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When KL Regularization Misfires in Group Policy Optimization

Ding, Fei
arXiv (All) · Papers
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68t0568t50computation-and-languagei-2-6i-2-7machine-learning
Why does removing reference-policy KL regularization sometimes improve group policy optimization? This motivates studying how reference-policy information should enter group-relative updates. We analyze seven potential failure modes in the interactions between KL and rewards: residual KL updates aft
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