Differentiable nonlinear MPC computes parametric NLP sensitivities via IFT and smoothed IPM conditions within SQP, achieving over 3x speedups versus prior solvers.
Temporal abstraction acts as a low-pass filter reducing effective successor representation rank to fix spectral mismatch in forward-backward representations and stabilize long-horizon continuous control learning.