Training Objective

Training objectives in machine learning guide model learning by defining what constitutes optimal performance. Current research focuses on improving these objectives across various model architectures, including transformers and diffusion models, to address issues like length exploitation in preference optimization, inefficient finetuning, and inconsistent explanations. This work aims to enhance model accuracy, efficiency, and fairness, impacting fields such as speech enhancement, large language model alignment, and healthcare decision-making by producing more reliable and ethically sound AI systems. The ultimate goal is to better align model behavior with desired outcomes, leading to improved performance and interpretability.