Abstract
Comment: 20 pages, 3 figures. Code: https://github.com/sileod/reasoning-core Data: https://hf.co/collections/reasoning-core/datasets
Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
- Authors
- Damien Sileo, Valentin Lacombe, Dimitri Kachler
- Categories
- cs.CL
Brief
Reasoning Core is a curated library of 50 procedural generators designed for completion‑supervised reasoning fine‑tuning. The authors evaluate it against Procedural Warmup, Reasoning Gym, and SynLogic across four base‑model sizes and training durations; in the primary 3B setting it achieves highest mean performance on DROP, LogiQA, and ARC‑Challenge. Audits show semantic validity isn't sufficient; compact targets and calibrated difficulty improve training utility. Code and datasets are public.
Why it matters
Reasoning Core is a curated collection of 50 procedural generators spanning mathematics, logic, planning, state tracking, formal languages, structured data, games, causality, and code; each generator includes semantic scorers, difficulty controls, and task evaluators.
Key details
- In a matched completion‑supervised protocol (primary comparison on a 3B base model), Reasoning Core achieved the highest mean scores on DROP, LogiQA, and ARC‑Challenge, outperforming a no‑procedural baseline and three alternative procedural collections (Procedural Warmup, Reasoning Gym, SynLogic); the paper (published 2026-08-05) and code/data are publicly released, and audits revealed generation/rendering/target/scoring mismatches.