The field of knowledge distillation and efficient model deployment is rapidly evolving, with a focus on developing innovative methods to transfer knowledge from complex teacher models to smaller, resource-efficient student models. Recent research has explored the use of counterfactual explanations, dynamic distillation frameworks, and ternary weights to improve the efficiency and effectiveness of knowledge distillation. Additionally, there is a growing interest in applying knowledge distillation to multilingual vision-language models and exploring its potential in debiasing and calibration. Noteworthy papers in this area include: Few-Shot Knowledge Distillation of LLMs With Counterfactual Explanations, which introduces a novel strategy for few-shot task-aware knowledge distillation using counterfactual explanations. TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge, which proposes a lightweight computational framework that converts connection weights of vision and text encoders into the ternary format. A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models, which introduces a novel test-time prompt tuning framework that encourages uniformity in the distribution of normalized textual features.