Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

Abstract:Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks. While scaling rollouts can stabilize training and enhance performance, the computational overhead is a critical issue. In algorithms like GRPO, multiple rollouts per prompt incur prohibitive costs, as a large portion of prompts provide negligible gradients and are thus of low utility. To address this problem, we investigate how to select high-utility prompts before the rollout phase. Our experimental analysis reveals that sample utility is non-uniform and evolving: the strongest learning signals concentrate at the ``learning edge", the intersection of intermediate difficulty and high uncertainty, which shifts as training proceeds. Motivated by this, we propose HIVE (History-Informed and online-VErified prompt selection), a dual-stage framework for data-efficient RL. HIVE utilizes historical reward trajectories for coarse selection and employs prompt entropy as a real-time proxy to prune instances with stale utility. By evaluating HIVE across multiple math reasoning benchmarks and models, we show that HIVE yields significant rollout efficiency without compromising performance.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2603.25184 [cs.LG]

(or arXiv:2603.25184v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2603.25184 Focus to learn more arXiv-issued DOI via DataCite

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