Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training

TL;DR - Reasoning Core is an open library of 50 procedural problem generators (math, logic, planning, state tracking, formal languages, code, games, causality) built specifically as data for completion-supervised fine-tuning, and it outperforms existing procedural collections on reasoning benchmarks. It matters because it shows procedural data quality — not just volume or validity — drives downstream reasoning gains.

• 50 generators ship with semantic scorers, difficulty controls, and task evaluators; library, generated datasets, and audit material are public.
• Under a matched completion-supervised protocol across four base-model settings and multiple training durations, the primary 3B comparison gives Reasoning Core the highest mean scores on DROP, LogiQA, and ARC-Challenge, beating a no-procedural-data baseline plus Procedural Warmup, Reasoning Gym, and SynLogic.
• Task-level analysis finds semantic validity alone doesn't guarantee training utility; compact targets and calibrated difficulty are highlighted as key design factors.
• Audits combining model-assisted review, human adjudication, and regression testing exposed subtle mismatches among generation, rendering, targets, and scoring — in Reasoning Core and the comparison collections alike.