Training Paradigm

Training paradigms in machine learning encompass the methods used to optimize model performance, focusing on efficient and effective learning strategies. Current research emphasizes improving training efficiency for large language models (LLMs) through techniques like dynamic data sampling (e.g., Learn, Focus, and Review), adaptive learning rate scheduling, and multi-agent frameworks for simulating complex learning environments. These advancements aim to reduce training costs, enhance model generalization, and address challenges like catastrophic forgetting and the need for personalized learning, ultimately impacting various fields from natural language processing to medical image analysis and robotics.