用强化学习+大模型，让学习路径自动匹配个人目标。
Personalized Learning Path Planning with Goal-Driven Learner State Modeling
- 构建结构化学习状态模型，将目标转化为可计算奖励信号。
- 结合SFT与GRPO训练策略，在真实平台生成连贯个性化路径。
- 适合教育科技研发者和个性化学习系统设计者参考。
个性化学习路径规划（PLPP）旨在设计与个体目标一致的自适应学习路径。尽管大型语言模型（LLMs）在个性化学习中展现潜力，现有方法常缺乏目标对齐的规划机制。我们提出Pxplore框架，融合基于强化学习的训练范式与LLM驱动的教育架构。设计了结构化的学习者状态模型和自动化奖励函数，将抽象目标转化为可计算信号。采用监督微调（SFT）与组相对策略优化（GRPO）联合训练策略，并部署于真实学习平台。大量实验验证了Pxplore在生成连贯、个性化且目标导向的学习路径方面的有效性。代码与数据集已开源：https://github.com/Pxplore/pxplore-algo。
原文摘要 · Abstract (English)
Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizing learning experiences, existing approaches often lack mechanisms for goal-aligned planning. We introduce Pxplore, a novel framework for PLPP that integrates a reinforcement-based training paradigm and an LLM-driven educational architecture. We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals. We train the policy combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), and deploy it within a real-world learning platform. Extensive experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths. We release our code and dataset at https://github.com/Pxplore/pxplore-algo.
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