| title | D^3-MOPD: Adaptive Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation |
|---|---|
| authors | Zechen Sun, Zhiwei Zhang, Fei Zhao, Juntao Li, Mu Chuan, Huayu Deng, Guojian Zhan, Wenliang Chen, Yao Hu, Min Zhang |
| date | 2026-08-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.24987 |
| affiliation | AllSpark Research |
| upvotes | 28 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Multi-teacher on-policy distillation (MOPD) distills several domain-expert teachers into a single student by minimizing per-domain reverse-KL divergence on the student's own rollouts. Existing approaches typically fix the per-domain data mixture before training, overlooking the fact that different domains converge at substantially different rates: some plateau early while others continue to improve throughout the training budget. A fixed mixture therefore wastes compute on fast-converging domains and undertrains slower-converging ones. To address this, we propose D^3-MOPD (Dynamic Domain ScheDuling for MOPD), a zero-overhead scheduler that repurposes the per-domain reverse-KL signal already produced during training to adapt the domain mixture online. Running asynchronously outside the training process, an off-process watcher periodically tracks each domain's KL trajectory, estimates remaining headroom and current improvement rate, and accordingly adjusts the domain sampling ratios without altering the core training loop. Our D^3-MOPD scales naturally to arbitrary numbers of domains, and the expected benefit grows as more domains introduce more diverse convergence patterns for the scheduler to exploit. On a Qwen3.6-35B-A3B student distilled from four domain-expert teachers, D^3-MOPD closes 97% of the average student-to-teacher performance gap, compared with 63% for vanilla MOPD, reaches the same peak performance with an approximately 3times reduction in rollout steps, and surpasses the specialist teachers on three of seven benchmarks. |
cite
@misc{sun2026d,
title={D^3-MOPD: Adaptive Dynamic Domain ScheDuling for Efficient Multi-Teacher Distillation},
author={Zechen Sun and Zhiwei Zhang and Fei Zhao and Juntao Li and Mu Chuan and Huayu Deng and Guojian Zhan and Wenliang Chen and Yao Hu and Min Zhang},
year={2026},
eprint={2608.24987},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.24987}
}