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Writing study: ReQAT: Achieving Full-Precision Reasoning Accuracy with 4-bit Floating-Point Quantization-Aware Training

← ICML 2026 oral writing corpus

Janghwan Lee, Sihwa Lee, Jinseok Kim, Yongjik Kim, Jieun Lim, Jinwook Oh, Jungwook Choi · 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
22 physical pages
Before references / appendix
≈ 9.21 page-equivalents
Main figures / tables
7 / 7
Attached appendix figures / tables
4 / 10
Attached appendix span
≈ 7.91 page-equivalents
Before references / appendix: ≈ 41.86%References / intervening material: ≈ 22.18%Attached appendix: ≈ 35.95%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≈ 1.27 pp.5.77%
2. Background2≈ 0.79 pp.3.59%
3. Empirical Analysis3≈ 1.38 pp.6.27%
4. Methods4≈ 2.11 pp.9.59%
5. Experiments6≈ 1.99 pp.9.05%
6. Discussions8≈ 0.75 pp.3.41%
7. Related Works9≈ 0.22 pp.1%
8. Conclusion9≈ 0.16 pp.0.73%
Acknowledgements10≈ 0.11 pp.0.5%
Impact Statement10≈ 0.05 pp.0.23%
References10≈ 4.88 pp.22.18%
A. Microscaling Formats15≈ 0.37 pp.1.68%
B. Details for Token Entropy Analysis in Sec. 315≈ 2.33 pp.10.59%
C. ReQAT Details17≈ 2.3 pp.10.45%
D. Experimental Details and More Results20≈ 2.91 pp.13.23%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Background; 3 Empirical Analysis

    Diagnoses low-entropy sampling errors before proposing a training change.

  2. 4 Methods

    Maps the diagnosis to trace alignment, selective entropy loss and calibration initialization.

  3. 5 Experiments; 6 Discussion; 7 Related Work; 8 Conclusion

    Separates budget-matched accuracy, serving throughput, ablations and supervision limits.

Problem, claim and contribution

The opening connects compression to a particular reasoning failure, not simply a global increase in reconstruction error.

Abstract, p.1; Section 3, pp.3-4 · Main paper

Argument and section order

Controlled mixed-precision routing is separated from the more scalable logit-noise proxy before the method is introduced.

Sections 3.2-4, p.4 · Main paper

Figures, captions and visual explanation

The diagnostic composite links token examples, probability changes, routing and perturbation curves; the results tables organize precision and training budgets rather than merging all configurations.

Figure 2, p.3; Tables 1-3, p.7 · Main paper

Tables, comparisons and evidence

The text matches fine-tuning token budgets, but the tables show that adding every component is not monotonically beneficial and that the 8B model remains below its BF16 fine-tuned baseline.

Tables 1-2 and accompanying claims, p.7 · Main paper

Appendix and reproducibility

Evaluation details distinguish AIME-120 from single-year reporting, state eight seeds and separate calibration from training and serving settings.

Appendix D.1-D.3, p.20 · Main paper

Clarity and writing conventions

The limitations identify dependence on trace quality, while the broad full-precision headline requires model-, budget- and component-specific qualification.

Limitations and Conclusion, p.9 · Main paper

What to reuse—and what not to infer

Transferable lesson: Make the diagnostic-to-intervention link explicit, then audit broad recovery and monotonic-improvement language against every relevant table row.

Boundary: The title is not a universal full-precision guarantee; hardware throughput reflects batching and memory as well as arithmetic, and logit noise is a proxy intervention.

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-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 20. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
3, 7

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.