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Anchoring What Matters: A Dual-Level Learning Framework for Visually-Grounded Multimodal Reasoning
Reinforcement learning with verifiable rewards (RLVR) has significantly improved the reasoning capabilities of large vision-language models (LVLMs). However, standard on-policy RLVR algorithms face a critical optimization bottleneck in preserving and reinforcing visually grounded reasoning behaviors
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