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arXiv · 2510.03679

Group Policy Gradient

Junhua Chen·Zixi Zhang·Hantao Zhong·Rika Antonova
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Abstract

We introduce Group Policy Gradient (GPG), a family of critic-free policy-gradient estimators for general MDPs. Inspired by the success of GRPO's approach in Reinforcement Learning from Human Feedback (RLHF), GPG replaces a learned value function with a group-based Monte Carlo advantage estimator, removing the memory, compute, and hyperparameter costs of training a critic while preserving PPO's clipped-objective structure. We prove the consistency of the GPG estimator, analyze the bias-variance tradeoffs, and demonstrate empirically that GPG matches or outperforms PPO on standard benchmarks. GPG makes better use of parallel simulations, which, together with its critic-free design, results in more efficient use of computational resources than PPO.

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Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova. 2025-10-04. Group Policy Gradient. https://arxiv.org/abs/2510.03679

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