The Functionally Invariant Paths (FIP) method adjusts the neural network's weight space by modeling it as a curved Riemannian manifold. This ensures that while the network learns new tasks, it remains functionally close to its original configuration, effectively retaining its performance on previous tasks despite substantial changes in parameters.