新闻
Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers released Reasoning Core, a collection of 50 procedural generators for creating training data across mathematics, logic, planning, and code domains.
- 为何重要
- Engineers fine-tuning language models need diverse, verifiable reasoning problems at scale for completion-supervised training.
- 注意
- Procedural generation alone does not guarantee correctness; semantic validity does not ensure training utility without proper difficulty calibration.
- reasoning
- fine-tun
- eval
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