Author

Kun Wang

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Preprint Aug 2026

Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness

The Forget-Retain Alignment Gap is introduced, a training-free predictor that scores an update's forget-retain alignment without running a relearning attack, and separates selective from dense updates more reliably than global distance, suggesting that weight selectivity better explains robustness than distance alone.

Yi Chen, Hanna Hsieh, Shu-Hong Liu et al. · 0 citations

#artificial intelligence Preprint Mar 2026

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. · 12 citations