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CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning

arXiv – CS AI|Zijun Gao, Zhikun Xu, Xiao Ye, Ben Zhou|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce CORE (Concept-Oriented REinforcement), a new training framework that improves large language models' mathematical reasoning by bridging the gap between memorizing definitions and applying concepts. The method uses concept-aligned quizzes and concept-primed trajectories to provide fine-grained supervision, showing consistent improvements over traditional training approaches across multiple benchmarks.

Key Takeaways

→CORE addresses the problem where LLMs can solve math exercises but fail to apply concepts when genuine understanding is required.
→The framework uses explicit concepts as controllable supervision signals rather than just reinforcing final answers.
→CORE synthesizes concept-aligned quizzes and injects concept snippets during training rollouts to improve reasoning.
→The method shows consistent gains over vanilla and supervised fine-tuning baselines on both in-domain and out-of-domain math benchmarks.
→CORE remains algorithm- and verifier-agnostic while providing fine-grained conceptual supervision for mathematical reasoning.

#machine-learning #mathematical-reasoning #reinforcement-learning #llm-training #concept-learning #ai-research #arxiv

Read Original →via arXiv – CS AI

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