Contextual Gradient Realignment for Large Language Model Training with Stochastic Latent Projection

Gradient instability presents a persistent challenge in Large Language Model (LLM) training, where stochastic variations in weight updates contribute to unpredictable optimization behavior. Conventional approaches rely on global constraints or indirect stabilization techniques, yet such strategies fail to leverage latent contextual dependencies that influence gradient trajectories. A novel optimization framework, Contextual Gradient Realignment (CGR), introduces a learned projection mechanism that modifies gradient updates within the latent space, ensuring that parameter adjustments remain consistent with structured representations developed throughout training. Empirical results indicate that CGR leads to improvements in convergence efficiency, preserving weight update coherence while preventing excessive fluctuations in parameter trajectories. Model generalization performance is assessed through validation and test accuracy metrics, demonstrating that structured gradient modifications enhance predictive capabilities without introducing computational bottlenecks. An extensive evaluation of gradient magnitude distributions and activation sparsity trends further confirms the effectiveness of CGR in facilitating stable weight propagation across transformer-based architectures. Computational efficiency remains within practical limits, with additional memory overhead offset through gains in optimization consistency and training stability. The findings suggest that CGR provides an effective gradient modification strategy that can be integrated into existing optimization frameworks to enhance the reliability and efficiency of large-scale neural network training.