It Takes Two: Your GRPO Is Secretly DPO
Authors: name: Yihong Wu, name: Liheng Ma, name: Lei Ding, name: Muzhi Li, name: Xinyu Wang, name: Kejia Chen, name: Zhan Su, name: Zhanguang Zhang, name: Chenyang Huang, name: Yingxue Zhang, name: Mark Coates, name: Jian-Yun Nie
Organization: Mila – Quebec Artificial Intelligence Institute

Group Relative Policy Optimization (GRPO) is a prominent reinforcement
learning algorithm for post-training Large Language Models (LLMs). It is
commonly believed that GRPO necessitates a large group size to ensure stable
training via precise statistical estimation, which incurs substantial
computational overhead. In this work, we challenge this assumption by reframing
GRPO as a form of contrastive learning, which reveals a fundamental connection
to Direct Preference Optimization (DPO). Motivated by DPO's empirical success,
we investigate the minimal two-rollout case (2-GRPO), a configuration
previously deemed infeasible. We provide a rigorous theoretical analysis to
validate 2-GRPO and demonstrate empirically that it achieves performance on par
with 16-GRPO, despite using only 1/8 of the rollouts and reducing training time
by over 70%.