Large language models (LLMs) have revolutionized how we interact with technology, but their massive size presents significant hurdles for deployment. Training and running these behemoths demand extensive resources, putting them out of reach for many. What if smaller, more manageable models could achieve comparable performance? New research explores a clever technique called dynamic self-distillation from the previous mini-batch (DynSDPB), offering a promising path to empower smaller language models. The core idea is surprisingly simple yet effective: let the model learn from itself. Instead of relying on a larger 'teacher' model, DynSDPB allows a smaller model to distill knowledge from its own predictions on previous batches of data. This 'self-teaching' process leverages a model's evolving understanding of the task, creating a feedback loop that refines its learning over time. However, simply mimicking past predictions isn't enough. Early in training, a model's predictions are often inaccurate. DynSDPB addresses this challenge by dynamically adjusting how much influence these past predictions have. As the model becomes more confident, the weight given to earlier predictions decreases, ensuring the model isn't misled by its initial mistakes. This adaptive approach also accounts for variations in the output length of text generation tasks, aligning predictions even when the number of generated tokens changes between iterations. The results are compelling. Experiments on various language understanding and generation tasks show that DynSDPB significantly boosts the performance of smaller language models. Remarkably, these smaller models, trained with DynSDPB, sometimes even outperform larger models trained with traditional methods. Moreover, DynSDPB helps mitigate the vanishing gradient problem, a common training challenge for deep learning models, further enhancing performance and stability. This self-distillation technique also neatly integrates with other existing methods like self-correction and self-training, promising even greater improvements in future research. This method’s simplicity and cost-effectiveness offer a practical way to bring the power of LLMs to a wider audience, paving the way for more accessible and efficient AI applications.