Bipedal balance is challenging due to its multiphase, hybrid nature and high-dimensional state space. Traditional balance control approaches for bipedal robots rely on low-dimensional models for locomotion planning and reactive control, constraining the full robot to behave like these simplified models. This involves tracking preset reference paths for several task space variables such as center of mass obtained through receding horizon model predictive control on low-dimensional models using task space controllers, often resulting in inefficient walking patterns with bent knees. However, we observe that bipedal balance is inherently low-dimensional and can be effectively described with simple state and action descriptors in a low dimensional state space. This allows the robot’s motion to evolve freely in its high-dimensional state space, only constraining its projection in the low-dimensional state space. In this work, we propose a novel control approach that avoids prescribing a low-dimensional model to the full model. Instead, our control framework uses a descriptive model with the minimum degrees of freedom necessary to maintain balance, allowing the remaining degrees of freedom to evolve freely in the high-dimensional space. This results in an efficient human-like walking gait and improved robustness.