Writing study: Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents

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← ICLR 2026 oral writing corpus

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 · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Datasets, benchmarks & measurement resources (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

21 physical pages

Before references / appendix

≈ 10.27 page-equivalents

Main figures / tables

2 / 8

Attached appendix figures / tables

2 / 3

Attached appendix span

≈ 6.19 page-equivalents

Before references / appendix: ≈ 48.9%References / intervening material: ≈ 21.62%Attached appendix: ≈ 29.48%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	1	≈ 1.17 pp.	5.57%
2 RELATED WORK	2	≈ 1.37 pp.	6.52%
3 THE AGENT DATA PROTOCOL	4	≈ 2.22 pp.	10.57%
4 CROSS DATASET ANALYSIS	6	≈ 0.41 pp.	1.95%
5 EXPERIMENTAL SETUP	6	≈ 0.96 pp.	4.57%
6 EXPERIMENTAL RESULTS	7	≈ 2.84 pp.	13.52%
7 CONCLUSION AND FUTURE WORK	10	≈ 0.63 pp.	3%
REFERENCES	11	≈ 4.54 pp.	21.62%
A USE OF LLMS	15	≈ 0.3 pp.	1.43%
B ADP EXAMPLE CONVERSION	16	≈ 2.37 pp.	11.29%
C DATA SAMPLING FOR BALANCED TRAINING	18	≈ 1.08 pp.	5.14%
D PERFORMANCE SCALING	19	≈ 0.55 pp.	2.62%
E ADDITIONAL EXPERIMENTS	20	≈ 0.29 pp.	1.38%
F LICENSE OF USE	20	≈ 1.61 pp.	7.67%

All detected figure and table locations

Main text

• Figure 1 · p. 2
• Table 1 · p. 3
• Figure 2 · p. 6
• Table 2 · p. 6
• Table 3 · p. 8
• Table 4 · p. 8
• Table 5 · p. 9
• Table 6 · p. 9
• Table 7 · p. 10
• Table 8 · p. 10

Attached appendix

• Table 9 · p. 18
• Figure 3 · p. 19
• Figure 4 · p. 19
• Table 10 · p. 20
• Table 11 · p. 20

Argument and organization

1. Schema and conversion pipeline

   Defines reusable actions and observations before describing dataset integration.

2. Training and evaluation

   Separates downstream utility, cross-task transfer and converter effort.

3. Conversion and sampling appendices

   Makes the abstract protocol operational through executable-looking examples and mixture details.

Problem, claim and contribution

The introduction reframes data scarcity as a format-interoperability bottleneck. Its contribution is a reusable representation plus converters and a corpus, not a novel agent reasoning algorithm.

§1, pp.1–2 · Main paper

Argument and section order

The multiplicative-to-additive conversion argument is an intelligible systems motivation. The later lines-of-code comparison is a proxy for adapter effort, however, rather than a measured universal engineering-time reduction.

§3 and Figure 2, pp.3–6; §6.3 and Tables 7–8, pp.9–10 · Main paper

Figures, captions and visual explanation

Figure 1 moves from a high-level conversion pipeline to concrete action and observation objects using consistent colors. Readers can connect an interaction example to its typed schema before encountering implementation details.

Figure 1, p.2 · Main paper

Tables, comparisons and evidence

The equal-example-count appendix helps address the confound between mixture quality and dataset size: approximately 30K examples yield 11.0 versus 16.6 SWE-Bench accuracy. It still does not isolate schema choice from data diversity or establish matched token-level compute.

Appendix E.1 and Table 10, p.20 · Main paper

Appendix and reproducibility

Raw, normalized and harness-specific listings expose the actual transformation. Sampling multipliers and domain exclusions reveal that the 1.3M-trajectory release is not the training size: runs use about 30K non-web or 20K web examples.

Appendices B–C, pp.16–19 · Main paper

Clarity and writing conventions

Absolute improvement must be labeled in percentage points. Figure 4 also does not show monotonically increasing gains for every curve: the WebArena delta decreases at 32B. Licensing and LLM-use disclosures are explicit and reusable presentation examples.

Figure 4, p.19; Appendix A, p.15; Appendix F, pp.20–21 · Main paper

What to reuse—and what not to infer

Transferable lesson: For data-infrastructure papers, pair the interface abstraction with a worked conversion, provenance, actual sampling policy and downstream controls; state what the effort proxy does and does not measure.

Boundary: Validation of a schema does not certify trajectory correctness or privacy. Mixed-data gains are not a causal test of standardization alone, and released-corpus size must not replace the much smaller training-mixture sizes.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

pp.1–10, abstract and §§1–8; reproducibility and references pp.10–15

Appendix / supplement

pp.15–21, appendices A–F including conversion listings, sampling, equal-scale experiment and licensing

Visually inspected PDF pages

2, 19

• official ICLR 2026 proceedings PDF · 21 pages