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To effectively learn from such structured exploration, we further propose a unified objective, which decomposes the reward sig","title":"Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR","url":"https://arxiv.org/abs/2605.15726","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.15726v1 Announce Type: new \nAbstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language models. However, its effectiveness is fundamentally limited by exploration: the policy can only improve on trajectories it has already sampled. While increasing the number of rollouts alleviates this issue, such brute-force scaling is computationally expensive, and existing approaches that modify the optimization objective provide limited control over what is explored. In this work, we propose NudgeRL, a framework for structured and diversity-driven exploration in RLVR. 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