The linear decomposition assumption in the Versatile Value Estimation (VVE) module may have limitations in scenarios where the relationship between state-action pairs and threshold conditions is nonlinear or complex. In such cases, a simple linear combination may not accurately capture the underlying dynamics of the system, leading to suboptimal value function estimations. To relax or generalize this assumption, more sophisticated function approximators, such as neural networks or kernel methods, could be employed to model the relationship between states, actions, and threshold conditions. By using more flexible models, the VVE module can better capture the intricate interactions between these variables, improving the accuracy and generalizability of the value function estimations.