Beyond balanced sampling, we apply domain-specific filtering to optimize training effectiveness for each agent framework based their evaluation focus and capabilities.
OpenHands and SWE-Agent Training Data. For OpenHands CodeActAgent and SWE-Agent, which are primarily evaluated on coding and software engincering tasks (SWE-Bench, AgentBench OS, and GAIA), we use only the non-web portion of the ADP training corpus. This includes datasets focused on code generation, software engineering, general agent instruction following, and API/- tool usage. Specifically, we exclude web browsing datasets Mind2Web, Go-Browse, NNetNav, and Synatra to avoid potential interference from web-specific interaction patterns that are not applicable to command-ling and coding environments. Thus, using the sampling multipliers in Table 9 the total number training samples used around 30K. Future experiments could explore different sampling multipliers and examine the effect of each dataset coding and software engineering tasks.
AgentLab Training Data. For AgentLab, which is designed for web browsing tasks and evalu- ated exclusively it on WebArena, we use only the web portion of the ADP training corpus. This in- cludes datasets focused on web navigation, browser-based task completion, and web-specific agent instruction following (Mind2Web, Go-Browse, NNetNav, and Synatra). We exclude coding and software engineering datasets to ensure the model is optimized for web browsing patterns and UI element interaction without dilution from less compatible domains. Thus, using the sampling multi- pliers Table the total number of training samples used is around 20K. Future experiments could explore different sampling multipliers and examine the effect of each dataset on web tasks.
D PERFORMANCE SCALING
AD nel Modes
Mosth
SAT-Agem: (SWE-Banet)
Operiiance (SW5-)ench)
60-
SWE-Agent 407 (SWE-Sench)
Openflnnts ADP 15V Banch)
Agerttab
Agentiat (Wesstenss)
22.%%
Agentiab Basn (VebAArena)
Nocel Sae tont Paramenen))
328
Figure 3: Performance Scaling Across Agents and Benchmarks (Base ADP Trained)
Figure 4: Performance Gains Across Agents and Benchmarks.
Figure and Figure 4 shows the scaling curve of performance and performance gains across agents and benchmarks. Both plots show clear monotonic gains regardless of model size and consistent boosts from ADP training across agents and tasks, with ADP-trained models outperforming their base counterparts at every scale.
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E ADDITIONAL EXPERIMENTS
E. ADP's ADVANTAGE PERSIST UNDER EQUAL DATA SCALE
To address the question of fair data scaling, additionally compare ADP against single-domain fine-tuning baseline under matched dataset size. Specifically, we train Qwen-33 3-88 8B on SWE-smith with up-sampling to match the number of training examples used the ADP mixture, and evaluate both models on WWE-Bench with the OpenHands harness. As shown in Table [O] SWE-smith training yields 1.00% accuracy, whereas ADP training achieves 16.6% under a comparable number of samples. This demonstrates that ADP's benefit does not stem from data volume alone, but from the greater diversity and unified structure the ADP corpus.
Table 10: Equal-scale comparison of Qwen --BB trained SWE-smith ADP, evaluated SWE-Bench with the OpenHands harness.
Model
Training Data
Data Scale
Accuracy
Qwen3-83 Qwen-3-83
SWE-smith sampled)
2 30K
11.0%
ADP
30K
16.6%
F LICENSE OF USE
This section provides licensing information for all datasets referenced in Table | and used in our experiments. We have made every effort identify and respect the licensing terms of each dataset. Users should verify current licensing terms before using these datasets. Users should also verify the licensing terms of datasets they are adding to ADP.
F.1 DATASET LICENSES
Table 11: Licensing information for datasets used in ADP
Dataset
License
Link
AgentInstruct
Apache 2.0
ZhipuA//AAentInssru
Code-Feedback
Apache 2.0
Do-aa-/CCoe--eeddiee
CodeActInstruct
Apache 2.0
Xingyaoww/code-act
Go-Browse
MIT
ApGa/
Mind2Web
CC BY 4.0
osunlp/Mind2Web
Nebius SWE Trajectories
CC BY 4.0
nebuus//WE-agentttrraae
NNetNav-live
Apache 2.0
stantordnll/nneetaav-rve
NNetNav-wa
Apache 2.0
stantordmilp/mnetavvv-wa
openhands-feedbcck
MIT
all-Hands/peemaands-tccccaakkk
Orea Agentinstruct
2DLA--ermissive-.00 MIT
micrssotiorc--ggnntsttuuu----
SWE-Gym
SWE-Gym/SWEEGGym
SWE-smith
MIT
Synatra
CC BY-SA 4.0
cotlyy/Synatra
License Compliance: We have ensured compliance with licenses of all datasets utilized in this paper. All licenses permit research use.
F.2 USAGE GUIDELINES
When using the ADP-converted versions of these datasets:
Verify Current Licenses: Check the original dataset repositories for the most up-to-date licens- ing terms
Respect Restrictions: Some datasets have restrictions on commercial use, redistribution, or specific use cases.
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3. Cite Appropriately: Include citations for both the original datasets and the ADP conversion methodology.
Contact Authors: For datasets with unclear licensing, contact the original authors for clarifica- tion on usage terms.
F.3 DISCLAIMER
Licenses were collected at the time of dataset integration and may have changed. Users are respon- sible for verifying current licensing terms and ensuring compliance with all applicable licenses. The ADP project does not assume responsibility for license violations by downstream users.
For questions about specific dataset licenses usage permissions, please contact the original dataset authors or maintainers directly.
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Reviews:
[{"review_id": "C25Qa2wcYt", "reviewer": "Reviewer_QXMH", "summary": "This paper presents the Agent Data Protocol (ADP), a unified and extensible framework for representing and exchanging multi-agent data in LLM-based systems. ADP standardizes key concepts—such as tasks, agents, trajectories, and scores—into a cohesive schema, enabling interoperability across platforms and supporting tasks like agent analysis, fine-tuning, and benchmarking. Through real-world use cases, the authors show that ADP simplifies data handling and fosters reproducible, collaborative research in the agent ecosystem.", "questions": "**Relation to Prior Work on Unified Agent Data Frameworks**  \n   The paper introduces ADP as a unified schema for agent data, but does not sufficiently discuss its relationship to prior work on similar frameworks, such as *AgentOhana* (Zhang et al., 2024), which also proposes a unified data and training pipeline for agent learning. Could the authors clarify the conceptual and technical differences between ADP and AgentOhana? Specifically, how does ADP improve upon or diverge from previous unification efforts in terms of schema design, data transformation capability, or supported use cases?\n\n[1] Zhang, Jianguo, Tian Lan, Rithesh Murthy, Zhiwei Liu, Weiran Yao, Ming Zhu, Juntao Tan et al. \"Agentohana: Design unified data and training pipeline for effective agent learning.\" arXiv preprint arXiv:2402.15506 (2024).", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Standardized and Extensible Data Schema**  \n   The paper introduces a well-structured, unified schema that captures essential components of agent-based interactions (tasks, agents, trajectories, and scores), addressing long-standing fragmentation in agent data representation.\n\n2. **Practical Utility Across Diverse Agent Systems**  \n   ADP demonstrates strong real-world applicability by enabling seamless data sharing and transformation across different platforms, toolchains, and evaluation pipelines, which is crucial for scalable multi-agent benchmarks and model comparison.\n\n3. **Empirical Validation Through Real Use Cases**  \n   The usefulness of ADP is effectively showcased through compelling use cases such as cross-agent analysis, supervised fine-tuning, and multi-agent evaluation—providing concrete evidence that the protocol supports reproducible and collaborative agent research.", "weaknesses": "See questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "C1WnACpPou", "reviewer": "Reviewer_MLUC", "summary": "This paper proposes an agent data protocol to standardize various agent tuning datasets into a single format. The goal is to help the community better utilize available agent datasets and reduce the engineering effort required to use them for agent tuning tasks. To this end, authors have defined the ADP standard and converted 13 different agent datasets into this format to show the coverage of the proposed standard. Using this converted data, the authors fine-tune agent models and show that they perform better on various benchmarks, from coding/software engineering/tool calling, etc., demonstrating that this is indeed helpful.", "questions": "Please check the weaknesses section.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well written and easy to follow. \nHaving a data standard that can help various agent datasets into a single format would help the research community in this area to a great extent and can help with the reusability of the assets with ease.\nAdopting existing 13 benchmark datasets to the format and open-sourcing them for the community\nAnalysis of the various datasets after the conversion and fine-tuning results to show the power of having a standardized data format and the kind of generalization it can bring to the table.", "weaknesses": "Can authors comment on the SOTA numbers for various tasks with similarly sized models? I can see improvements for a selected model from its base performance. Are there any fune-tuned models in a similar parameter range that get better numbers than what is reported here with ADP data?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dEfqd8c9sk", "reviewer": "Reviewer_NLwX", "summary": "The paper observes that the current bottleneck in the development of LLM-based agents lies not in the shortage of data, but in the lack of a unified data standard. To address this, the authors propose ADP as an interlingua for diverse downstream tasks, unifying the formats of agent actions and environment observations. They convert 13 existing agent datasets into the ADP format and train models on this unified dataset. The results show that agents trained with ADP achieve significant performance improvements over the base models, and that models trained on mixed ADP data outperform those trained solely on single-task data in certain tasks.", "questions": "1. I am curious about the extent to which ADP can outperform non-unified data when the amount of training data is controlled to be equal.\n\n2. The ADP framework currently supports only TextObservation and WebObservation, which limits its applicability to certain interaction scenarios. In real-world scenarios, environment observations may be more complex (e.g., structured JSON data or game environment states). How does ADP handle or generalize to such out-of-scope observation types?\n\n3. Table 2 shows that most datasets have high coverage of Function Thought. However, the paper does not further analyze these \"thought\" processes. I wonder whether training with mixed ADP data not only improves the final action accuracy but also enhances the agent's reasoning quality.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper presents an excellent motivation, keenly identifying data fragmentation as a key engineering bottleneck in current agent research and providing a clear direction for future data standardization efforts.\n\n2. It provides a valuable contribution to the open-source community by integrating 13 diverse datasets into a unified ADP format and demonstrating the value of mixed data, thereby establishing a solid data foundation for building general agent capabilities.\n\n3. The experiments are conducted across multiple agent frameworks and consistently show performance improvements, highlighting the effectiveness and generality of the proposed ADP approach.", "weaknesses": "1. The experiments involve an unfair comparison. While the paper emphasizes the importance of unifying agent fine-tuning data formats through ADP, the comparative experiments do not use an equal amount of ADP and non-unified data. Due to the inconsistency in data scale, it is difficult to convincingly demonstrate the advantages of ADP over other data formats, or to support the claim that mixed data is superior to single-task data.\n\n2. The experiments are incomplete. Although the paper selects Qwen3-8B and Qwen2.5-7B-Instruct as baseline models, several tasks lack results for Qwen3-8B, which may raise concerns about the generality of the proposed approach.\n\n3. As a data-centric work, the paper does not provide a complete example of the ADP data format, making it difficult for readers to intuitively understand how ADP differs from existing data representations.\n\n4. The paper has several issues in presentation and formatting. For instance, some acronyms are not capitalized (e.g., \"agent data protocol\" in Line 015 should be capitalized), inconsistent capitalization appears throughout (e.g., Line 145), some abbreviations are introduced before their full forms (e.g., Line 245), and table captions are inconsistently formatted (Tables 3 and 4 have captions placed below, unlike others).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Hc1vlwVVEJ", "reviewer": "Reviewer_VUjH", "summary": "The paper proposes the Agent Data Protocol (ADP), a lightweight actions–observations schema that standardizes heterogeneous agent datasets into a common “interlingua.” Thirteen public datasets spanning coding, software engineering, tool use, and web browsing are converted into ADP, then compiled into multiple agent harnesses via a single ADP→SFT step. The authors report consistent SFT gains for 7–8B models on SWE-Bench Verified, WebArena, AgentBench OS, and GAIA, and argue that ADP reduces conversion effort from quadratic in datasets × harnesses to linear, supported by lines-of-code accounting. The work targets community-scale reuse by releasing schemas, converters, and a balanced training mixture.", "questions": "See weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Concrete unification with measurable engineering payoff. A minimal, well-scoped schema (APIAction, CodeAction, MessageAction; Text/WebObservation) plus validators collapses integration complexity from O(D×A) to O(D+A), with clear LOC evidence for both Raw→ADP and ADP→SFT paths.\n\nBroad empirical utility across agents and tasks. ADP-trained models improve over base models on four benchmarks and exhibit positive cross-task transfer, suggesting the protocol enables effective multi-domain SFT rather than task siloing.\n\nAdoption-oriented tooling. Bidirectional converters, automated quality checks, and a balanced sampling strategy make the proposal practical for other groups to plug in new datasets or harnesses.\n\nReusability and standardization value. A shared, open schema can reduce duplicated one-off converters and facilitate reproducible studies on agent data at scale.", "weaknesses": "- The unified data is not yet available. \n\n- Limited contamination and licensing analysis. Decontamination, deduplication across sources, license compatibility, and provenance controls are not documented sufficiently, which weakens confidence in reported gains.\n\n- Ablations do not isolate the protocol’s causal effect. There is no comparison against naïve harmonization baselines such as prompt-normalized concatenation, nor granular ablations on validators, sampling multipliers, or per-dataset contributions.\n\n- Incomplete statistical reporting. Variance across seeds, confidence intervals, and sensitivity to mixture weights are missing, making robustness unclear.\n\n- Schema coverage gaps. Current ADP emphasizes text, tools, code, and web HTML or AX trees; richer multimodality, GUI desktop state, error and rollback semantics, and environment replay metadata are not fully specified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)