Deep Agent Released R1-V: Reinforcing Super Generalization in Vision-Language Models with Cost-Effective Reinforcement Learning to Outperform Larger Models

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Deep Agent's R1-V approach enhances the generalization ability of vision-language models (VLMs) using cost-effective reinforcement learning with verifiable rewards (RLVR), outperforming larger models in out-of-distribution (OOD) tests. Trained on a small model with only 2 billion parameters, R1-V shows that effective training methodology can lead to superior performance without extensive computational resources. The model achieved robust visual counting abilities and was trained on CLEVR-70k and R1-Distilled Visual Reasoning datasets. R1-V emphasizes efficient training methods and makes its code, model weights, datasets, and training scripts publicly available to support open-source AI research.