CORE: Contrastive Reflection Enables Rapid Reasoning Improvement with Minimal Samples
arXiv·medium signal
Language models using verifiable rewards typically need hundreds of training samples for reasoning improvements. CORE achieves comparable gains with far fewer samples through contrastive reflection — comparing successful and failed reasoning traces. Practical for teams wanting to improve reasoning capabilities without massive data collection.