When KL Regularization Misfires in Group Policy Optimization

Abstract: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 after reward clipping, after gradient cancellation, and in groups with identical rewards; KL growth with response length and an imbalance in its relative contribution; KL concentration on a small number of tokens; and sampling noise when k1 is incorporated into rewards. We propose Zero-Sum Calibrated Policy Optimization (ZCPO), which uses relative drift measured by conditional KL to calibrate within-group reward coefficients and integrates them into the base surrogate. Mathematical reasoning experiments and ablations support this design's effectiveness in our settings.

Machine Learning (cs.LG); Computation and Language (cs.CL)

arXiv:2610.12161 [cs.LG]

(or arXiv:2610.12161v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.12161 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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