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Writing study: The Signal is in the Steps: Local Scoring for Reasoning Data Selection

← ICML 2026 oral writing corpus

Hoang Anh Just, Myeongseob Ko, Ruoxi Jia · Official ICML 2026 oral record

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
31 physical pages
Before references / appendix
≈ 9.36 page-equivalents
Main figures / tables
6 / 4
Attached appendix figures / tables
4 / 18
Attached appendix span
≈ 16.91 page-equivalents
Before references / appendix: ≈ 30.19%References / intervening material: ≈ 15.26%Attached appendix: ≈ 54.55%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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 0.95 pp.3.06%
2. Related Work2≈ 0.55 pp.1.77%
3. Problem Setting and Baseline2≈ 0.4 pp.1.29%
4. When Global Selection Fails3≈ 0.8 pp.2.58%
5. Why GALP Fails4≈ 0.52 pp.1.68%
6. Method: Local Average Log Probability4≈ 1.63 pp.5.26%
7. Evaluation6≈ 3.62 pp.11.68%
8. Conclusion9≈ 0.27 pp.0.87%
Impact Statement10≈ 0.27 pp.0.87%
References10≈ 4.73 pp.15.26%
A. Toy theoretical model: locality induces a reasoning gap15≈ 5 pp.16.13%
B. Dataset and evaluation details20≈ 2.72 pp.8.77%
C. Additional Results22≈ 9.19 pp.29.65%
All detected figure and table locations

Argument and organization

  1. Problem and claim

    Starts from a baseline that works in a restricted setting, then identifies the long, mixed-teacher regime where its ranking fails. The new rule answers a specific response-selection problem rather than replacing all likelihood-based selection.

  2. Argument sequence

    Separates when global scoring fails, a hypothesis for why, the local scoring definition and two deployment uses: choosing a teacher and choosing individual responses. Diagnostic analysis follows the main comparisons.

  3. Evidence and verification

    Within-teacher terciles provide a sanity check before the mixed-teacher comparison. Later window and segmenter controls ask whether the gain is specific to one truncation setting or segmentation model.

Problem, claim and contribution

Starts from a baseline that works in a restricted setting, then identifies the long, mixed-teacher regime where its ranking fails. The new rule answers a specific response-selection problem rather than replacing all likelihood-based selection.

Introduction and Sections 3–4, pp. 1–3 · Main paper

Argument and section order

Separates when global scoring fails, a hypothesis for why, the local scoring definition and two deployment uses: choosing a teacher and choosing individual responses. Diagnostic analysis follows the main comparisons.

Sections 4–7, pp. 3–9 · Main paper

Figures, captions and visual explanation

The context-window plot uses consistent teacher colors and marker shapes to show a ranking reversal; the token-category bar chart states its 440-response analysis set and prints percentages. These displays serve different explanatory jobs.

Figure 3, physical p. 5; Figure 4, physical p. 8; rasters inspected · Main paper

Tables, comparisons and evidence

Within-teacher terciles provide a sanity check before the mixed-teacher comparison. Later window and segmenter controls ask whether the gain is specific to one truncation setting or segmentation model.

Table 1, p. 3; Sections 7.1–7.2, pp. 6–7; Tables 11 and 21, pp. 26 and 31 · Main paper

Appendix and reproducibility

The toy theoretical model explicitly lists Markov and background-distribution simplifications. A separate details section records segmentation, sampling, SFT and evaluation, while additional tables expose teacher mixtures and expanded pools.

Appendix A limitations, p. 19; Appendices B–C, pp. 20–31 · Main paper

Clarity and writing conventions

Figure 2 is visibly a line-and-band plot although its caption calls each display a violin. Its strong generalization wording also exceeds what cosine similarity alone can establish; the main limitation correctly separates local learnability from objective correctness.

Figure 2, physical p. 5; Limitations, p. 10 · Main paper

What to reuse—and what not to infer

Transferable lesson: Preserve the baseline’s successful regime, diagnose the changed regime, and test whether the proposed selection rule survives reasonable implementation alternatives.

Boundary: Local likelihood is not a correctness verifier or a guarantee of long-range consistency; the paper does not evaluate it as an RL reward.

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 1–8, pp. 1–9, with limitations and impact on p. 10; baseline definition, failure regime, local score, teacher/response selection and diagnostic examples checked.
Appendix / supplement
Structural scan of Appendices A–C, pp. 15–31, with selected checks of toy-model limitations, segmentation implementation, training/evaluation settings, window and segmenter ablations. Not independent validation of the toy proofs or reported measurements.
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.