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VLMs achieve superior reasoning performance when their thoughts are explicitly tied to visual evidence, outperforming larger models in some tasks.
OpenVLThinkerV2 leapfrogs existing open-source and proprietary multimodal models by using a novel Gaussian-based RL objective that ensures gradient equity across diverse visual tasks.
