One core capability of large language models (LLMs) is to follow naturallanguage instructions. However, the issue of automatically constructinghigh-quality training data to enhance the complex instruction-followingabilities of LLMs without manual annotation remains unresolved. In this paper,we introduce AutoIF, the first scalable and reliable method for automaticallygenerating instruction-following training data. AutoIF transforms thevalidation of instruction-following data quality into code verification,requiring LLMs to generate instructions, the corresponding code to check thecorrectness of the instruction responses, and unit test samples to verify thecode's correctness. Then, execution feedback-based rejection sampling cangenerate data for Supervised Fine-Tuning (SFT) and Reinforcement Learning fromHuman Feedback (RLHF) training. AutoIF achieves significant improvements acrossthree training algorithms, SFT, Offline DPO, and Online DPO, when applied tothe top open-source LLMs, Qwen2 and LLaMA3, in self-alignment andstrong-to-weak distillation settings. Our code is publicly available athttps://github.com/QwenLM/AutoIF.