Imagine teaching AI to understand images not through laborious labeling, but by letting it learn from itself. That's the revolutionary idea behind a new technique called Self-Training on Image Comprehension (STIC). Researchers have long grappled with the challenge of feeding AI enough high-quality, labeled image data to truly grasp visual content. STIC flips the script by allowing large vision language models (LVLMs) to generate their own training data, focusing on describing images in detail. This two-stage process first involves the model creating its own preferred and dispreferred image descriptions, essentially teaching itself what's a good and bad caption. The second stage refines the model's reasoning abilities by incorporating these self-generated descriptions into existing instruction-tuning data. The results are impressive: STIC boosts performance across seven different image comprehension benchmarks by an average of 4%, all while using 70% less labeled data. This breakthrough has significant implications for the future of AI. Imagine medical diagnoses aided by AI that accurately interprets medical scans, or educational tools that personalize learning based on visual cues. STIC opens doors to a world where AI's visual understanding is not limited by the availability of labeled data, paving the way for more efficient, scalable, and impactful applications. However, challenges remain. While STIC excels in many areas, it still struggles with complex visual reasoning tasks like those found in advanced mathematics. Future research will focus on expanding the types of images used for self-training and developing more sophisticated methods for generating preference data. Despite these challenges, STIC represents a significant leap forward in AI's ability to learn from the world around it, promising a future where machines see and understand with greater clarity than ever before.