Writing study: Exploratory Diffusion Model for Unsupervised Reinforcement Learning

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← ICLR 2026 oral writing corpus

Chengyang Ying, Huayu Chen, Xinning Zhou, Zhongkai Hao, Hang Su, Jun Zhu · 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

31 physical pages

Before references / appendix

≈ 10.46 page-equivalents

Main figures / tables

4 / 1

Attached appendix figures / tables

5 / 5

Attached appendix span

≈ 14.89 page-equivalents

Before references / appendix: ≈ 33.74%References / intervening material: ≈ 18.23%Attached appendix: ≈ 48.03%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	≈ 1.3 pp.	4.19%
2 RELATED WORK	2	≈ 0.72 pp.	2.32%
3 BACKGROUND	3	≈ 0.8 pp.	2.58%
4 METHODOLOGY	4	≈ 3.13 pp.	10.1%
5 EXPERIMENTS	7	≈ 2.8 pp.	9.03%
6 CONCLUSION	10	≈ 0.8 pp.	2.58%
ETHICS STATEMENT	11	≈ 0.12 pp.	0.39%
REPRODUCIBILITY STATEMENT	11	≈ 0.23 pp.	0.74%
REFERENCES	11	≈ 5.65 pp.	18.23%
A EXTENDED RELATED WORK	17	≈ 1 pp.	3.23%
B THEORETICAL ANALYSES	18	≈ 4 pp.	12.9%
C ANALYSES AND DETAILS OF EXDM	22	≈ 2 pp.	6.45%
D EXPERIMENTAL DETAILS	24	≈ 7.44 pp.	24%
E THE USE OF LARGE LANGUAGE MODELS	31	≈ 0.45 pp.	1.45%

All detected figure and table locations

Main text

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

Attached appendix

• Figure 5 · p. 24
• Table 2 · p. 26
• Figure 6 · p. 27
• Figure 7 · p. 28
• Figure 8 · p. 29
• Table 3 · p. 29
• Table 4 · p. 29
• Table 5 · p. 30
• Table 6 · p. 30
• Figure 9 · p. 31

Argument and organization

1. Exploration bottleneck and two-stage method

   Motivates expressive distribution modeling, then separates reward-free data collection from task-reward fine-tuning.

2. Theory, practical algorithms and research questions

   Pairs each stage with pseudocode and tests exploration, Gaussian-policy transfer and diffusion-policy transfer separately.

3. Proofs and reproducibility supplement

   Expands assumptions, optimization details, per-task results and implementation budgets after the main narrative.

Problem, claim and contribution

The opening gives diffusion two distinct jobs: model heterogeneous replay data and provide a reusable policy prior. Gaussian acting is then motivated by interaction cost rather than presented as an unrelated component.

Sections 1 and 4.1, pages 1–2 and 4–5. · Main paper

Argument and section order

Two algorithms mirror the pretraining/fine-tuning split; three explicit experimental questions prevent exploration coverage from being confused with downstream return.

Algorithms 1–2, pages 5–6; Section 5, page 7. · Main paper

Figures, captions and visual explanation

Figure 1 links a geometric motivation to a replay-buffer loop and two adaptation paths, using repeated policy symbols and arrows. Its caption explains the Gaussian actor, but tiny labels and the unconditional 'always complex' annotation obscure theorem assumptions.

Figure 1, page 2; Appendix B.1, page 18. · Main paper

Tables, comparisons and evidence

Matched maze heatmaps sit above a coverage table with ten-seed uncertainty. Separate confidence-interval panels report Gaussian, cross-embodiment and diffusion-policy results; the prose explicitly acknowledges weaker diffusion than Gaussian fine-tuning.

Figure 2 and Table 1, page 7; Figure 3, page 8; Section 5.4, page 9. · Main paper

Appendix and reproducibility

The supplement follows a useful theory-to-implementation order: proof assumptions, objective derivations, baseline code origins, hyperparameters, per-task results and hardware/time budgets. A separate final paragraph discloses writing-only LLM use.

Appendices B–E, pages 18–31. · Main paper

Clarity and writing conventions

The caption-level guarantee should be narrowed: the first theorem assumes independent uniform state occupancies for deterministic policies; the second analyzes exact policy iteration, not directly the practical IQL/CEP updates. Equation 37 also uses energy f where the preceding derivation specifies its gradient.

Appendix B.1, page 18; Appendix B.2, pages 20–21; Appendix C.4, Equations 35–37, page 23. · Main paper

What to reuse—and what not to infer

Transferable lesson: Use the same stage boundaries in the overview, pseudocode and experiments; show spatial coverage beside its numerical metric, and separate interaction efficiency from wall-clock cost and downstream adaptation.

Boundary: Do not copy unconditional optimality or exact density/entropy claims from the framing. The proof assumptions, approximate denoising reward and practical optimization differ; energy-gradient versus noise-prediction notation needs reconciliation. These are state-based simulated-control experiments, not visual or pathology validation.

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; ethics/reproducibility statements on page 11 and references on pages 11–16.

Appendix / supplement

Pages 17–31, Appendices A–E: extended related work, all proofs and objective analyses, algorithm details, hyperparameters, complete result figures/tables, compute and LLM-use disclosures.

Visually inspected PDF pages

2, 7, 8, 23, 27, 28, 29, 30, 31

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 31 pages