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arXiv · 2503.09029

DAST: Difficulty-Aware Self-Training on Large Language Models

Boyang Xue·Qi Zhu·Hongru Wang·Rui Wang·Sheng Wang·Hongling Xu·Fei Mi·Yasheng Wang·Lifeng Shang·Qun Liu·Kam-Fai Wong
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Abstract

Present Large Language Models (LLM) self-training methods always under-sample on challenging queries, leading to inadequate learning on difficult problems which limits LLMs' ability. Therefore, this work proposes a difficulty-aware self-training (DAST) framework that focuses on improving both the quantity and quality of self-generated responses on challenging queries during self-training. DAST is specified in three components: 1) sampling-based difficulty level estimation, 2) difficulty-aware data augmentation, and 3) the self-training algorithm using SFT and DPO respectively. Experiments on mathematical tasks demonstrate the effectiveness and generalization of DAST, highlighting the critical role of difficulty-aware strategies in advancing LLM self-training.

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Boyang Xue, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Hongling Xu, Fei Mi, Yasheng Wang, Lifeng Shang, Qun Liu, Kam-Fai Wong. 2025-03-12. DAST: Difficulty-Aware Self-Training on Large Language Models. https://arxiv.org/abs/2503.09029

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