Imagine teaching a brilliant student a new skill, but they only grasp the format, not the underlying concept. That's the current hurdle with Large Language Models (LLMs). While instruction tuning helps LLMs follow instructions, they often just learn the format, not the real meaning. Researchers have discovered that this happens because the training data creates 'biased features,' which are like shortcuts that don't truly capture the task's essence. Enter DELIA, a novel method that tackles this problem by flooding the LLM with diverse examples. This creates a 'buffering effect' that neutralizes the biases and helps the model approximate the 'ideal features' – the true semantic understanding of the task. Think of it as showing the student countless examples from different angles, helping them grasp the core concept rather than just memorizing the steps. Experiments using DELIA show impressive improvements. In one test, it boosted performance on a text formatting task by a remarkable 36%. In another, it significantly improved translation quality, surpassing existing methods. This research suggests a powerful new approach to instruction tuning. By leveraging the buffering effect of diverse data, we can unlock the true potential of LLMs and empower them to adapt to new tasks with genuine understanding. While challenges remain, DELIA represents a leap towards a future where AI can truly learn and adapt.