The field of control theory is moving towards more efficient and effective methods for controlling underactuated systems. Recent developments have focused on improving the performance of model predictive control approaches, particularly in terms of planning horizons and computational cost. Additionally, there is a growing interest in redefining and expanding the notion of virtual holonomic constraints to increase their practical applicability in motion planning. The tradeoff between control performance and computational effort is also being explored, with the goal of finding optimal balances between these two factors. Notable papers in this area include VIMPPI, which presents a novel control approach that substantially outperforms baseline methods, and Virtual Holonomic Constraints in Motion Planning, which challenges the conventional definition of virtual holonomic constraints and proposes a more inclusive approach. The Path Integral Bottleneck paper also provides a significant contribution by quantifying the control-compute tradeoff.