This paper presents a benchmarking framework for evaluating and comparing the performance of standing and walking (SaW) controllers for humanoid robots. The framework provides quantitative metrics for assessing command following, disturbance rejection, and energy efficiency. The authors also revisit reward function design to develop a minimally-constraining reward function that allows for more flexible learning of disturbance rejection and command following. The proposed benchmarking method enables systematic evaluation of SaW controllers, which is essential for understanding the trade-offs between different approaches and improving controller performance. The authors compare their new controller with state-of-the-art controllers on the Digit humanoid robot and find that their controller outperforms others in disturbance rejection and command following. The results highlight the importance of systematic evaluation in advancing SaW control learning. The proposed benchmarks and reward functions serve as a starting point for further research and development in this area.This paper presents a benchmarking framework for evaluating and comparing the performance of standing and walking (SaW) controllers for humanoid robots. The framework provides quantitative metrics for assessing command following, disturbance rejection, and energy efficiency. The authors also revisit reward function design to develop a minimally-constraining reward function that allows for more flexible learning of disturbance rejection and command following. The proposed benchmarking method enables systematic evaluation of SaW controllers, which is essential for understanding the trade-offs between different approaches and improving controller performance. The authors compare their new controller with state-of-the-art controllers on the Digit humanoid robot and find that their controller outperforms others in disturbance rejection and command following. The results highlight the importance of systematic evaluation in advancing SaW control learning. The proposed benchmarks and reward functions serve as a starting point for further research and development in this area.