Each AgenticMath component contributes performance gains, with the review-revise loop playing a particularly important role¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? On Mistral-7B with a fixed 15K synthesized dataset, adding Seed Filtering, the Review-Revise loop, and Synthetic Data Evaluation each improves average performance stage-wise. → 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? · design · r2

Ablation study on Mistral-7B with a fixed 15K synthesized dataset (Table 3): Problem Rephrase alone averages 31.4, rising to 32.0 with Seed Filtering, 33.0 with Review-Revise, and 33.2 with Synthetic Data Evaluation.

"Seed Filtering and the Review–Revise loop both yield clear performance gains, with iterative refinement playing a particularly important role."

Discussion¶

Related Claims¶

• Higher seed-problem filtering thresholds yield better downstream reasoning performance, indicating problem quality directly impacts results — related
• Under the same teacher model, AgenticMath outperforms prior data-generation methods, indicating gains arise from framework design rather than teacher strength — related
• Review-revise iterations improve synthesized problem quality, with gains largely stabilizing by the third iteration — a narrower finding that bears on this claim