UNO! UNified Offline Training Paradigm for Learning Path Recommendation
Linzhi Peng, Wentao Zhu, Ke Cheng, Heng Chang, Junchen Ye, Bowen Du, Weifeng Lv
Abstract
With the wide adoption of online education platforms, adaptive learning systems have become increasingly important. Learning Path Recommendation (LPR) aims to dynamically adjust learning content to optimize learning efficiency based on individual student needs. However, current LPR methods suffer from sparse reward for precise assessment and only focus on anonymous sessions that overlook more personalized and effective paths. To address these challenges, we propose UNO, UNified Offline Training Paradigm for Learning Path Recommendation. This approach introduces an offline training paradigm in RL-based LPR to provide dense process rewards by a personalized advantage based on a reward model, which can estimate the students
BibTeX
@inproceedings{aaai2026_unounifiedofflin,
title = {UNO! UNified Offline Training Paradigm for Learning Path Recommendation},
author = {Linzhi Peng and Wentao Zhu and Ke Cheng and Heng Chang and Junchen Ye and Bowen Du and Weifeng Lv},
booktitle = {AAAI 2026},
year = {2026}
}