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#machine learning Preprint Sep 2026

RL Starts before RL: On Policy Distillation for Better Reinforcement Learning

Reinforcement learning (RL) improves reasoning, but its performance depends on the policy from which training begins. We study on-policy distillation (OPD) as a preparation stage for RL and ask whether its benefits extend beyond improvements in the distilled model's initial accuracy. Under shared RL settings, students...

Shuai Dong, Yong-Fu Zhu, Yu-Qi Xu et al. · 0 citations
Preprint Sep 2026

SafeRI: Recognition and Intervention for Token-Level Safety Intervention in Large Vision Language Models

Existing safety alignment methods for vision-language models usually modify the model behavior globally: once the safety parameters are trained or loaded, they participate in both unsafe and already-safe generations. This always-on intervention can unnecessarily perturb the model's original reasoning path and degrade g...

Cao-Yuan Ma, Tian Gu, Wen-Pu Liu et al. · 0 citations

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