Tag-Instruct: Controlled Instruction Complexity Enhancement through Structure-based Augmentation (2025)

Abstract: 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.

## introduction large language models (llms) have demonstrated remarkable success across diverse tasks @xcite @xcite , from natural language understanding @xcite to complex reasoning @xcite . to fully realize their potential, these models require high-quality instruction-tuning data, which plays a crucial role in model post-training and reinforcement learning initialization @xcite @xcite . this recognition has led to extensive research in instruction data synthesis @xcite @xcite . while recent work has made progress in synthetic data generation @xcite , current instruction datasets still lack sufficient complexity to fully develop model capabilities @xcite . studies show that model performance strongly correlates with instruction difficulty @xcite , yet existing approaches struggle to effectively control and scale instruction difficulty. specifically, @xcite and its follow-up works @xcite rely on models' prior knowledge for self-reflection on augmentation directions, typically using prompts to directly generate "harder" instructions. however, this approach lacks precise control over difficulty progression, as the concept of "harder" remains ambiguous and unquantifiable, making it challenging to systematically guide the augmentation process. while @xcite attempts to address this by discretizing instructions into semantic trees for manipulation, it still struggles to identify which semantic components are worth augmenting and how to effectively guide the augmentation direction. we name these approaches as prompt-based augmentation, which face two fundamental challenges: (1) difficulty in identifying which semantic components are worth augmenting, and (2) lack of principled guidance on which augmentation direction would lead to meaningful complexity increases. from a linguistic perspective, instructions contain both essential semantic components with high information density and auxiliary content that merely contributes to fluency @xcite . this observation reveals why prompt-based augmentation struggles -by operating directly on raw instructions, it works in an unnecessarily complex search space that includes both essential and auxiliary content. to address these challenges, we propose structure-based augmentation, drawing inspiration from representation learning in variational autoencoders (vaes) @xcite . our key insight is that by compressing instructions into a structured tag space, we can (1) identify valuable semantic components by focusing only on essential information, and (2) guide augmentation direction by quantitatively measuring each tag's contribution to instruction complexity. this transforms the augmentation problem from unstructured token-level modifications to systematic concept-level operations @xcite . building on this intuition, we propose tag-instruct, a novel compress & operation framework for controlled difficulty augmentation. our framework operates through a three-stage iterative pipeline: (1) semantic encoding that compresses instructions into a compact tag representation while preserving task-relevant information, (2) rl-based tag expansion that explores the tag space to discover new semantic tags that meaningfully increase complexity. (3) instruction synthesis that generates enhanced instructions by conditioning on both the expanded tag set and original instruction. this iterative process enables progressive difficulty increases while maintaining semantic coherence. extensive experiments demonstrate the effectiveness of tag-instruct across diverse settings. using alpaca-5k @xcite as the base dataset, our method enables llama-3-8b to achieve performance comparable to gpt-4-turbo on standard benchmarks, significantly outperforming existing instruction augmentation approaches. through detailed ablation studies, we verified that operating in tag space provides superior controllability and makes different instruction synthesis frameworks more stable and effective. foot_0 2 related work instruction data augmentation high-quality instruction data is fundamental for model alignment and performance @xcite . for instruction synthesis from scratch, @xcite proposed automatic generation through bootstrapping from human demonstrations, though constrained by seed data. @xcite advanced this by introducing pre-query templates for direct instruction construction, while @xcite improved data quality using taggingbased prompts and ucb-based bootstrapping. ge et al. (2024) ## conclusion in this paper, we propose tag-instruct, a novel framework for enhancing instruction complexity through structured semantic compression and controlled difficulty augmentation. by operating in a compressed tag space rather than raw text, our approach enables more precise and controllable instruction enhancement. empirical results on al-pacaeval 2 demonstrate that tag-instruct outperforms existing instruction augmentation methods. our analysis shows that structured semantic manipulation enables controlled progression of instruction difficulty while maintaining coherence across different synthesis frameworks. the effectiveness of our approach suggests that structured semantic manipulation is a promising direction for instruction optimization in language models.