Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models

Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as mathematics or coding, limiting generalization and widening the gap with proprietary models. To bridge this gap, we introduce Infinity-Instruct, a high-quality instruction dataset designed to enhance both foundational and chat capabilities of LLMs through a two-phase pipeline. In Phase 1, we curate 7.4M high-quality foundational instructions (InfInstruct-F-7.4M) from over 100M samples using hybrid data selection techniques. In Phase 2, we synthesize 1.5M high-quality chat instructions (InfInstruct-G-1.5M) through a two-stage process involving instruction selection, evolution, and diagnostic filtering. We empirically evaluate Infinity-Instruct by fine-tuning several open-source models, including Mistral, LLaMA, Qwen, and Yi, and observe substantial performance gains across both foundational and instruction following benchmarks, consistently surpassing official instruction-tuned counterparts. Notably, InfInstruct-LLaMA3.1-70B outperforms GPT-4-0314 by 8.6\% on instruction following tasks while achieving comparable foundational performance. These results underscore the synergy between foundational and chat training and offer new insights into holistic LLM development. Our dataset\footnote{this https URL} and codes\footnote{this https URL} have been publicly released.

@article{li2025_2506.11116,
  title={ Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models },
  author={ Jijie Li and Li Du and Hanyu Zhao and Bo-wen Zhang and Liangdong Wang and Boyan Gao and Guang Liu and Yonghua Lin },
  journal={arXiv preprint arXiv:2506.11116},
  year={ 2025 }
}