# Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR

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Text version: https://stenobird.com/podcast/daily-paper-cast-7079649/nudging-beyond-the-comfort-zone-efficient-strategy-guided-exploration-for-rlvr.md
Podcast: [Daily Paper Cast](https://stenobird.com/podcast/daily-paper-cast-7079649)
Published: 2026-05-19T04:19:28+00:00
Episode link: https://share.transistor.fm/s/7f34b019
Audio file: https://media.transistor.fm/7f34b019/bfc03ac5.mp3
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Duration seconds: 1267

## Resource

🤗 Upvotes: 28 | cs.AI, cs.CL Authors: Chanuk Lee, Sangwoo Park, Minki Kang, Sung Ju Hwang Title: Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR Arxiv: http://arxiv.org/abs/2605.15726v1 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. 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. Our approach introduces Strategy Nudging, which conditions each rollout on lightweight, strategy-level contexts to induce diverse reasoning trajectories without relying on expensive oracle supervision. To effectively learn from such structured exploration, we further propose a unified objective, which decomposes the reward signal into inter- and intra-context components and incorporates a distillation objective to transfer discovered behaviors back to the base policy. Empirically, NudgeRL outperforms standard GRPO with up to 8 times larger rollout budgets, while outperforming oracle-guided RL baseline on average across five challenging math benchmarks. These results demonstrate that structured, context-driven exploration can serve as an efficient and scalable alternative to both brute-force rollout scaling and feasibility-oriented methods based on privileged information. Our code is available at https://github.com/tally0818/NudgeRL.

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