Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR
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| Authors | Chanuk Lee et al. |
| Year | 2026 |
| HF Upvotes | 19 |
| arXiv | 2605.15726 |
| Download | |
| Code | https://github.com/tally0818/NudgeRL |
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.
Engineering Breakdown
Plain English
This paper addresses a core bottleneck in reinforcement learning for language models: exploration is expensive and inefficient. The authors propose NudgeRL, which uses lightweight 'strategy contexts' to guide each rollout toward diverse reasoning paths, allowing the model to explore more efficiently without massive increases in compute. The key finding is that conditioning rollouts on different reasoning strategies significantly improves sample efficiency compared to naive scaling or existing objective-modification approaches.
Key Engineering Insight
Instead of brute-force scaling (more rollouts) or complex objective tweaks, you can cheaply steer exploration by conditioning each sample on a lightweight strategy hint—think of it as 'try solving this problem differently this time' rather than hoping random variation finds diverse paths. This decouples exploration control from the training objective itself.
Why It Matters for Engineers
Language model reasoning (math, coding, complex tasks) requires exploring many solution paths, but compute for rollouts scales linearly with attempts. If your production system spends 70% of inference time on rollout generation for RL training, improving sample efficiency by even 30-40% directly cuts infrastructure costs. This is especially critical for fine-tuning reasoning models where each rollout is expensive.
Research Context
Prior work on RLVR (reinforcement learning with verifiable rewards) showed promise for scaling LLM reasoning but hit a wall: policies only improve on what they've sampled, forcing teams to either generate 10x more rollouts or modify loss functions—both costly or limited. NudgeRL advances this by introducing a simple, composable way to guide diversity at sampling time. This opens the door to practical efficiency gains in production reasoning systems without rearchitecting the training pipeline.
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