A Statistical Perspective on Knowledge Distillation: Foundations, Classical Methods, and Large Language Model Extensions
Knowledge Distillation (KD) has emerged as a vital paradigm for transferring the capabilities of high-capacity models to efficient ``student'' counterparts, addressing critical challenges in computational cost, deployment constraints, and privacy-sensitive settings. Although KD is widely used in practice, it is often viewed primarily as an engineering technique, with a unified statistical perspective remaining less developed. This review bridges that gap by presenting a unified Bayesian formulation of KD that formulates teacher predictions as prior information. This provides a principled interpretation of how teacher information is incorporated into student learning and establishes a rigo...
| Authors | Luyang Fang, Haoran Lu, Jiazhang Cai et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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