Fine-tuning large language models (LLMs) is like teaching a brilliant but generic student to excel in a specific subject. You need the right study materials, but surprisingly, the *order* in which you present those materials matters more than you might think. New research reveals that simply changing the sequence of training data can lead to uneven learning and impact the LLM's final performance. Think of it as giving the student the same textbook chapters but shuffling them around—different orders can lead to different levels of understanding. This "training imbalance" is a hidden challenge in LLM fine-tuning, and researchers have discovered a clever solution: merging models trained on different data sequences. Imagine taking the notes of several students who studied the chapters in different orders and combining the best parts of each. This "parameter merging" technique creates a more balanced and robust understanding, effectively mitigating the training imbalance. Moreover, a novel approach called "parameter-selection merging" goes beyond simply averaging the models. Instead, it cherry-picks the best parameters from each model for each specific aspect of the task, resulting in even greater performance gains. This research reveals a crucial insight into the nuances of LLM training and provides a powerful new technique for optimizing performance. By addressing the training imbalance, parameter merging paves the way for more efficient and effective LLM fine-tuning, unlocking the full potential of these powerful models. While the current research has primarily focused on 7b models, initial tests suggest that larger models might benefit even more from this approach. Further research into larger models and multi-task scenarios holds immense promise for future advancements in LLM training. This innovative method offers a significant step forward in maximizing the power of LLMs.