Writing study: Planner Aware Path Learning in Diffusion Language Models Training

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← ICLR 2026 oral writing corpus

Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks, Shuibai Zhang, Michael Bronstein, Anru Zhang, Joey Bose, Alexander Tong · 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

46 physical pages

Before references / appendix

≈ 11.11 page-equivalents

Main figures / tables

4 / 4

Attached appendix figures / tables

2 / 3

Attached appendix span

≈ 30.89 page-equivalents

Before references / appendix: ≈ 24.15%References / intervening material: ≈ 8.7%Attached appendix: ≈ 67.15%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	≈ 1.22 pp.	2.65%
2 BACKGROUND AND PRELIMINARIES	2	≈ 1.47 pp.	3.2%
3 METHOD: PATH LEARNING	4	≈ 3.01 pp.	6.54%
4 EXPERIMENTS	7	≈ 2.83 pp.	6.15%
5 RELATED WORK	10	≈ 0.4 pp.	0.87%
6 CONCLUSION	10	≈ 0.53 pp.	1.15%
ACKNOWLEDGMENTS	11	≈ 0.16 pp.	0.35%
ETHICS STATEMENT	11	≈ 0.14 pp.	0.3%
REPRODUCIBILITY STATEMENT	11	≈ 0.7 pp.	1.52%
REFERENCES	12	≈ 4 pp.	8.7%
APPENDICES	16	≈ 1 pp.	2.17%
A THEORETICAL DERIVATIONS AND PROOFS	17	≈ 21.51 pp.	46.76%
B EXPERIMENTAL DETAILS	38	≈ 4.18 pp.	9.09%
C ADDITIONAL RESULTS	42	≈ 3.44 pp.	7.48%
D PRACTITIONER’S GUIDE	46	≈ 0.77 pp.	1.67%

All detected figure and table locations

Main text

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

Attached appendix

• Table 5 · p. 41
• Figure 5 · p. 42
• Table 6 · p. 42
• Figure 6 · p. 43
• Table 7 · p. 43

Argument and organization

1. Planner-aware likelihood and its practical surrogate

   A sampler-mismatch question leads to a counterexample, an exact path-dependent bound, then explicitly approximated training.

2. Experiments and appendix

   Protein, text and code evaluations are followed by derivations, domain-specific protocols and unsuccessful alternatives.

Problem, claim and contribution

The boxed question names a precise mismatch: training with uniform unmasking while sampling with a confidence-based planner. This is more informative than claiming diffusion language models are generally inefficient.

Pages 2–3, Sections 1 and 3.1. · Main paper

Argument and section order

The exact planner-aware bound and the implemented weighted cross-entropy are separated. State-distribution approximation and omission of a correction term remain substantive assumptions, not theorem-backed equivalences.

Sections 3.2–3.4; Appendix A.2.3 and C. · Main paper

Figures, captions and visual explanation

Figure 1 aligns uniform and planned token-revelation paths in parallel columns, using token positions and outlined choices to make the train–sample mismatch concrete before the derivation.

Page 4, Figure 1. · Main paper

Tables, comparisons and evidence

The code table separates compact-model comparisons from larger references. It also contains an important exception: MBPP pass@1 falls from 17.6 to 16.7, so the surrounding improvement narrative must not become an all-metrics claim.

Page 9, Table 4. · Main paper

Appendix and reproducibility

The long proof appendix is navigable through a dedicated contents page, while the later approximation checks and pure-weighted-loss instability explain why the runnable loss retains a uniform component.

Page 16 contents; Appendix A, pages 17–38; Appendix C, pages 42–46. · Main paper

Clarity and writing conventions

Incorrect generated programs make failure discussion specific, but oracle-length infilling and ordinary completion must remain visibly distinct protocols. Protein foldability is a computational confidence proxy, not demonstrated biological function.

Page 45, Listings 4–6; Tables 2, 3 and 6; Appendix B. · Main paper

What to reuse—and what not to infer

Transferable lesson: Expose every approximation between a likelihood theorem and the implemented loss, then use matched-protocol tables and concrete failures to delimit the practical claim.

Boundary: Practical PAPL is not the exact planner-aware bound. Relative MAUVE gains are not absolute percentage-point gains; oracle-length infilling is not ordinary generation; MBPP pass@1 declines; predicted protein structure confidence does not establish functionality.

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 1–11, Sections 1–6, ethics and reproducibility; references on pages 12–15.

Appendix / supplement

Pages 16–46: appendix contents; A discrete and continuous-time derivations and proofs; B experimental protocols; C approximations, ablations and failure examples; D practitioner guidance.

Visually inspected PDF pages

4, 9, 45

• ICLR 2026 final proceedings · 46 pages