On the Generalization of Knowledge Distillation: An Information-Theoretic View
Knowledge distillation is widely used to improve generalization in practice, yet its theoretical understanding remains elusive. In the standard distillation setting, a teacher model provides soft predictions to guide the training of a student model. We model teacher and student training as coupled stochastic processes and introduce a distillation divergence, defined as the Kullback-Leibler divergence between these two stochastic kernels. Within this framework, we derive two generalization bounds for the student model relative to the teacher's generalization gap: an upper bound under a sub-Gaussian assumption via algorithmic stability, and a lower bound under a central condition with sharp...
| Authors | Bingying Li, Haiyun He |
|---|---|
| Primary category | cs.IT |
| Published | 13 May 2026 |
| Last updated at source | 15 May 2026 |
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