AdaptiveEvolve: A Novel Approach to Synthetic Instruction Dataset Generation with Adaptive Quality Assessment

Instruction-tuning has emerged as a critical technique for aligning large language models (LLMs) with human intent, but the quality of instruction datasets significantly impacts model performance. In this paper, we introduce AdaptiveEvolve, a novel end-to-end pipeline for generating synthetic instruction datasets that combines instruction evolution with a new adaptive quality assessment framework. Our approach uniquely integrates educational value metrics into the instruction filtering process, addressing a critical gap in existing methods. We implement and evaluate a complete pipeline that generates base instructions via SelfInstruct, evolves them using targeted complexity transformations, and filters them through our proposed quality assessment system. Our experiments include two complementary seed instruction approaches: direct instruction seeds and persona-based generation inspired by the finepersonas dataset. Comparative analysis against benchmark datasets (FLAN, Alpaca, and T0) demonstrates that AdaptiveEvolve instructions are significantly longer (3.06-5.06× longer), achieve higher educational value scores (39-160% higher than most benchmarks), and maintain a 96-100% qualification rate on quality thresholds. The pipeline’s modular design enables targeted adaptation to different domains and quality criteria, overcoming limitations in previous approaches. We release our complete implementation and evolved dataset to support reproducibility and further research in high-quality instruction-tuning data generation without human curation.