High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present TAG-INSTRUCT, a novel framework that enhances instruction complexity through structured semantic compression and controlled difficulty augmentation. Unlike previous prompt-based methods operating on raw text, TAG-INSTRUCT compresses instructions into a compact tag space and systematically enhances complexity through RL-guided tag expansion. Through extensive experiments, we show that TAG-INSTRUCT outperforms existing instruction complexity augmentation approaches. Our analysis reveals that operating in tag space provides superior controllability and stability across different instruction synthesis frameworks.
@article{arxiv.2505.18557,
title = {TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation},
author = {He Zhu and Zhiwen Ruan and Junyou Su and Xingwei He and Yun Chen and Wenjia Zhang and Guanhua Chen},
journal= {arXiv preprint arXiv:2505.18557},
year = {2025}
}