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README.md

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 license: apache-2.0
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-PyVision-Video-7B-RL
+PyVision-RL: Forging Open Agentic Vision Models via RL
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+Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior. We introduce \model-RL, a reinforcement learning framework for open-weight multimodal models that stabilizes training and sustains interaction. Our approach combines an oversampling–filtering–ranking rollout strategy with an accumulative tool reward to prevent collapse and encourage multi-turn tool use. Using a unified training pipeline, we develop \model-Image and \model-Video for image and video understanding. For video reasoning, \model-Video employs on-demand context construction, selectively sampling task-relevant frames during reasoning to significantly reduce visual token usage. Experiments show strong performance and improved efficiency, demonstrating that sustained interaction and on-demand visual processing are critical for scalable multimodal agents. Code, data and models are released at \url{https://github.com/agents-x-project/PyVision-RL}