Writing study: Distributional Inverse Reinforcement Learning

ICML overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICML 2026 oral writing corpus

Feiyang Wu, Ye Zhao, Anqi Wu · Official ICML 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

32 physical pages

Before references / appendix

≈ 9.19 page-equivalents

Main figures / tables

5 / 3

Attached appendix figures / tables

8 / 5

Attached appendix span

≈ 19.91 page-equivalents

Before references / appendix: ≈ 28.72%References / intervening material: ≈ 9.06%Attached appendix: ≈ 62.22%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Wrapped main heading labels restored from the immediately following same-size source line in the physical PDF. Heading start positions are unchanged. Newly detected unnumbered impact/singular-acknowledgment statements are retained from the size-aware source extraction; authored heading corrections are unchanged.

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	≈ 1.25 pp.	3.91%
2. Related Work	2	≈ 0.83 pp.	2.59%
3. Preliminaries	3	≈ 0.35 pp.	1.09%
4. Distributional Inverse Reinforcement Learning Framework	3	≈ 2.49 pp.	7.78%
5. Theoretical Results	6	≈ 0.68 pp.	2.13%
6. Experiment	6	≈ 2.93 pp.	9.16%
7. Conclusion	9	≈ 0.23 pp.	0.72%
Impact Statement	10	≈ 0.08 pp.	0.25%
Acknowledgments	10	≈ 0.06 pp.	0.19%
References	10	≈ 2.9 pp.	9.06%
A. Related work comparison	13	≈ 0.54 pp.	1.69%
B. Extended preliminaries	13	≈ 4.46 pp.	13.94%
C. Proofs	18	≈ 9.64 pp.	30.13%
D. Model architecture and hyper-parameters	27	≈ 0.1 pp.	0.31%
E. Additional Ablation studies	27	≈ 1.02 pp.	3.19%
F. Additional Results on Matching Return Distribution	28	≈ 0.67 pp.	2.09%
G. Additional Results on Dopamine Level	29	≈ 1.07 pp.	3.34%
H. Limitations	30	≈ 2.41 pp.	7.53%

All detected figure and table locations

Main text

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

Attached appendix

• Table 4 · p. 13
• Table 5 · p. 14
• Table 6 · p. 28
• Table 7 · p. 28
• Table 8 · p. 28
• Figure 6 · p. 29
• Figure 7 · p. 29
• Figure 8 · p. 30
• Figure 9 · p. 30
• Figure 10 · p. 31
• Figure 11 · p. 31
• Figure 12 · p. 31
• Figure 13 · p. 32

Argument and organization

1. Problem and claim

   Separates distribution matching from distributional ordering and distinguishes uncertainty over deterministic parameters from a stochastic reward model. Those distinctions establish the gap before introducing the new objective.

2. Argument sequence

   A quantile/CDF illustration precedes the formal dominance definition; reward inference and policy learning are then separated before pseudocode, convergence assumptions and experiments.

3. Evidence and verification

   Risk-sensitive and deterministic-reward D4RL settings have distinct tables and protocols. The deterministic HalfCheetah result retains a stronger baseline and a footnote about extra data, so it must not be summarized as a universal win.

Problem, claim and contribution

Separates distribution matching from distributional ordering and distinguishes uncertainty over deterministic parameters from a stochastic reward model. Those distinctions establish the gap before introducing the new objective.

Introduction and Related Work, pp. 1–3 · Main paper

Argument and section order

A quantile/CDF illustration precedes the formal dominance definition; reward inference and policy learning are then separated before pseudocode, convergence assumptions and experiments.

Sections 3–6, pp. 3–6 · Main paper

Figures, captions and visual explanation

Figure 1 shades dominance violations rather than presenting an unlabeled schematic. Figure 3 pairs density and empirical-CDF views of the same state-action examples, while Figure 4 reports mean correlation and distribution distance separately.

Figure 1, physical p. 3; Figures 3–4, physical p. 8; rasters inspected · Main paper

Tables, comparisons and evidence

Risk-sensitive and deterministic-reward D4RL settings have distinct tables and protocols. The deterministic HalfCheetah result retains a stronger baseline and a footnote about extra data, so it must not be summarized as a universal win.

Tables 1–2, pp. 8–9 · Main paper

Appendix and reproducibility

Preliminaries and proof blocks are followed by a parameter table, trajectory-count and risk-measure sweeps, higher-moment diagnostics and additional dopamine fits. Limitations explicitly discuss non-identifiability and independent state-action reward models.

Appendices A–H, pp. 13–32 · Main paper

Clarity and writing conventions

The policy section calls the chosen distortion-risk objective an approximation, and the conclusion explicitly denies unique recovery of the true reward. This is stronger calibration than inferring identifiability from an attractive distribution plot.

Section 4.2, p. 5; Conclusion, p. 9 · Main paper

What to reuse—and what not to infer

Transferable lesson: Introduce the mathematical distinction with one diagnostic picture, then keep surrogate guarantees, distribution fit and downstream performance as separate claims.

Boundary: Agreement with dopamine traces is not causal identification of motivation, and compatible inferred rewards are not uniquely recovered ground truth.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-07

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-focused scan of Sections 1–7, pp. 1–9, including definitions, objective/surrogate distinction, assumptions, algorithm and comparison settings; impact statement p. 10.

Appendix / supplement

Structural and selected-detail scan of Appendices A–H, pp. 13–32: comparison taxonomy, distributional preliminaries, proof assumptions, configuration, sensitivity and reward-fit plots. No independent proof or neuroscience validation.

Visually inspected PDF pages

3, 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.

• Published proceedings PDF, PMLR volume 306, retrieved 2026-10-07 · 32 pages