Writing study: Q-RAG: Long Context Multi‑Step Retrieval via Value‑Based Embedder Training

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← ICLR 2026 oral writing corpus

Artyom Sorokin, Nazar Buzun, Aleksandr Anokhin, Egor VEDERNIKOV, Petr Anokhin, Mikhail Burtsev, Evgeny Burnaev · 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: Learning & optimization algorithms (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

26 physical pages

Before references / appendix

≈ 10.11 page-equivalents

Main figures / tables

3 / 4

Attached appendix figures / tables

3 / 4

Attached appendix span

≈ 12.89 page-equivalents

Before references / appendix: ≈ 38.88%References / intervening material: ≈ 11.54%Attached appendix: ≈ 49.58%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	≈ 0.81 pp.	3.12%
2 RELATED WORK	2	≈ 0.85 pp.	3.27%
3 METHODS	3	≈ 2.99 pp.	11.5%
4 EXPERIMENTS	6	≈ 2.63 pp.	10.12%
5 ABLATION STUDY	8	≈ 1.58 pp.	6.08%
6 CONCLUSION	10	≈ 0.37 pp.	1.42%
ACKNOWLEDGMENTS	10	≈ 0.29 pp.	1.12%
REFERENCES	11	≈ 3 pp.	11.54%
A INNER PRODUCT APPROXIMATION FOR Q-FUNCTION	14	≈ 7 pp.	26.92%
B EARLY STOPPING EXPERIMENTS	21	≈ 2.54 pp.	9.77%
C PLANNING FOR MULTI-STEP RETRIEVAL	23	≈ 0.18 pp.	0.69%
D METHOD COMPLEXITY AND EFFICIENCY	23	≈ 1.28 pp.	4.92%
E EXTRA QA RESULTS	25	≈ 0.28 pp.	1.08%
F TRAINING DETAILS	25	≈ 0.25 pp.	0.96%
G EVALUATION DETAILS	25	≈ 1.36 pp.	5.23%

All detected figure and table locations

Main text

• Figure 1 · p. 3
• Figure 2 · p. 6
• Table 1 · p. 7
• Table 2 · p. 8
• Figure 3 · p. 9
• Table 3 · p. 9
• Table 4 · p. 10

Attached appendix

• Figure 4 · p. 22
• Table 5 · p. 22
• Figure 5 · p. 23
• Table 6 · p. 23
• Figure 6 · p. 24
• Table 7 · p. 25
• Table 8 · p. 26

Argument and organization

1. Methods

   Formalize retrieval as a chunk-selection MDP, then introduce fact-supervised soft value learning and relative positional encoding.

2. Experiments and ablations

   Separate synthetic ultra-long context, open-domain QA, value-learning controls and retrieval budgets.

3. Appendices

   Supply approximation theory, stopping trade-offs, computational accounting and benchmark-specific configurations.

Problem, claim and contribution

The contrast is explicit: train state/action embedders while leaving the answer-generating LLM frozen. The main text honestly states that reported rewards use supporting facts, not the proposed future LLM-feedback alternative.

Sections 2–3.2. · Main paper

Figures, captions and visual explanation

A running key-search example instantiates the agent/environment diagram. Showing selected chunks in document order clarifies the state update; the caption's probability-proportional-to-Q wording should match the softmax equation.

Figure 1, page 3; Equation 3. · Main paper

Tables, comparisons and evidence

Symbols distinguish reproduced, reported and ablation rows. This is valuable when baseline coverage differs across lengths, and absent results are shown as unavailable rather than zero.

Section 4.1 and Table 1, page 7. · Main paper

Early-stop analysis excludes episodes that never retrieve all supporting facts; its high stopping AUC therefore cannot stand alone as unconditional system reliability.

Appendix B, pages 21–23. · Main paper

Appendix and reproducibility

The final configuration table discloses a QwQ-32B answerer for QA and separate smaller answerers for synthetic tasks. Fact EM means supporting-set coverage, not exact set equality, a crucial definition that belongs near every reused table.

Appendix G and Table 8, pages 25–26. · Main paper

Clarity and writing conventions

The appendix proves approximation properties under compactness/smoothness and expanding feature families. These are not convergence guarantees for learned neural Q training or fixed practical RoPE frequencies.

Section 3.2 versus Appendix A, pages 14–20. · Main paper

What to reuse—and what not to infer

Transferable lesson: Use explicit provenance symbols and benchmark-specific model/metric tables to make a resource-efficiency claim auditable.

Boundary: The original corpus still requires linear-time chunk embedding and storage; constant answerer context is not constant end-to-end cost. Oracle-hop baseline access, support-fact supervision and conditional stopping analysis limit comparisons.

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 1–6 and reproducibility; references 11–13.

Appendix / supplement

Pages 14–26: Appendices A–G, all approximation proofs, stopping experiments, planning, complexity, extra QA and exact configuration/metric definitions.

Visually inspected PDF pages

3, 7

• ICLR 2026 final proceedings · 26 pages