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MaPP: A Unified Marginalized Posterior-Predictive Framework for Data-Efficient RLVR
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but incurs substantial costs from rollouts and policy updates. Online prompt selection improves efficiency by using per-prompt Bayesian posteriors to predict difficulty and prioritize i
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