Which term refers broadly to the function that the model aims to minimize during training?

The term that refers broadly to the function that the model aims to minimize during training is the objective function. In the context of machine learning and generative AI, the objective function quantifies how well the model performs in relation to the task at hand. During training, the model seeks to adjust its parameters in order to minimize the value of this function, thereby improving its predictions or outputs.

The objective function can take various forms depending on the specific task—such as mean squared error for regression problems or cross-entropy loss for classification tasks. The minimization process typically involves algorithms that compute the gradients of the objective function with respect to the model parameters, guiding the optimization process.

The other terms mentioned serve different purposes. Synchronous updates and asynchronous updates refer to methodologies for updating model parameters in distributed training environments, focusing on how and when the model parameters are adjusted based on training data. Gradient checkpointing is a technique employed to manage memory usage during the training of large models, allowing for efficient computation of gradients without holding all intermediate activations in memory. These concepts, while essential to model training and optimization, do not encapsulate the core concept of the function being minimized, which is the essence of the objective function.