DeepSeek's R1 model uses a batch size of 32 during its training process. Batch size refers to the number of training examples utilized in one iteration of training. Selecting an appropriate batch size is crucial because it impacts the model's convergence, training speed, and the generalization capability of the model.
Using a batch size of 32 allows for a balanced approach. A smaller batch size could lead to more noisy updates to the model parameters, which might help it escape local minima but can also result in unstable training. In contrast, a larger batch size could provide more stable updates but might require more memory and can lead to longer training times. The choice of 32 strikes a common balance, making it a popular choice among developers. It enables the model to learn efficiently without overstressing computational resources.
Additionally, this batch size allows the model to generalize better to unseen data. With 32 samples in each batch, the model can be updated frequently, which helps capture the underlying patterns in the data without being overly influenced by outliers. This is especially important when working with complex datasets, as it helps in building a more robust model that performs well in various scenarios. Overall, the batch size of 32 plays a significant role in the training dynamics and performance of the DeepSeek R1 model.