A proposed method called exploration-guided prompt scaffolding aims to fix the equal rollout budget that standard online reinforcement learning gives to training prompts, which vary widely in how informative they are for the current policy. The approach targets multimodal reinforcement post-training, where some prompts are already saturated and others are too difficult to yield reliable learning signals.
Training prompts in online reinforcement learning (RL) differ substantially in how informative they are for the current policy: some are already saturated while others are too difficult to yield reliable learning signals, yet both receive equal rollout budget under standard training. We propose an e
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