Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation

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.