On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse
arXiv:2606.29238v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a rigorous derivation of GRPO from first principles of the policy gradient theorem, revealing a fundamental credit assignment failure: under output-only reward, every token in a rollout receives identical advantage, collapsing token-level credit to a single scalar. We prove this induces gradient sparsity that intensifies over training, and demonstrate empirically via SVD analysis of GRPO gradients o
This research provides a rigorous theoretical analysis of a known reinforcement learning algorithm (GRPO), clarifying its underlying limitations and offering insights for future AI development.
Understanding the fundamental limitations and credit assignment failures of algorithms like GRPO is crucial for developing more robust and efficient AI agents, especially for complex real-world tasks.
This paper deepens the theoretical understanding of policy gradient methods and highlights areas for improvement in reinforcement learning algorithms that reduce the need for learned critics.
- · AI researchers
- · Reinforcement learning developers
- · Inefficient AI models
Improved understanding of GRPO's limitations for AI practitioners.
Development of new reinforcement learning algorithms that address credit assignment and gradient sparsity more effectively.
More robust and generalizable AI agents capable of handling complex, real-world credit assignment problems.
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Read at arXiv cs.LG