Writing study: AgentGym-RL: An Open-Source Framework to Train LLM Agents for Long-Horizon Decision Making via Multi-Turn RL

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← ICLR 2026 oral writing corpus

Zhiheng Xi, Jixuan Huang, Chenyang Liao, Baodai Huang, Jiaqi Liu, Honglin Guo, yajie yang, Rui Zheng, Junjie Ye, Jiazheng Zhang, Wenxiang Chen, Wei He, Yiwen Ding, Guanyu Li, Zehui Chen, Zhengyin Du, Xuesong Yao, Yufei Xu, Jiecao Chen, Tao Gui, Zuxuan Wu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang · 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: Datasets, benchmarks & measurement resources (primary) · Learning & optimization algorithms · Inference, algorithms & systems efficiency.

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

43 physical pages

Before references / appendix

≈ 10.46 page-equivalents

Main figures / tables

8 / 4

Attached appendix figures / tables

14 / 4

Attached appendix span

≈ 20.89 page-equivalents

Before references / appendix: ≈ 24.33%References / intervening material: ≈ 27.09%Attached appendix: ≈ 48.58%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	≈ 1.09 pp.	2.53%
2 PRELIMINARIES	2	≈ 1 pp.	2.33%
3 THE AGENTGYM-RL FRAMEWORK	3	≈ 1.88 pp.	4.37%
4 SCALINGINTER-RL: SCALING INTERACTIONS FOR LLM AGENTS	5	≈ 1.11 pp.	2.58%
5 EXPERIMENTS	6	≈ 1.5 pp.	3.49%
6 DISCUSSION	8	≈ 1.89 pp.	4.4%
7 RELATED WORK	10	≈ 0.61 pp.	1.42%
8 CONCLUSION	10	≈ 0.32 pp.	0.74%
ETHICS STATEMENT	11	≈ 0.14 pp.	0.33%
REPRODUCIBILITY STATEMENT	11	≈ 0.22 pp.	0.51%
REFERENCES	11	≈ 11.65 pp.	27.09%
A THE USE OF LARGE LANGUAGE MODELS	23	≈ 0.08 pp.	0.19%
B ILLUSTRATION OF SCALINGINTER-RL	23	≈ 0.4 pp.	0.93%
C DETAILS OF THE FEATURES AND CHARACTERISTICS OF AGENTGYM-RL	23	≈ 1.22 pp.	2.84%
D DETAILED TASK PERFORMANCE ACROSS ENVIRONMENTS	24	≈ 2.81 pp.	6.53%
E IMPLEMENTATION DETAILS AND SETTINGS OF EACH ENVIRONMENT	27	≈ 3.98 pp.	9.26%
F ANALYSIS ABOUT THE INSTABILITY OF LONG-HORIZON TRAINING	31	≈ 0.18 pp.	0.42%
G TRAJECTORY EXAMPLES AND VISUALIZATIONS OF OUR RL AGENT	31	≈ 1.98 pp.	4.6%
H COMPARATIVE CASE ANALYSIS	33	≈ 10.26 pp.	23.86%

All detected figure and table locations

Main text

• Figure 1 · p. 2
• Figure 2 · p. 3
• Figure 3 · p. 4
• Figure 4 · p. 5
• Figure 5 · p. 5
• Figure 6 · p. 6
• Table 1 · p. 7
• Figure 7 · p. 8
• Table 2 · p. 8
• Table 3 · p. 8
• Figure 8 · p. 9
• Table 4 · p. 9

Attached appendix

• Figure 9 · p. 23
• Figure 10 · p. 24
• Table 5 · p. 25
• Table 6 · p. 26
• Table 7 · p. 27
• Table 8 · p. 28
• Figure 11 · p. 32
• Figure 12 · p. 33
• Figure 13 · p. 34
• Figure 14 · p. 35
• Figure 15 · p. 36
• Figure 16 · p. 37
• Figure 17 · p. 38
• Figure 18 · p. 39
• Figure 19 · p. 40
• Figure 20 · p. 41
• Figure 21 · p. 42
• Figure 22 · p. 43

Argument and organization

1. Framework, then interaction curriculum

   Separate the reusable environment/agent/trainer infrastructure from the staged-horizon algorithm.

2. Results, scaling analyses, and trajectory cases

   Move from aggregate outcomes to turn/sample budgets, algorithm comparisons, efficiency, and behavioral failures.

Problem, claim and contribution

The introduction distinguishes increasing internal reasoning from increasing environmental interaction; the method then targets the latter without conflating the two compute budgets.

Sections 1 and 4, pages 1–2 and 5–6. · Main paper

Argument and section order

The main table shows a representative search benchmark, while environment-specific tables and exact action APIs move to the appendix. Bold result-led paragraphs support scanning but need careful scope qualification.

Table 1 and Section 5.2, page 7; Appendix D, pages 24–28. · Main paper

Figures, captions and visual explanation

The framework diagram combines a concrete web objective, typed reasoning/action/observation tokens, an HTTP boundary, and a policy-update loop. It makes software modularity visible rather than listing components alone.

Figure 2, page 3. · Main paper

Tables, comparisons and evidence

Paired reward and interaction-count curves diagnose collapse under a fixed long horizon before motivating the staged schedule; later plots separately track time and cumulative rounds.

Figure 4, page 5; Figure 8, page 9. · Main paper

Appendix and reproducibility

Per-environment sections supply prompts, task selections, horizon schedules, learning rates, temperatures, and rollout counts; success and failure trajectories follow those reproducibility details.

Appendices E–H, pages 27–43. · Main paper

Clarity and writing conventions

Two concrete cross-reference errors illustrate why final caption/table reconciliation matters: the chemistry paragraph says every model scores zero despite nonzero baseline entries, and the sodium-chloride trajectory is captioned as moving a banana.

Appendix D, page 27 versus Table 8, page 28; Figure 14, page 35. · Main paper

What to reuse—and what not to infer

Transferable lesson: For a systems-and-algorithm paper, explain the reusable interface first, diagnose the training failure with paired traces, then connect the proposed schedule to both effectiveness and resource cost.

Boundary: Do not reproduce the chemistry-zero claim or the banana caption. The 50-example WebArena evaluation excludes state-changing tasks, and aggregate model comparisons do not establish a universal scaling law or real-world agent reliability.

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 1–8; ethics/reproducibility statements on page 11; references on pages 11–22.

Appendix / supplement

Pages 23–43, Appendices A–H: disclosure, staged-training illustration, framework features, environment-specific results and settings, instability analysis, and all trajectory cases.

Visually inspected PDF pages

3, 5, 7, 28, 35

• ICLR 2026 official proceedings PDF, retrieved 2026-09-19 · 43 pages