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CORE then (i) synthesizes concept-aligned quizzes, (ii) injects brief concept snippets during rollouts to elicit concept-primed trajectories, and (iii) reinforces c","title":"CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning","url":"https://arxiv.org/abs/2512.18857","vendor":"arxiv_cs_ai"},"summary":"arXiv:2512.18857v3 Announce Type: replace \nAbstract: Large language models (LLMs) often solve challenging math exercises yet fail to apply the concept right when the problem requires genuine understanding. Popular Reinforcement Learning with Verifiable Rewards (RLVR) pipelines reinforce final answers but provide little fine-grained conceptual signal, so models improve at pattern reuse rather than conceptual applications. We introduce CORE (Concept-Oriented REinforcement), an RL training framework that turns explicit concepts into a controllable supervision signal. 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