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This paper proposes an exploration-guided prompt scaffolding framework that dynamically adjusts training prompts for multimodal reinforcement learning, achieving up to 9.7% relative improvement in performance on benchmarks.
Eval-Skill is an exploration-guided method that synthesizes reusable evaluation skills for reward modeling, achieving significant gains on RewardBench 2 over existing backbones.