The growing complexity and scale of modern deep learning models have improved the ability to generate and understand human language, yet challenges persist in achieving robust generalization and syntactic flexibility. Dynamic Syntactic Insertion (DSI) addresses these limitations through the novel introduction of random syntactic variations during the finetuning phase, enhancing the model's capacity to process diverse linguistic structures. Through empirical experiments on the GPT-NeoX architecture, significant performance improvements were observed across multiple metrics, including syntactic robustness, fluency, and generalization accuracy. The DSI-enhanced model consistently outperformed the baseline, particularly in handling syntactically complex and perturbed datasets, demonstrating its adaptability to a broader range of linguistic inputs. Furthermore, the incorporation of syntactic variability led to reductions in perplexity and increased performance across tasks on the GLUE benchmark, highlighting the method's effectiveness. The findings from this study suggest that syntactic augmentation techniques, such as DSI, provide a promising pathway for improving the resilience and adaptability of language models across diverse tasks and linguistic environments.