A comprehensive experiment conducted by Labelbox demonstrates that combining rubric-based rewards with Group Relative Policy Optimization (GRPO) significantly improves agent performance in complex e-commerce tasks compared to traditional sparse reward methods. The study, which tested three training approaches—sparse rewards, rubric rewards, and GRPO with rubric rewards—found that the combined method achieved a 65% success rate and reduced training time by 60%, outperforming the other approaches. The results underscore the importance of providing intermediate feedback and optimizing exploration strategies for complex business applications, highlighting that business tasks often require optimizing multiple competing objectives. The experiment's findings suggest that organizations should consider these techniques for reinforcement learning applications, as they offer a practical and efficient approach to training agents for real-world business challenges, reinforcing the idea that existing methods, when validated in realistic settings, can effectively bridge the gap between academic theory and practical deployment.