GRPO is Secretly a Process Reward Model

This paper establishs that Group Relative Policy Optimization (GRPO), while appearing to use only final outcome rewards, inherently functions as a Process Reward Model (PRM) through its implicit sub-trajectory credit assignment. By analyzing groups of trajectories that share identical prefixes, the authors prove that GRPO naturally computes step-level rewards using a Monte Carlo approach. However, this hidden structure reveals a flaw where imbalanced step frequencies can skew advantages, inadvertently suppressing high-reward paths and hindering efficient model training. To fix this, the researchers introduce $\lambda$-GRPO, a modified objective that scales token-level losses to neutralize these frequency imbalances. Empirical testing shows that $\lambda$-GRPO enables Large Language Models to achieve superior reasoning performance significantly faster than the standard algorithm. Ultimately, the work demonstrates that the built-in PRM structure of GRPO can be optimized to boost efficiency without the need for expensive, manual step-level annotations.