Path-like collective variables (CVs) can be very effective for accurately modeling complex biomolecular processes in molecular dynamics simulations. Recently, we have introduced DeepLNE (deep-locally non-linear-embedding), a machine learning-based path-like CV that provides a progression variable s along the path as a non-linear combination of several descriptors. We have demonstrated the effectiveness of DeepLNE by showing that for simple models such as the Müller-Brown potential and alanine dipeptide, the progression along the path variable closely approximates the ideal reaction coordinate. However, DeepLNE is computationally expensive for realistic systems needing many descriptors and limited in its ability to handle multi-state reactions. Here, we present DeepLNE++, which uses a knowledge distillation approach to significantly accelerate the evaluation of DeepLNE, making it feasible to compute free energy landscapes for large and complex biomolecular systems. In addition, DeepLNE++ encodes system-specific knowledge within a supervised multitasking framework, enhancing its versatility and effectiveness.