The GitHub repository "Deep-Agent/R1-V" focuses on improving Vision Language Models (VLM) using Reinforcement Learning with Verifiable Rewards (RLVR). The project aims to enhance algorithm efficiency, task diversity, and the impact of open-source research. It demonstrates that RLVR is more effective and robust than traditional supervised fine-tuning, especially in out-of-distribution scenarios. A smaller 2B model outperformed a larger 72B model after only 100 training steps, conducted on 8 A100 GPUs for 30 minutes at a cost of $2.62. The training used the CLEVR-70k dataset and a distilled visual reasoning dataset. Community contributions are encouraged, particularly for issues marked "help wanted." Recent updates include training code, evaluation scripts, and visual reasoning traces. The text provides a command for distributed training using `torchrun` with specific configurations, including training a model named Qwen2-VL-2B-Instruct on a dataset, with key parameters like batch size, gradient accumulation, and attention implementation. Instructions for handling OOM errors and an evaluation script for OOD performance are included. The project has 1.8k stars, 43 watchers, and 128 forks, with contributions from five individuals. The codebase uses Python, Jupyter Notebook, and Shell.