The official repository for the paper "Generate, but Verify" presents the REVERSE model, aimed at reducing hallucinations in vision-language models through retrospective resampling. It provides installation instructions, model checkpoints, and evaluation guidelines, along with acknowledgments to foundational resources from LLaVA and Qwen series.

Vision-Zero is a novel framework that enhances vision-language models (VLMs) through competitive visual games without requiring human-labeled data. It achieves state-of-the-art performance in various reasoning tasks, demonstrating that self-play can effectively improve model capabilities while significantly reducing training costs. The framework supports diverse datasets, including synthetic, chart-based, and real-world images, showcasing its versatility and effectiveness in fine-grained visual reasoning tasks.

The article provides an overview of a codebase for training language and vision-language models using PyTorch, highlighting installation instructions, model inference, and training setup. It details the required dependencies, configuration paths, and methods for integrating new datasets and models, while also addressing the usage of various GPU resources for efficient training and evaluation.