Worked on the NVIDIA/NeMo-RL repository to enhance the robustness of reinforcement learning validation by shifting the evaluation metric from binary accuracy to the mean of rewards. This approach addressed the challenge of non-binary reward distributions, enabling more reliable and representative assessment of RL models. Using Python and leveraging machine learning and reinforcement learning expertise, the update reduced evaluation noise and improved the stability of model selection processes. The change facilitated faster iteration cycles and increased confidence in deployment readiness by ensuring that validation metrics accurately reflected performance across diverse reward scales, supporting more effective experimentation and development within the RL framework.