Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR
Reinforcement learning with verifiable rewards (RLVR) is a scalable paradigm for improving the mathematical reasoning of large language models, but it is fundamentally limited by exploration: the policy can only improve on trajectories it has already sampled. Sampling more rollouts alleviates this at prohibitive compute cost, while objective-level modifications offer little control over what is explored. We propose NudgeRL, a framework for structured, diversity-driven exploration in RLVR whose core component, Strategy Nudging, conditions each rollout on a lightweight strategy-level context, without requiring the context generator to solve the problem itself. To learn from such exploration...
| Authors | Chanuk Lee, Sangwoo Park, Minki Kang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 May 2026 |
| Last updated at source | 3 October 2026 |
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