TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation

2025-05-24

AI Summary

To address the challenge of precise instruction complexity control in large language model training, this paper proposes a structured augmentation framework grounded in semantic label space. Methodologically, it introduces a novel “instruction → semantic label” compression mapping, integrated with PPO-based reinforcement learning to enable controllable label-space manipulation and faithful reverse reconstruction—thereby achieving fine-grained complexity modulation and stable cross-task transfer. The framework unifies semantic structure modeling, synthetic logic, and complexity constraints within a single coherent architecture. Empirical evaluation across diverse instruction synthesis tasks demonstrates significant improvements: +23.6% in control accuracy and a 41% reduction in output variance, indicating enhanced generation stability. Notably, the approach exhibits strong generalization—adapting seamlessly to heterogeneous instruction frameworks without task-specific fine-tuning.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Enhance instruction complexity controllably for LLMs
• Compress instructions into structured tag space
• Improve complexity augmentation via RL-guided tag expansion

Innovation

• Structured semantic compression for instruction complexity
• RL-guided tag expansion for controlled difficulty
• Tag space operation enhances controllability and stability