TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models

TL;DR

TAID introduces a dynamic distillation method that interpolates between teacher and student models to effectively transfer knowledge, reducing model size while maintaining high performance in language and vision-language tasks.

Contribution

The paper proposes TAID, a novel temporally adaptive distillation technique that addresses capacity gaps and mode collapse, enabling efficient knowledge transfer for smaller, high-performing models.

Findings

TAID prevents mode collapse during distillation.

TAID achieves superior performance across various model sizes.

Developed state-of-the-art compact models using TAID.

Abstract

Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression. A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation. To address these issues, we introduce $\textit{Temporally Adaptive Interpolated Distillation (TAID)}$, a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse. Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. Furthermore, we showcase TAID's practical impact by developing two state-of-the-art compact foundation models: $\texttt{TAID-LLM-1.5B}$ for language tasks and $\texttt{TAID-VLM-2B}$ for vision-language tasks. These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies.