Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
Score
77系统对比多个程序化数据集的完成监督训练效果，并首次揭示语义有效性与训练效用之间的关键错位。
方法设计严谨，包含多模型、多时长实验和三层审计，但部分任务级分析细节有限。
开源50个生成器与审计流程，可直接复用于合成推理数据生产与质量验证。
合成数据策略是当前大模型推理训练的热点，该工作契合追求可扩展训练数据的前沿需求。
arXiv预印本，公开代码与数据，但尚未经过同行评议。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper delivers empirical comparisons and deep audits of large-scale procedural reasoning generators, combining methodological novelty with engineering contribution.
AI commentary
「Synthetic reasoning data is a major lever for improving model reasoning, yet most teams focus only on generation scale, overlooking how target structure and difficulty calibration shape training outcomes. The paper's true value is not just another dataset, but a reusable audit methodology, reminding us that procedural generation does not equate to correctness. For those working on reasoning data engineering or preference fine-tuning, this open-source toolkit and comparative findings are worth a close read.」
Quick overview
This paper introduces Reasoning Core, a collection of 50 procedural generators spanning mathematics, logic, planning, state tracking, formal languages, structured data, games, causality, and code, equipped with semantic scorers, difficulty controls, and task evaluators. Under a matched completion-supervised protocol, Reasoning Core achieves the highest mean scores on DROP, LogiQA, and ARC-Challenge in a primary 3B comparison, outperforming baselines and three alternative procedural collections. Task-level analyses show that semantic validity alone does not ensure training utility, highlighting compact targets and calibrated difficulty as important design factors. Audits combining model-assisted review, human adjudication, and regression testing reveal subtle mismatches among generation, rendering, targets, and scoring, reminding that procedural generation alone does not guarantee correctness. The library, generated datasets, and audit material are publicly available.
- Reasoning Core comprises 50 procedural generators covering nine reasoning domains, with semantic scorers and difficulty controls.
- At 3B scale, Reasoning Core achieves higher mean scores on DROP, LogiQA, and ARC-Challenge than Procedural Warmup, Reasoning Gym, and SynLogic under completion supervision.
- Semantic validity alone does not guarantee training utility; compact targets and calibrated difficulty are critical design factors.
- Model-assisted audits reveal mismatches among generation, rendering, targets, and scoring, showing procedural generation requires rigorous validation.
Full text
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