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

• URL: http://arxiv.org/abs/2503.09029v1
• Date: Wed, 12 Mar 2025 03:36:45 GMT
• Title: DAST: Difficulty-Aware Self-Training on Large Language Models
• Authors: Boyang Xue, Qi Zhu, Hongru Wang, Rui Wang, Sheng Wang, Hongling Xu, Fei Mi, Yasheng Wang, Lifeng Shang, Qun Liu, Kam-Fai Wong,
• Abstract summary: Large Language Models (LLM) self-training methods always under-sample on challenging queries.<n>This work proposes a difficulty-aware self-training framework that focuses on improving the quantity and quality of self-generated responses.
• Score: 68.30467836807362
• License: http://creativecommons.org/licenses/by/4.0/
• 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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