Large-scale neural networks have achieved remarkable performance in various language tasks, yet their ability to generalize across diverse datasets remains a significant challenge. The introduction of Dynamic Embedding Perturbation (DEP) presents a novel approach by applying controlled stochastic variations to the embedding layer during training, enabling the model to navigate a broader range of input representations and reduce overfitting. Experiments conducted on a leading opensource language model demonstrated substantial improvements in perplexity for language modeling, as well as enhanced accuracy in summarization and sentiment analysis tasks. DEP's effectiveness stems from its ability to introduce randomness early in training while maintaining the semantic integrity of the input, ultimately leading to better generalization and robustness across multiple tasks. These findings suggest that DEP offers a promising direction for improving the generalization capabilities of large-scale models, paving the way for more resilient and adaptive systems in future language applications.