While training a text-generation model, the loss does not converge after several epochs and validation accuracy oscillates. To help the model generalize, which adjustment is most appropriate?

When a model's loss does not converge and validation accuracy oscillates, it often indicates instability during training, possibly due to a learning rate that is too high. A lower learning rate allows for smaller, more stable updates to the model's weights, helping the loss to converge smoothly. Increasing the mini-batch size provides a more accurate estimate of the gradient, which can also contribute to more stable training and better generalization by reducing the variance in gradient updates. Conversely, increasing the learning rate or decreasing the mini-batch size would likely exacerbate the instability. Progressively increasing the learning rate is generally used for specific learning rate schedules, but not typically as a first step to address non-convergence and oscillation.