Writing study: Token-Importance Guided Direct Preference Optimization

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← ICLR 2026 oral writing corpus

Ning Yang, Hai Lin, Yibo Liu, Baoliang Tian, Guoqing Liu, Haijun Zhang · 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: Learning & optimization algorithms (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

23 physical pages

Before references / appendix

≈ 10.48 page-equivalents

Main figures / tables

4 / 2

Attached appendix figures / tables

2 / 13

Attached appendix span

≈ 9.6 page-equivalents

Before references / appendix: ≈ 45.57%References / intervening material: ≈ 12.7%Attached appendix: ≈ 41.74%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	≈ 1.12 pp.	4.87%
2 RELATED WORK	2	≈ 0.83 pp.	3.61%
3 PRELIMINARIES	3	≈ 0.92 pp.	4%
4 METHODOLOGY	4	≈ 3.26 pp.	14.17%
5 EXPERIMENTS	7	≈ 3.02 pp.	13.13%
6 CONCLUSION	10	≈ 0.39 pp.	1.7%
ETHICS STATEMENT	11	≈ 0.15 pp.	0.65%
REPRODUCIBILITY STATEMENT	11	≈ 0.12 pp.	0.52%
ACKNOWLEDGMENTS	11	≈ 0.1 pp.	0.43%
REFERENCES	11	≈ 2.92 pp.	12.7%
A THEORETICAL PROOF	14	≈ 2.84 pp.	12.35%
B ADDITIONAL EXPERIMENTAL RESULTS	17	≈ 3.54 pp.	15.39%
C CASE STUDY	20	≈ 1.82 pp.	7.91%
D LIMITATIONS AND FURTHER DISCUSSION	22	≈ 1.39 pp.	6.04%

All detected figure and table locations

Main text

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

Attached appendix

• Table B1 · p. 17
• Table B2 · p. 19
• Table B3 · p. 19
• Table B4 · p. 19
• Table B5 · p. 19
• Table B6 · p. 19
• Table B7 · p. 20
• Table B8 · p. 20
• Table B9 · p. 20
• Table B10 · p. 21
• Table B11 · p. 21
• Figure B1 · p. 21
• Table B12 · p. 22
• Table B13 · p. 22
• Figure B2 · p. 22

Argument and organization

1. Introduction, related work, preliminaries

   Motivate token-level preference weighting and introduce DPO and triplet objectives.

2. Methodology

   Build from gradient attribution and a positional prior to the weighted objective, anchor generation, and pseudocode.

3. Experiments and appendices

   Pair comparisons with weighting/triplet ablations, qualitative examples, and derivations.

Problem, claim and contribution

Two claimed gaps—token-importance estimation and coarse preference supervision—map directly onto the two proposed components.

Introduction, pp. 1–2 · Main paper

Argument and section order

The method exposes intermediate quantities before presenting the combined objective and training loop, making component boundaries easy to follow.

Sections 4.2–4.4, pp. 4–6 · Main paper

Figures, captions and visual explanation

Color-highlighted preferred, intermediate, and rejected answers make weighting understandable without relying solely on equations; the display should not be confused with a validated causal explanation.

Figure 3, p. 9 · Main paper

Tables, comparisons and evidence

The ablation separates uniform/random weights, prior choice, and triplet removal, but its abstract dimension labels obscure comparison with the named benchmark tables.

Table 2, p. 9; Tables B5–B7, pp. 19–20 · Main paper

Appendix and reproducibility

The supplement provides per-model scores, noise and diversity checks, weight examples, and an explicit training-overhead discussion.

Appendices B–D, pp. 17–23 · Main paper

Clarity and writing conventions

The conclusion's universal-sounding superiority language is stronger than mixed per-task scores and conditional arguments warrant.

Section 6, p. 10; Appendix A.2–A.3, pp. 14–16 · Main paper

What to reuse—and what not to infer

Transferable lesson: Map each methodological motivation to a named component and a corresponding ablation, while keeping benchmark labels and training protocol visible in every result table.

Boundary: Theorem 2 replaces a Taylor expression by an approximation before asserting an inequality and omits the added triplet term; it should not be repeated as an established guarantee. Gradient sensitivity is not causal attribution, normalized-weight descriptions conflict with displayed magnitudes, and training-data/implementation details require verification before reproduction.

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

Sections 1–6, pp. 1–10; ethics, reproducibility and references pp. 11–14

Appendix / supplement

Appendices A–D, pp. 14–23

Visually inspected PDF pages

6, 9

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 23 pages