Review-revise iterations improve synthesized problem quality, with gains largely stabilizing by the third iteration¶

Claim · All claims
Evidence · 1 study · causal r2 · q2
Currency · newest study 2025 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? Average quality scores across accepted problems rise from 4.06 (Round 1) to 4.13 (Round 2) and stay at 4.13 (Round 3), supporting the choice of three iterations. → Xianyang Liu 2025

Evidence¶

Xianyang Liu 2025¶

Xianyang Liu, Yilin Liu, Shuai Wang, Hao Cheng, Andrew Estornell, Yuzhi Zhao, Jun Shu, Jiaheng Wei. (2025). AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation. arXiv preprint. https://arxiv.org/abs/2510.19361 doi:10.48550/arxiv.2510.19361

q2 · i? · causal · r2

Analysis of the three review-revise iterations (Table 7) scoring accepted problems with GPT-4o-mini on complexity, information value, and clarity; average scores are 4.06, 4.13, and 4.13 across rounds.

"the first two review–revise iterations produce consistent improvements across all metrics. By the third iteration, however, the gains largely stabilize, indicating diminishing returns."

Discussion¶

Related Claims¶

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