Preprint
Aug 2026
Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning
Reinforcement learning (RL) has become a central paradigm for large language model (LLM) post-training, but optimization toward new objectives can degrade capabilities already present in the base model. KL regularization is widely used to mitigate such forgetting by constraining policy drift toward a reference model. H...
Li Wang, Xiaodong Lu, Xiaohan Wang et al.
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