| title | Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR |
|---|---|
| authors | Chanuk Lee, Sangwoo Park, Minki Kang, Sung Ju Hwang |
| date | 2026-05-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.15726 |
| affiliation | KAIST AI |
| upvotes | 20 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{lee2026nudging,
title={Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR},
author={Chanuk Lee and Sangwoo Park and Minki Kang and Sung Ju Hwang},
year={2026},
eprint={2605.15726},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.15726}
}