无限指令：扩展指令选择与合成以增强语言模型

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

June 9, 2025

作者: Jijie Li, Li Du, Hanyu Zhao, Bo-wen Zhang, Liangdong Wang, Boyan Gao, Guang Liu, Yonghua Lin

cs.AI

摘要

大型语言模型（LLMs）在实际应用中展现出强劲性能，然而现有的开源指令数据集往往局限于狭窄领域，如数学或编程，这限制了模型的泛化能力，并拉大了与专有模型之间的差距。为弥合这一差距，我们推出了Infinity-Instruct，一个旨在通过两阶段流程提升LLMs基础与对话能力的高质量指令数据集。在第一阶段，我们采用混合数据筛选技术，从超过1亿样本中精选出740万条高质量基础指令（InfInstruct-F-7.4M）。第二阶段，通过包含指令选择、进化及诊断过滤的两步过程，合成了150万条高质量对话指令（InfInstruct-G-1.5M）。我们通过微调包括Mistral、LLaMA、Qwen和Yi在内的多个开源模型，对Infinity-Instruct进行了实证评估，观察到在基础与指令跟随基准测试上均取得显著性能提升，持续超越官方指令调优版本。特别地，InfInstruct-LLaMA3.1-70B在指令跟随任务上以8.6%的优势超越GPT-4-0314，同时保持相当的基础性能。这些结果凸显了基础与对话训练之间的协同效应，为LLM的全面发展提供了新见解。我们的数据集https://huggingface.co/datasets/BAAI/Infinity-Instruct和代码https://gitee.com/li-touch/infinity-instruct已公开发布。

English

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 datasethttps://huggingface.co/datasets/BAAI/Infinity-Instruct and codeshttps://gitee.com/li-touch/infinity-instruct have been publicly released.