A Unified Revisit of Temperature in Classification-Based Knowledge Distillation
A central idea of knowledge distillation is to expose relational structure embedded in the teacher's weights for the student to learn, which is often facilitated using a temperature parameter. Despite its widespread use, there remains limited understanding on how to select an appropriate temperature value, or how this value depends on other training elements such as optimizer, teacher pretraining/finetuning, etc. In practice, temperature is commonly chosen via grid search or by adopting values from prior work, which can be time-consuming or may lead to suboptimal student performance when training setups differ. In this work, we posit that temperature is closely linked to these training co...
| Authors | Logan Frank, Jim Davis |
|---|---|
| Primary category | cs.LG |
| Published | 2 March 2026 |
| Last updated at source | 4 March 2026 |
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