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

**[Machine Learning News](https://daily.dev/sources/mlnews)** · 3 min read · 1 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.marktechpost.com/2025/02/04/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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Tags: [#ai](https://daily.dev/tags/ai), [#machine-learning](https://daily.dev/tags/machine-learning), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#vlm](https://daily.dev/tags/vlm)

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