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
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 | ≈ 1.27 pp. | 5.77% |
| 2. Background | 2 | ≈ 0.79 pp. | 3.59% |
| 3. Empirical Analysis | 3 | ≈ 1.38 pp. | 6.27% |
| 4. Methods | 4 | ≈ 2.11 pp. | 9.59% |
| 5. Experiments | 6 | ≈ 1.99 pp. | 9.05% |
| 6. Discussions | 8 | ≈ 0.75 pp. | 3.41% |
| 7. Related Works | 9 | ≈ 0.22 pp. | 1% |
| 8. Conclusion | 9 | ≈ 0.16 pp. | 0.73% |
| Acknowledgements | 10 | ≈ 0.11 pp. | 0.5% |
| Impact Statement | 10 | ≈ 0.05 pp. | 0.23% |
| References | 10 | ≈ 4.88 pp. | 22.18% |
| A. Microscaling Formats | 15 | ≈ 0.37 pp. | 1.68% |
| B. Details for Token Entropy Analysis in Sec. 3 | 15 | ≈ 2.33 pp. | 10.59% |
| C. ReQAT Details | 17 | ≈ 2.3 pp. | 10.45% |
| D. Experimental Details and More Results | 20 | ≈ 2.91 pp. | 13.23% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Background; 3 Empirical Analysis
Diagnoses low-entropy sampling errors before proposing a training change.
- 4 Methods
Maps the diagnosis to trace alignment, selective entropy loss and calibration initialization.
- 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.