Writing study: DiffusionNFT: Online Diffusion Reinforcement with Forward Process

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← ICLR 2026 oral writing corpus

Kaiwen Zheng, Huayu Chen, Haotian Ye, Haoxiang Wang, Qinsheng Zhang, Kai Jiang, Hang Su, Stefano Ermon, Jun Zhu, Ming-Yu Liu · 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

22 physical pages

Before references / appendix

≈ 9.58 page-equivalents

Main figures / tables

10 / 1

Attached appendix figures / tables

3 / 1

Attached appendix span

≈ 8.89 page-equivalents

Before references / appendix: ≈ 43.55%References / intervening material: ≈ 16.05%Attached appendix: ≈ 40.41%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	≈ 1.34 pp.	6.09%
2 BACKGROUND	3	≈ 0.64 pp.	2.91%
3 DIFFUSION REINFORCEMENT VIA NEGATIVE-AWARE FINETUNING	3	≈ 3.49 pp.	15.86%
4 EXPERIMENTS	7	≈ 2.26 pp.	10.27%
5 RELATED WORK	9	≈ 0.59 pp.	2.68%
6 CONCLUSION	10	≈ 0.29 pp.	1.32%
ACKNOWLEDGMENTS	10	≈ 0.14 pp.	0.64%
REFERENCES	10	≈ 3.52 pp.	16%
A PROOF OF THEOREMS	14	≈ 2.4 pp.	10.91%
B THEORETICAL DISCUSSIONS	16	≈ 2.6 pp.	11.82%
C EXPERIMENT DETAILS	19	≈ 0.34 pp.	1.55%
D ADDITIONAL RESULTS	19	≈ 3.55 pp.	16.14%

All detected figure and table locations

Main text

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

Attached appendix

• Table 2 · p. 19
• Figure 11 · p. 20
• Figure 12 · p. 21
• Figure 13 · p. 22

Argument and organization

1. Move reinforcement to the forward process

   Frames likelihood and solver coupling as the obstacles.

2. Positive/negative policy geometry and implicit objective

   Derives a training loss before listing practical approximations.

3. Multi-reward results, head-to-head speed and stability ablations

   Separates overall capability from task-specific optimization efficiency.

Problem, claim and contribution

A single question about forward versus reverse optimization gives the paper a clear organizing contrast instead of an undifferentiated list of engineering improvements.

Section 1, p. 2 · Main paper

Argument and section order

Idealized policy statements precede practical reward clipping, moving-average sampling and adaptive loss weighting, making assumptions distinguishable from implementation.

Sections 3.1–3.3, pp. 3–6 · Main paper

Figures, captions and visual explanation

The method diagram colors implicit positive and negative branches differently and writes their coefficients next to the arrows, directly matching the displayed loss.

Figure 4 and Equation 5, p. 5 · Main paper

Efficiency is plotted against GPU hours, while sampler, soft-update and weighting ablations expose collapse and stability tradeoffs rather than only final scores.

Figures 6–10, pp. 8–9 · Main paper

Tables, comparisons and evidence

The result table shades rewards used for training and preserves out-of-domain columns, an effective visual distinction between optimized targets and broader evaluation.

Table 1, p. 7 · Main paper

Appendix and reproducibility

Additional results disclose that OCR-only optimization hurts other metrics and uses CFG for non-OCR evaluation; that qualification materially changes a blanket CFG-free reading.

Appendices C–D and Table 2, p. 19 · Main paper

What to reuse—and what not to infer

Transferable lesson: Make the training/evaluation boundary visible in the table itself, and distinguish idealized objective derivations, practical stabilizers and measured wall-clock gains.

Boundary: The optimal-velocity theorem assumes unlimited data and capacity; fixed-strength finite training is not a universal monotonic reward guarantee. The 25× claim is task/configuration-specific, and reward scores do not establish unrestricted human preference or pathology utility.

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 and Algorithm 1

Appendix / supplement

Pages 14–22, Appendices A–D: all theorem proofs, flow samplers, GRPO gradient discussion, training details and additional results/captions

Visually inspected PDF pages

5, 7

• ICLR 2026 final proceedings · 22 pages