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Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation

Paper · 2026 · Hengyuan Zhang, Shiping Yang, Xiao Liang et al.

Summary

Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer every prompt before choosing, so cost grows with the size of the teacher pool. They also choose data per dataset rather than per prompt.

The authors propose PerSyn, which replaces this with a "Route then Generate" workflow. A router, trained with a Bradley-Terry preference objective, predicts for each prompt which teacher suits a particular student. Teachers are scored by a weighted blend of reward-model quality and learnability, measured as the student's log-likelihood of the response. Only the selected teacher then writes the response, and the student is trained by SFT on the resulting personalized dataset.

Across several model families and scales, in both instruction tuning and math reasoning, PerSyn matches or outperforms all baselines while reducing synthesis cost. This makes student-specific distillation more practical.

Key points

  • Reported gains: PerSyn improves results by up to 8.7% on IFEval and 7.5% on MATH. The router sends more than 95% of prompts to smaller teachers instead of one super-sized LLM.
  • New resource: the authors release PerSyn-Math, a math synthetic dataset with parallel responses from 15 teacher models.
  • Ablation: both reward components matter, but quality contributes more than learnability.
  • Long-CoT teachers (DeepSeek-R1, Qwen3): they are rarely optimal for small students but remain necessary for hard prompts.
    • Reassigning their prompts to Qwen2.5-Math-7B-Instruct lowered the Qwen2.5-3B student's average score by 1.3%.
    • That model solved only 7.4% of those prompts.
    • Training only on Long-CoT data (the Strong baseline) led to repetitive, non-terminating reasoning.
  • Limitations: experiments cover students up to 14B parameters and only instruction tuning and math. Generalization to code, multimodal or other domains is untested.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢TencentShenzhen · China134 works · 19,743 citations🎓University of California, Los AngelesLos Angeles · United States87 works · 54,961 citations🎓University of Hong KongPok Fu Lam · China89 works · 11,774 citations🔬Canadian Institute for Advanced ResearchToronto · Canada28 works · 16,725 citations🎓Dartmouth CollegeHanover · United States37 works · 42,625 citations🎓Simon Fraser UniversityBurnaby · Canada17 works · 4,413 citations🎓University of Maryland, Baltimore CountyBaltimore · United States14 works · 915 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang and Ngai Wong. 2026. Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.666

@inproceedings{zhang2026find,
  title = {{Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation}},
  author = {Hengyuan Zhang, Shiping Yang, Xiao Liang, Chenming Shang, Yuxuan Jiang, Chaofan Tao, Jing Xiong, Hayden Kwok-Hay So, Ruobing Xie, Angel X. Chang and Ngai Wong},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.666},
  url = {https://doi.org/10.18653/v1/2026.acl-long.666}
}

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