arXiv:2601.01484v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, often by leveraging soft probabilistic outputs. While KD has shown strong empirical success in numerous applications, its theoretical underpinnings remain only partially understood. In this work, we adopt a Bayesian perspective on KD to rigorously analyze the convergence behavior of students trained with Stochastic Gradient Descent (SGD). We study two regimes: $(i)$ when the teacher provides the exact
The continuous evolution of AI models demands more efficient knowledge transfer methods, making theoretical advancements in Knowledge Distillation highly relevant as models become larger and more complex.
Improved theoretical understanding and guidelines for Knowledge Distillation can lead to more efficient and reliable AI model development, particularly for deploying smaller, high-performing models from larger teachers.
This research provides a deeper theoretical foundation for Stochastic Gradient Descent (SGD) based Knowledge Distillation, offering specific guidelines for its application and potentially enhancing the performance and stability of student models.
- · AI researchers
- · ML engineers
- · AI model developers
More robust and efficient training of smaller student models using Knowledge Distillation.
Accelerated deployment of AI models in resource-constrained environments due to optimized student models.
Increased accessibility and efficiency in developing and deploying specialized AI applications across various industries.
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Read at arXiv cs.LG