Writing study: Semi-Supervised Preference Optimization with Limited Feedback

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← ICLR 2026 oral writing corpus

Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song · 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

30 physical pages

Before references / appendix

≈ 10.19 page-equivalents

Main figures / tables

3 / 5

Attached appendix figures / tables

0 / 14

Attached appendix span

≈ 15.89 page-equivalents

Before references / appendix: ≈ 33.97%References / intervening material: ≈ 13.07%Attached appendix: ≈ 52.97%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.	3.73%
2 RELATED WORK	2	≈ 0.8 pp.	2.67%
3 PRELIMINARIES	3	≈ 0.96 pp.	3.2%
4 PREFERENCE OPTIMIZATION WITH BOTH PAIRED AND UNPAIRED DATA	4	≈ 2.03 pp.	6.77%
5 EXPERIMENTS	6	≈ 4.32 pp.	14.4%
6 CONCLUSION	10	≈ 0.43 pp.	1.43%
REFERENCES	11	≈ 3.92 pp.	13.07%
A STATEMENT ON THE USE OF LARGE LANGUAGE MODELS	15	≈ 0.13 pp.	0.43%
B THEORETICAL ANALYSIS	15	≈ 1.35 pp.	4.5%
C IMPLEMENTATION DETAILS	16	≈ 3.51 pp.	11.7%
D THE DIFFERENCE BETWEEN SSRM, SPA, AND SSPO	20	≈ 1.16 pp.	3.87%
E COMPUTATIONAL OVERHEAD	21	≈ 1 pp.	3.33%
F MORE QUANTITATIVE ANALYSES	22	≈ 1.84 pp.	6.13%
G QUALITATIVE EXAMPLES	24	≈ 6.89 pp.	22.97%

All detected figure and table locations

Main text

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

Attached appendix

• Table 6 · p. 17
• Table 7 · p. 17
• Table 8 · p. 18
• Table 9 · p. 22
• Table 10 · p. 23
• Table 11 · p. 23
• Table 12 · p. 23
• Table 13 · p. 25
• Table 14 · p. 25
• Table 15 · p. 26
• Table 16 · p. 27
• Table 17 · p. 28
• Table 18 · p. 29
• Table 19 · p. 30

Argument and organization

1. Scarce comparisons to thresholded unpaired responses

   Moves from a data-access problem to a classification formulation, then distinguishes an ideal threshold from its KDE/EMA implementation.

2. Toy validation, real-data results, and mechanism checks

   Tests the proposed schedule and threshold with complementary synthetic, quantitative, and qualitative evidence.

Problem, claim and contribution

The introduction identifies an available but structurally incompatible resource—single SFT responses—and explains why pairwise preference methods cannot directly consume it.

Section 1; Figure 1, p. 2 · Main paper

Argument and section order

Separate subsections for threshold existence, practical estimation, and curriculum weighting help readers distinguish theoretical motivation from the executable algorithm.

Sections 4.1–4.3; Appendix C.4 · Main paper

Figures, captions and visual explanation

The overview uses different shapes for labeled winners, losers, and unpaired responses, then visualizes pseudo-label assignment around one threshold. The caption explains both the discarded resource and the proposed reuse.

Figure 1, p. 2 · Main paper

Loss-contribution trajectories and reward-distribution snapshots make the time-dependent mechanism visible. They illustrate behavior, but do not by themselves prove freedom from confirmation bias.

Figures 2–3, p. 9 · Main paper

Tables, comparisons and evidence

The table groups results by dataset, backbone, label budget, and both raw and length-controlled win rate. However, the prose claim of superiority in every setting is stronger than its mixed metric entries.

Table 2, p. 7; Section 5.3 · Main paper

Appendix and reproducibility

The appendix supplies implementation and runtime details and adds DPO/SimPO plus SFT controls using the same extra data; these controls materially narrow the interpretation of the headline gains.

Appendices C, E, and F.1; Table 10 · Main paper

What to reuse—and what not to infer

Transferable lesson: When a method exploits extra data, explain its format mismatch visually and include matched-extra-data baselines alongside label-budget comparisons.

Boundary: Sub-Gaussian rewards alone do not ensure a separating interval: the mean gap must support the stated tail bounds. The proof also does not establish transfer to shifted unpaired data. Read medical examples as generated outputs, not validated advice, and do not equate benchmark judge scores with verified human alignment.

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

PDF pp. 1–10, Sections 1–6, all main tables and case study.

Appendix / supplement

PDF pp. 15–30, Appendices A–G, including proof, gradients, pseudocode, hyperparameters, matched-data baselines, runtime, and all qualitative examples.

Visually inspected PDF pages

2, 7, 9

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 30 pages