EduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity

arXiv:2507.20335v2 Announce Type: replace-cross Abstract: Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs, and creativity that supports exploration. General-purpose models do not consistently satisfy these educational requirements, motivating explicit alignment with all three objectives. We present EduAlign, a framework that combines educational reward modeling with multi-objective reinforcement learning. To obtain training signals for these goals across educational scenarios, we develop a rubric-guided data synthesis pipeline that iteratively refines scoring criteria and generation prompts for each subject, grade band, and task type. The resulting supervision trains a multidimensional reward model, Edu-RM, to predict helpfulness, personalization, and creativity scores. To jointly optimize these educational objectives, we introduce Ideal-point Policy Optimization (IdealPO), a multi-objective reinforcement learning algorithm. IdealPO combines ideal-point marginal credit with continuous conflict weighting to guide policy updates that account for uneven progress and conflicting contributions across objectives. Experiments with 7B and 32B models show that EduAlign achieves the highest mean ratings on all three dimensions from both education experts and a model judge, alongside the highest aggregate scores on EduBench, ELMES, and EduValues among the compared methods. Reward-model validation and component ablations further support the effectiveness of the framework.

Abstract:Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs, and creativity that supports exploration. General-purpose models do not consistently satisfy these educational requirements, motivating explicit alignment with all three objectives. We present EduAlign, a framework that combines educational reward modeling with multi-objective reinforcement learning. To obtain training signals for these goals across educational scenarios, we develop a rubric-guided data synthesis pipeline that iteratively refines scoring criteria and generation prompts for each subject, grade band, and task type. The resulting supervision trains a multidimensional reward model, Edu-RM, to predict helpfulness, personalization, and creativity scores. To jointly optimize these educational objectives, we introduce Ideal-point Policy Optimization (IdealPO), a multi-objective reinforcement learning algorithm. IdealPO combines ideal-point marginal credit with continuous conflict weighting to guide policy updates that account for uneven progress and conflicting contributions across objectives. Experiments with 7B and 32B models show that EduAlign achieves the highest mean ratings on all three dimensions from both education experts and a model judge, alongside the highest aggregate scores on EduBench, ELMES, and EduValues among the compared methods. Reward-model validation and component ablations further support the effectiveness of the framework.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2507.20335 [cs.LG]

(or arXiv:2507.20335v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2507.20335 Focus to learn more arXiv-issued DOI via DataCite

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EduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity

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