Writing study: ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control

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← NeurIPS 2026 oral writing corpus

Shelly Golan, Michael Finkelson, Ariel Bereslavsky, Yotam Nitzan, Or Patashnik · Official NeurIPS 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: Training, objectives & optimization (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

26 physical pages

Before references / appendix

≈ 10.11 page-equivalents

Main figures / tables

9 / 0

Attached appendix figures / tables

7 / 9

Attached appendix span

≈ 12.95 page-equivalents

Before references / appendix: ≈ 38.88%References / intervening material: ≈ 11.31%Attached appendix: ≈ 49.81%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Top-level heading and reference/appendix boundaries checked against source PDF text. Bibliography titles/author initials, numbered contribution/property bullets, diagram steps, equations and TOC entries are not substantive headings. Section spans are page-coordinate estimates, not ink area or prose-only length. This is an author manuscript mirror, not a verified accepted-version template. Source layout uses two-column reading order.

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	≈ 0.92 pp.	3.54%
2. Related Work	2	≈ 0.82 pp.	3.15%
3. Preliminaries	3	≈ 1.17 pp.	4.5%
4. Method	4	≈ 2.63 pp.	10.12%
5. Experiments	7	≈ 3.05 pp.	11.73%
6. Conclusions	10	≈ 0.63 pp.	2.42%
Acknowledgments	11	≈ 0.06 pp.	0.23%
References	11	≈ 2.94 pp.	11.31%
Appendix	14	≈ 0.12 pp.	0.46%
A. Implementation Details	14	≈ 5.09 pp.	19.58%
B. Experiments	19	≈ 1.41 pp.	5.42%
C. Limitations	20	≈ 6.34 pp.	24.38%

All detected figure and table locations

Main text

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

Attached appendix

• Table 1 · p. 15
• Table 2 · p. 16
• Table 3 · p. 17
• Table 4 · p. 17
• Table 5 · p. 19
• Figure 10 · p. 19
• Table 8 · p. 19
• Figure 11 · p. 19
• Figure 12 · p. 19
• Table 6 · p. 20
• Table 7 · p. 20
• Figure 13 · p. 20
• Figure 14 · p. 21
• Figure 15 · p. 22
• Table 9 · p. 23
• Figure 16 · p. 24

Argument and organization

1. Problem and contribution

   Replaces a single fixed reward trade-off with a model whose inference-time preference can vary.

2. Argument sequence

   Introduces Pareto notions and baseline post-training before reward normalization, conditioning and task-specific comparisons.

3. Evidence and verification

   Separate conditioning and scalarization ablations are followed by alternate-reward robustness and hypervolume reporting.

Problem, claim and contribution

Replaces a single fixed reward trade-off with a model whose inference-time preference can vary.

Introduction; Figure 1 · Main paper

Argument and section order

Introduces Pareto notions and baseline post-training before reward normalization, conditioning and task-specific comparisons.

Sections 3–5 · Main paper

Figures, captions and visual explanation

The training pipeline places normalization before weighted loss aggregation. A Pareto curve is paired with the same prompt's image sequence across preference settings.

Figure 2, p. 5; Figure 6, p. 8 · Main paper

Tables, comparisons and evidence

Separate conditioning and scalarization ablations are followed by alternate-reward robustness and hypervolume reporting.

Figures 7–12; Tables 6–7 · Main paper

Appendix and reproducibility

Backbone-specific hyperparameters, reward models, prompt-rewriting baselines and failure limits expose the evaluation choices.

Appendices A–C · Main paper

Clarity and writing conventions

Qualitative descriptions explain what changes across the preference axis rather than labeling a gallery as simply better.

Section 5.3 · Main paper

What to reuse—and what not to infer

Transferable lesson: For controllability, align numerical trade-off curves and identical-input visual sweeps, and evaluate with rewards beyond the optimized ones.

Boundary: Observed sampled frontiers are not a proof of global Pareto optimality. The two-column public manuscript is not verified NeurIPS camera-ready formatting.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-02

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented distributed manuscript scan of Sections 1–6, pp. 1–10; references pp. 11–13: abstract/framing, section sequence, substantive text windows, captions, evidence organization and conclusion; not exhaustive technical verification.

Appendix / supplement

Writing-oriented scan of Implementation, reward definitions, robustness comparisons, prompts and samples, pp. 14–26; code and experiments were not executed.

Visually inspected PDF pages

5, 8

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Public author arXiv manuscript 2604.20816v1; not verified identical to the accepted or camera-ready OpenReview revision · 26 pages