In this paper, we present the EFRame framework to improve the performance of the Group Relative Policy Optimization (GRPO) algorithm, which suffers from limited exploration, low sample efficiency, and instability in complex inference tasks. EFRame systematically improves GRPO by introducing additional rollouts to explore high-quality trajectories, online filtering to remove low-quality samples that cause noise and variance, and empirical reproducibility to repeatedly utilize rare but informative samples. Through various inference benchmark experiments, we demonstrate that EFRame not only improves the robustness and efficiency of training, but also enables deeper inference capabilities that were not possible with conventional GRPO. Furthermore, EFRame enables more fine-grained classification of training samples, which allows for a deeper analysis of how different types of samples contribute to the reinforcement learning process.
