Writing study: The Art of Scaling Reinforcement Learning Compute for LLMs

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← ICLR 2026 oral writing corpus

Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, Rachit Bansal, Venkata Sai Surya Subramanyam Duvvuri, Manzil Zaheer, Inderjit Dhillon, David Brandfonbrener, Rishabh Agarwal · 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: Empirical analysis, diagnosis & auditing (primary) · Learning & optimization algorithms.

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

30 physical pages

Before references / appendix

≈ 9.8 page-equivalents

Main figures / tables

6 / 0

Attached appendix figures / tables

20 / 1

Attached appendix span

≈ 15.89 page-equivalents

Before references / appendix: ≈ 32.67%References / intervening material: ≈ 14.37%Attached appendix: ≈ 52.97%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	2	≈ 1.28 pp.	4.27%
2 PRELIMINARIES & SETUP	3	≈ 1.9 pp.	6.33%
3 AN EMPIRICAL STUDY OF RL SCALING	5	≈ 2.18 pp.	7.27%
4 SCALERL: SCALING RL COMPUTE EFFECTIVELY & PREDICTABLY	7	≈ 0.92 pp.	3.07%
5 PREDICTABLE SCALING RETURNS ACROSS RL COMPUTE AXES	8	≈ 0.68 pp.	2.27%
6 RELATED WORK	9	≈ 0.39 pp.	1.3%
7 DISCUSSION & CONCLUSION	9	≈ 0.99 pp.	3.3%
REFERENCES	10	≈ 4.3 pp.	14.33%
A APPENDIX	15	≈ 15.89 pp.	52.97%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 2
• Figure 3 · p. 4
• Figure 4 · p. 5
• Figure 5 · p. 7
• Figure 6 · p. 8

Attached appendix

• Figure 7 · p. 18
• Figure 8 · p. 19
• Figure 9 · p. 20
• Figure 10 · p. 20
• Figure 11 · p. 21
• Figure 12 · p. 21
• Figure 13 · p. 22
• Figure 14 · p. 23
• Figure 15 · p. 23
• Table 1 · p. 23
• Figure 16 · p. 24
• Figure 17 · p. 24
• Figure 18 · p. 25
• Figure 19 · p. 26
• Figure 20 · p. 27
• Figure 21 · p. 28
• Figure 22 · p. 28
• Figure 23 · p. 29
• Figure 24 · p. 29
• Figure 25 · p. 30
• Figure 26 · p. 30

Argument and organization

1. Predictive framework and large-run demonstration

   Lead with the target prediction before defining a saturating compute-performance model.

2. Forward ablations and assembled recipe

   Identify design choices and then test their interaction through leave-one-out experiments.

3. Scaling axes and robustness

   Extend the recipe across budgets, models and tasks while qualifying generalization.

Problem, claim and contribution

The paper distinguishes a methodology for comparing scalability from a new optimizer. ScaleRL is explicitly an assembly of existing techniques; the novelty claim rests on systematic prediction and ablation.

Sections 1 and 7, pages 2–3 and 9–10 · Main paper

Argument and section order

Forward ablations select candidate components, then leave-one-out tests revisit those components in the assembled recipe. The discussion acknowledges their context-dependent effects rather than expecting identical gains in both designs.

Sections 3–4, pages 5–8; Appendix A.10, pages 19–20 · Main paper

Figures, captions and visual explanation

Figure 1 separates fitted and extrapolated trajectories using line styles and marks later observations distinctly. Figure 3 directly labels the asymptote, midpoint and slope, giving readers a decoding key before ablation plots.

Figures 1 and 3, pages 1 and 4 · Main paper

Tables, comparisons and evidence

The principal prediction target is mean pass rate on 1,000 held-out in-distribution prompts with 16 generations, not general downstream ability. Separate downstream plots and throughput diagnostics prevent those concepts being silently conflated.

Section 2.1, page 5; Appendices A.15 and A.20–A.21 · Main paper

Appendix and reproducibility

The appendix details fit ranges, parameter grid search, three-run variation and failure modes, including truncation instability and modified baseline recipes. These are essential qualifications to the headline scaling claim.

Appendices A.3–A.7 and A.16–A.18, pages 16–19 and 24–27 · Main paper

Clarity and writing conventions

Reported asymptotes are fitted quantities within an empirical range. Treating a larger fitted exponent as universal efficiency is too broad because midpoint and asymptote also matter; the paper's own curve schematic makes that dependency visible.

Equation 1; Figure 3; Appendix A.8, page 19 · Main paper

What to reuse—and what not to infer

Transferable lesson: Show observed versus predicted points unmistakably, explain fit parameters visually, and validate the final combined recipe rather than assuming independent component improvements add together.

Boundary: This is empirical RL scaling for specific models, data and hardware, not a universal law. Approximate uncertainty from three runs is not a calibrated prediction interval, and adapted baseline recipes are not exact reproductions of every original system.

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 1–7

Appendix / supplement

Pages 15–30, Appendices A.1–A.21, all fitting, recipe, stability and downstream extensions

Visually inspected PDF pages

1, 4, 7

• ICLR 2026 final proceedings · 30 pages