arXiv Machine Learning
Aug 4

Question Begets Question: Self-Evolving Curriculum for Reinforcement Fine-Tuning on Competition Mathematics

arXiv:2608. 01522v1 Announce Type: new Abstract: Teaching a language model a skill it has not mastered is obstructed by three recurring difficulties: training data is scarce, ground-truth reasoning traces are usually unavailable, and models often exhibit an apparent ceiling beyond which additional data yields no further improvement.

By Longtian Bao, Jianyou Wang, Yang Zhang, Youze Zheng, Ramamohan Paturi
arXiv Machine Learning
Jun 15

Self-Evolving Visual Questioner

arXiv:2606. 13929v1 Announce Type: cross Abstract: Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains underexplored.

By Yijun Liang, Hengguang Zhou, Ming Li, Lichen Li, Cho-Jui Hsieh, Tianyi Zhou