Hugging Face Trending Papers
Aug 17

Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models

On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student's own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a controlled study that varies one generalization factor at a time, from in-domain distribution shifts to cross-domain transfer and the multi-teacher setting.

arXiv AI
Aug 3

ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning

arXiv:2607. 28647v1 Announce Type: cross Abstract: This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum-aligned lesson design, interactive student learning, and feedback-driven refinement.

By Thang Doan Viet, Anh Nguyen Hoang, Tinh Luong Son, Anh Hoang Thi Ngoc, Huyen Giang Thi Thu, Tai Le Quy
arXiv Computation and Language
Sep 17

PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning

PersonaPath is a new benchmark for knowledge‑centric personalized learning path planning, pairing 2,000 learner personas with a hierarchical knowledge graph of 347 textbooks, 1,751 units, and 4,092 concepts across 77 subjects. The study evaluates large language models on this benchmark, finding that even the best model achieves only a 29.5% final pass rate in Basic Education and fails to exceed 44.7% in tailoring paths to individual learners, highlighting a significant adaptivity gap. This work underscores the challenge of moving beyond exercise‑centric recommendation toward goal‑oriented, curriculum‑scale guidance.

By Yu Liu, Zeming Liu, Tianle Zhang, Zihao Cheng, Yuhang Guo, Kehai Chen, Min Zhang, Yunhong Wang, Haifeng Wang
arXiv AI
Jun 17

Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects

arXiv:2606. 17706v1 Announce Type: cross Abstract: Curriculum learning couples two design choices, how samples are scored by difficulty and how harder samples are paced into training, making it difficult to attribute observed gains to either component.

By Savini Kommalage, Sanka Mohottala, Asiri Gawesha, Dulara Madhusanka, Menan Velayuthan, Dharshana Kasthurirathna, Mahima Milinda Alwis Weerasinghe, Charith Abhayaratne