@inproceedings{zhang-etal-2026-find,
    title = "Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation",
    author = "Zhang, Hengyuan  and
      Yang, Shiping  and
      Liang, Xiao  and
      Shang, Chenming  and
      Jiang, Yuxuan  and
      Tao, Chaofan  and
      Xiong, Jing  and
      So, Hayden Kwok-Hay  and
      Xie, Ruobing  and
      Chang, Angel X  and
      Wong, Ngai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.666/",
    pages = "14619--14637",
    ISBN = "979-8-89176-390-6",
    abstract = "Training student models on synthetic data generated by strong teacher models is a promising approach to distilling the capabilities of teachers. However, existing studies reveal that stronger models are not always optimal teachers, suggesting a mismatch between the teacher{'}s output and the student{'}s learning ability. To address this issue, we propose PerSyn (Personalized data Synthesis), a novel and efficient approach that customizes synthetic data to align with the learning capabilities of the student model. Specifically, our PerSyn method routes each prompt to its optimal teacher via a query-level router that jointly considers the student models' learnability and teacher models' response quality. It successfully transfers the synthesis paradigm from the conventional ``Generate then Select'' to a more efficient manner, i.e., ``Route then Generate'', eliminating the need for all teacher models to generate parallel responses across the entire prompt set. Extensive experiments across different model families and scales demonstrate that PerSyn consistently outperforms all baselines on six benchmarks, including instruct tuning and math reasoning settings. Further analysis verifies the effectiveness of PerSyn and offers extra insights to propel future research. Our code is available at https://anonymous.4open.science/r/PerSyn-8D85."
}