Large Vision-Language Models (LVLMs) are revolutionizing how AI interacts with the world, enabling machines to not only "see" images but also generate human-like text descriptions about them. However, these models sometimes "hallucinate," creating descriptions that are grammatically correct but factually wrong, like describing objects that aren't actually in the image. This happens because LVLMs sometimes prioritize their internal text knowledge over the visual information they receive. Researchers have been tackling this "hallucination" problem using various techniques, often relying on expensive human feedback or additional AI models to correct the LVLMs. A new research paper introduces a clever solution called "Calibrated Self-Rewarding" (CSR). Instead of relying on external feedback, CSR allows the LVLM to learn from its own mistakes. It works by generating multiple possible descriptions for an image, then using a combination of its internal language model and a separate image-relevance score to judge which description is best. This score helps the model prioritize visual information and learn to "see and reason" more accurately. The results are impressive. CSR significantly improves the performance of LVLMs across various benchmarks, reducing hallucinations by up to 7.62% compared to existing methods. The key innovation is the calibrated reward system, which balances the model's language fluency with its ability to accurately describe the image. This approach not only improves performance but also makes the learning process more efficient. CSR is also compatible with different types of LVLMs, suggesting it could become a standard technique for improving these models. While CSR represents a significant step forward, challenges remain. Further research is needed to explore the scalability of CSR to even larger models and to fully eliminate hallucinations. However, this self-learning approach opens exciting possibilities for developing more robust and reliable AI systems that can truly understand and interact with the visual world.