Domain generalization (DG) aims to enhance the ability of model learning from source domains to generalize to other unseen domains. Existing gradient-based methods focus on learning better domain-invariant features using gradients from multiple source domains, but do not consider the impact of gradient granularity on model training. In this paper, we rethink how to mitigate the gradient conflicting problem from an optimization perspective. The limitations of existing gradient-based methods are theoretically analyzed in terms of modification ratio and modification frequency, showing that gradient granularity is a key factor in ensuring correct modification of the gradient. To address this issue, a gradient modification method, called CorGrad, is proposed by layering and slicing refinement operations to increase the modification frequency and the modification ratio. It can better reduce domain-specific information so that the model can learn better domain-invariant features. Finally, extensive experiments are conducted to verify the effectiveness of the proposed CorGrad, and the results show that the proposed CorGrad can obtain competitive performance in five DG benchmarks, and an average performance of 60.4% can be obtained on the DomainBed when using ResNet18 as the backbone. The code is publicly available at unmapped: uri https://github.com/libzwo/CorGrad.