  Instruction Fine-Tuning enhances pre-trained language models from basic
next-word prediction to complex instruction-following. However, existing
One-off Instruction Fine-Tuning (One-off IFT) method, applied on a diverse
instruction, may not effectively boost models' adherence to instructions due to
the simultaneous handling of varying instruction complexities. To improve this,
Phased Instruction Fine-Tuning (Phased IFT) is proposed, based on the idea that
learning to follow instructions is a gradual process. It assesses instruction
difficulty using GPT-4, divides the instruction data into subsets of increasing
difficulty, and uptrains the model sequentially on these subsets. Experiments
with Llama-2 7B/13B/70B, Llama3 8/70B and Mistral-7B models using Alpaca data
show that Phased IFT significantly outperforms One-off IFT, supporting the
progressive alignment hypothesis and providing a simple and efficient way to
enhance large language models. Codes and datasets from our experiments are
freely available at https://github.com/xubuvd/PhasedSFT.
