Batch size is a critical hyperparameter in machine learning that determines how many samples are processed before the model’s internal parameters are updated. It significantly influences various aspects of model training, including performance, training costs, and generalization capabilities. Research has shown that different batch sizes can lead to varying outcomes in these areas, making it essential to choose an appropriate size for your specific task ¹.

One notable effect of batch size is its relationship with model loss. Generally, increasing the batch size can lead to a reduction in performance. This is because larger batch sizes may cause models to get stuck in local minima, while smaller batches are more likely to explore the solution space and find global minima ¹. Additionally, larger batch sizes can result in fewer updates during training, which may contribute to a generalization gap ¹.

To mitigate the negative effects of larger batch sizes, it is often necessary to adjust the learning rate accordingly. Studies have indicated that when the learning rate is properly tuned, the differences in validation loss across various batch sizes can be minimized ¹³.