Intermediate Training

Intermediate training, a technique where models are pre-trained on related but simpler tasks before tackling the main objective, is being actively explored to improve the performance and efficiency of various machine learning models. Current research focuses on optimizing intermediate task selection and leveraging different model architectures (e.g., Transformers) and algorithms (e.g., clustering, imitation learning) to effectively transfer knowledge. This approach shows promise in addressing data scarcity issues, improving generalization across domains and languages, and enhancing the robustness of models in challenging applications like multimodal sentiment analysis and automatic speech recognition. The ultimate goal is to develop more efficient and effective training strategies for complex machine learning tasks.