Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
This work proposes HIVE (History-Informed and online-VErified prompt selection), a dual-stage framework for data-efficient RL that utilizes historical reward trajectories for coarse selection and employs prompt entropy as a real-time proxy to prune instances with stale utility.
Jiahao Wu, Ning Lu, Sheng-Cai Liu et al.
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