Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR
The article discusses a new framework called NudgeRL for improving exploration in reinforcement learning with verifiable rewards (RLVR). This framework aims to enhance reasoning capabilities in large language models by introducing structured and diversity-driven exploration. The results indicate that NudgeRL significantly outperforms traditional methods, demonstrating its potential as an efficient alternative for scaling exploration.
- ▪NudgeRL introduces Strategy Nudging to condition rollouts on lightweight, strategy-level contexts.
- ▪The framework allows for diverse reasoning trajectories without the need for expensive oracle supervision.
- ▪Empirical results show that NudgeRL can outperform standard GRPO with larger rollout budgets and oracle-guided RL baselines.
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Computer Science > Artificial Intelligence arXiv:2605.15726 (cs) [Submitted on 15 May 2026] Title:Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR Authors:Chanuk Lee, Sangwoo Park, Minki Kang, Sung Ju Hwang View a PDF of the paper titled Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR, by Chanuk Lee and 3 other authors View PDF HTML (experimental) Abstract: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.
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