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This work proposes an exploration-guided prompt scaffolding framework that adapts the training prompt distribution dynamically throughout RL post-training of multimodal large language models (MLLMs), and uses a lightweight rollout-based proxy for prompt utility derived from KL-regularized policy improvement theory.
