What process involves using groups to compute and aggregate gradients for training efficiency?

The process that involves using groups to compute and aggregate gradients for training efficiency is batch training. This approach is essential in machine learning, where the model is updated not after every single training example but rather after processing a group (or batch) of them. By doing so, batch training can significantly improve computational efficiency compared to single instance processing, where each example is processed individually.

Batch training allows the model to calculate the average gradients of the loss function over all samples within the batch, which stabilizes the training process and often results in more efficient convergence toward the optimal parameters of the model. It tends to better utilize computational resources, enabling parallel processing on hardware like GPUs, which can lead to faster training times.

In contrast, online learning involves updating the model after each individual training instance, which can be less efficient in terms of computation and often results in more noisy gradient updates. Single instance processing refers to processing one example at a time, which doesn't leverage the advantages of batch computations. Real-time processing typically focuses on the immediate use of data for inference or decision-making rather than the batch-based approach used for training. Thus, batch training is the most suitable answer for the question posed.