Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs) . However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends towards better generalization.
In this paper, we exploresupervised multitask pre-trainingby proposingInstruction Pre-Training, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train LMs. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness ofInstruction Pre-Training. In pre-training from scratch,Instruction Pre-Trainingnot only consistently enhances pre-trained base models but also benefits more from further instruction tuning. In continual pre-training,Instruction Pre-Trainingenables Llama3-8B to be comparable to or even outperform Llama3-70B. Our model, code, and data are available athttps://github.com/microsoft/LMOps.