Gradient accumulation is a technique used in deep learning to improve training efficiency by accumulating gradients over multiple iterations before updating model weights. This approach can help reduce memory usage and increase the number of steps that can be taken during training, but it also introduces additional complexity and potential for instability. Researchers have proposed various methods to stabilize gradient accumulation, including techniques such as clipping and normalization. --- Why it matters: This matters because it affects how efficiently large models can be trained on limited hardware resources, which is a significant challenge in many AI applications. Source: https://huggingface.co/blog/gradient_accumulation