Writing study: Reward-free Alignment for Conflicting Objectives

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← ICML 2026 oral writing corpus

Peter Chen, Xiaopeng Li, Xi Chen, Tianyi Lin · 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

27 physical pages

Before references / appendix

≈ 8.32 page-equivalents

Main figures / tables

6 / 2

Attached appendix figures / tables

0 / 0

Attached appendix span

≈ 13.91 page-equivalents

Before references / appendix: ≈ 30.81%References / intervening material: ≈ 17.67%Attached appendix: ≈ 51.52%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	1	≈ 1.89 pp.	7%
2. Preliminaries and Technical Background	3	≈ 0.77 pp.	2.85%
3. Main Results	4	≈ 1.2 pp.	4.44%
4. Experiment	5	≈ 3.6 pp.	13.33%
5. Conclusion	8	≈ 0.2 pp.	0.74%
Impact Statement	9	≈ 0.22 pp.	0.81%
Acknowledgment	9	≈ 0.05 pp.	0.19%
References	9	≈ 4.77 pp.	17.67%
A. Further Related Works	14	≈ 1.48 pp.	5.48%
B. Missing Proofs	15	≈ 4.88 pp.	18.07%
C. Additional Experiment Setups and Results	20	≈ 7.11 pp.	26.33%
D. Ethical Statement	27	≈ 0.45 pp.	1.67%

All detected figure and table locations

Main text

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

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Preliminaries

   Defines direct preference alignment and distinguishes Pareto optimality from criticality.

2. 3 Main Results

   Derives clipped conflict-aware updates and states convergence and two-objective descent results.

3. 4 Experiments; 5 Conclusion

   Separates summarization trade-offs, safety trade-offs and clipping/radius ablations.

Problem, claim and contribution

Reward-free means avoiding an explicit training reward model, while evaluation still uses learned scorers and an LLM judge.

Abstract, p.1; evaluation protocols, pp.5,7 · Main paper

Argument and section order

The theorem states smoothness, step-size and conflict-radius conditions beside the algorithm; the acceleration result is narrowed to the two-objective case.

Theorems 3.1-3.2, pp.4-5 · Main paper

Figures, captions and visual explanation

A three-way gradient diagram contrasts weighting, overcorrection and clipping; later panels separate objective-space trade-offs from correction-weight and validation-margin trajectories.

Figure 1, p.4; Figures 4-5, p.7 · Main paper

Tables, comparisons and evidence

The paper distinguishes validation preference margins from generated-response quality and acknowledges that a safety-prioritizing judge can favor a different trade-off.

Sections 4.1-4.2, pp.5-8 · Main paper

Appendix and reproducibility

The evaluation details identify exact scorers, generation settings, the sign reversal of harmfulness cost and what pre-alignment means for base models.

Appendices C.2-C.3, p.27 · Main paper

Clarity and writing conventions

The conclusion names Pareto-critical points rather than claiming global Pareto optimality; plotted finite weight sweeps remain empirical frontiers.

Conclusion, p.8; theoretical definitions, p.3 · Main paper

What to reuse—and what not to infer

Transferable lesson: Keep optimization guarantees, validation proxies and judged generation outcomes distinct, and make the selected objective trade-off visible.

Boundary: Pareto criticality is not global optimality; reward-free training does not remove evaluator preferences or show that every objective improves in every tested configuration.

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.8, with focused physical-page text inspection on 1, 3, 4, 5, 6, 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 27. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.

Visually inspected PDF pages

4, 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.

• Published proceedings PDF, PMLR volume 306, retrieved 2026-10-07 · 27 pages