AIBullisharXiv – CS AI · Mar 277/10
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Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model
Researchers propose HIVE, a new framework for training large language models more efficiently in reinforcement learning by selecting high-utility prompts before rollout. The method uses historical reward data and prompt entropy to identify the 'learning edge' where models learn most effectively, significantly reducing computational overhead without performance loss.