微软发布StudentSim：个性化学生模拟器训练框架
原标题：Paper page - StudentSim: Training LLM-based Student Simulators
AI 摘要
微软提出 StudentSim 框架，通过两阶段训练（池化训练加个性化微调）从稀疏数据生成个性化学生模拟器，使其既能模仿学生反应，又能响应导师指导。在象棋、英语写作和数学三个领域的 60 名学生基准 StudentSimEval 上，StudentSim 在行为保真度和指导响应性上均优于 GPT-5.4 和 Maia2。作为概念验证，使用 StudentSim 作为奖励模型训练的象棋导师获得了专家更高的评价。代码已开源。
正文节选
StudentSim: Training LLM-based Student Simulators Abstract StudentSim trains personalized student simulators from sparse data to mirror learner responses and adapt to tutor guidance, outperforming existing models across chess, writing, and math. AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow, and costly to collect from real learners. Student simulators can provide