Writing study: SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety

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← ICLR 2026 oral writing corpus

Geon-Hyeong Kim, Yu Jin Kim, Byoungjip Kim, Honglak Lee, Kyunghoon Bae, Youngsoo Jang, Moontae Lee · 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) · Theory & formal foundations.

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

40 physical pages

Before references / appendix

≈ 10.11 page-equivalents

Main figures / tables

3 / 3

Attached appendix figures / tables

1 / 26

Attached appendix span

≈ 27.9 page-equivalents

Before references / appendix: ≈ 25.27%References / intervening material: ≈ 4.98%Attached appendix: ≈ 69.75%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	≈ 1.2 pp.	3%
2 PRELIMINARIES	2	≈ 1.4 pp.	3.5%
3 DIRECT PREFERENCE OPTIMIZATION WITH ENHANCED SAFETY	4	≈ 1.9 pp.	4.75%
4 THEORETICAL ANALYSIS	6	≈ 0.57 pp.	1.42%
5 EXPERIMENTS	6	≈ 3.94 pp.	9.85%
6 CONCLUSION	10	≈ 0.49 pp.	1.23%
REFERENCES	11	≈ 2 pp.	5%
Appendix	13	≈ 0.28 pp.	0.7%
A THEORETICAL ANALYSIS	13	≈ 4.72 pp.	11.8%
B DETAILS OF THE EXPERIMENTS	18	≈ 4 pp.	10%
C SUPPLEMENTARY EXPERIMENTS	22	≈ 7 pp.	17.5%
D FURTHER GPT-BASED EVALUATION USING VARIOUS TEMPLATES	29	≈ 9 pp.	22.5%
E FAILURE ANALYSIS AND EVALUATION LIMITATIONS	38	≈ 2.89 pp.	7.23%

All detected figure and table locations

Main text

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

Attached appendix

• Table 4 · p. 18
• Table 5 · p. 22
• Table 6 · p. 22
• Table 7 · p. 22
• Table 8 · p. 23
• Table 9 · p. 23
• Table 10 · p. 24
• Table 11 · p. 24
• Table 12 · p. 25
• Table 13 · p. 26
• Table 14 · p. 26
• Table 15 · p. 27
• Table 16 · p. 27
• Table 17 · p. 28
• Table 18 · p. 28
• Table 19 · p. 29
• Table 20 · p. 29
• Table 21 · p. 29
• Figure 4 · p. 30
• Table 22 · p. 31
• Table 23 · p. 31
• Table 24 · p. 32
• Table 25 · p. 33
• Table 26 · p. 34
• Table 27 · p. 35
• Table 28 · p. 36
• Table 29 · p. 37

Argument and organization

1. Hard constraint to optimal policy to transformed pair data

   Derives the method in three explicit steps before presenting assumptions and formal results.

2. Safety, helpfulness and over-refusal as separate outcomes

   Progresses from benchmark comparisons to margin/scaling ablations, human judgments and benign-prompt refusal.

Problem, claim and contribution

The introduction makes reduced training complexity concrete by contrasting auxiliary reward/cost networks with a transformed preference dataset.

Section 1 and Figure 1, pages 1–2. · Main paper

Argument and section order

Section 3 states its three-step derivation in advance. A piecewise transformation then specifies unchanged, swapped and discarded pairs before the margin is added.

Sections 3.1–3.3, pages 4–5. · Main paper

Figures, captions and visual explanation

Figure 1 uses paired pipelines and consistent red/blue component coding to show precisely which networks and labels differ between SafeRLHF and SafeDPO.

Figure 1, page 2. · Main paper

Tables, comparisons and evidence

Human evaluation is not hidden when it reverses the automatic judges' helpfulness ranking. The over-refusal table reports the cost of the stronger harmless ratio.

Table 2, page 9; Table 3, page 10. · Main paper

Appendix and reproducibility

Exact values behind Figure 2, training hyperparameters, evaluator prompts, participant instructions and multi-seed standard errors make the empirical record unusually auditable.

Appendices B, C.7 and C.9, pages 18–21, 26 and 28. · Main paper

Clarity and writing conventions

Separate evaluation subsections explain why safety and helpfulness judgments can be coupled; concrete failure cases make a vague conservativeness limitation observable.

Appendices D.2 and E, pages 31 and 38–40. · Main paper

What to reuse—and what not to infer

Transferable lesson: For a simple method, spend the saved architectural complexity on explicit data-transformation rules and evaluation that reveals both benefits and behavioral costs.

Boundary: The zero-unsafe-mass result concerns an ideal optimal policy under stated assumptions, not a finite trained model's universal deployment guarantee. Keep empirical judge thresholds and over-refusal visible; a 100% observed harmless ratio is benchmark-specific, and evaluator-defined helpfulness differs from the human rubric.

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

Pages 1–10, Sections 1–6; references on pages 11–12.

Appendix / supplement

Pages 13–40, Appendices A–E, including all proofs, training settings, human instructions, judge templates, numeric plot values, qualitative cases and over-refusal analysis.

Visually inspected PDF pages

2, 9

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 40 pages