P2R decouples perception from reasoning in VLMs via a two-stage process and PRA-GRPO alternating RL training, reporting gains such as 93.2% on V-Star for the 4B model over its Qwen3-VL backbone.
Grasp any region: Towards precise, contextual pixel understanding for multimodal llms.ArXiv, abs/2510.18876
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ClaimDiff-RL introduces reference-conditioned atomic claim differences verified by a multimodal judge as the reward signal for fine-grained RL in long-form image captioning.
Vision-OPD transfers an MLLM's privileged regional perception to its full-image policy through on-policy token-level self-distillation, yielding competitive results on fine-grained visual benchmarks.
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.
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