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This paper introduces LEEPS, a latent-guided explore-exploit prompt sampler for efficient reinforcement learning with verifiable rewards (RLVR) in LLMs. It adaptively balances reuse of informative prompts and exploration of uncertain ones, improving reasoning benchmark scores by 2.6-3.7% over baselines while adding only ~2 seconds of overhead per training step.