Imagine training a powerful AI model that understands both images and text, not with mountains of data, but with a carefully curated selection. This is the idea behind Align$^2$LLaVA, a new technique for building Multimodal Large Language Models (MLLMs). These models, like the LLaVA series, are usually trained on massive datasets of instructions. However, generating these instructions automatically often leads to inconsistent quality. Align$^2$LLaVA tackles this by prioritizing quality over quantity through a two-step process: human preference alignment and LLM characteristic alignment. First, human experts evaluate and rank instructions based on their clarity, relevance to the image, and accuracy of the responses. This feedback trains a reward model that learns to identify high-quality instructions. Then, the researchers use the MLLM's internal language model to fine-tune the instructions, aligning them with the model's own 'writing style'. The results are impressive. Using Align$^2$LLaVA, the researchers compressed a massive 158,000-instruction dataset by a staggering 90%—and yet, the smaller, refined dataset actually *improved* the model’s performance across eight different benchmarks! This is a game-changer for creating more efficient MLLMs, opening doors to training powerful multimodal AIs without the need for endless data and making them more accessible to broader audiences. Future research could explore how to further reduce reliance on human input while maintaining high data quality. The focus now shifts to developing methods that can effectively utilize human feedback combined with automated refinement techniques.