<ul><li>Deep Agent released R1-V, a reinforcement learning approach that enhances the generalization ability of vision-language models (VLMs) while being cost-effective.</li><li>The R1-V approach employs reinforcement learning techniques to teach VLMs to develop robust visual counting abilities, enhancing their performance in various AI applications.</li><li>Despite having only 2 billion parameters, R1-V outperforms a significantly larger model in out-of-distribution (OOD) tests, demonstrating the importance of the training methodology and reinforcement learning strategies.</li><li>R1-V's training efficiency and relatively low computational cost of $2.62 make it an attractive choice for researchers and developers seeking high performance without extensive computational resources.</li></ul>

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

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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