Montessori-Instruct: Generate Influential Training Data Tailored for
  Student Learning
Authors: name: Xiaochuan Li, name: Zichun Yu, name: Chenyan Xiong

Synthetic data has been widely used to train large language models, but their
generative nature inevitably introduces noisy, non-informative, and misleading
learning signals. In this paper, we propose Montessori-Instruct, a novel data
synthesis framework that tailors the data synthesis ability of the teacher
language model toward the student language model's learning process.
Specifically, we utilize local data influence of synthetic training data points
on students to characterize students' learning preferences. Then, we train the
teacher model with Direct Preference Optimization (DPO) to generate synthetic
data tailored toward student learning preferences. Experiments with
Llama3-8B-Instruct (teacher) and Llama3-8B (student) on Alpaca Eval and
MT-Bench demonstrate that Montessori-Instruct significantly outperforms
standard synthesis methods by 18.35\% and 46.24\% relatively. Our method also
beats data synthesized by a stronger teacher model, GPT-4o. Further analysis
confirms the benefits of teacher's learning to generate more influential
training data in the student's improved learning, the advantages of local data
influence in accurately measuring student preferences, and the robustness of
Montessori-Instruct across different student models. Our code and data are
open-sourced at https://github.com/cxcscmu/Montessori-Instruct.