GRASP: Gradient-Based Planning for World Models at Longer Horizons

GRASP is a new gradient-based planner for learned world models that makes long-horizon planning practical by lifting trajectories into virtual states for parallel-in-time optimization, adding stochasticity to state iterates for exploration, and reshaping gradients so actions receive clean signals while avoiding brittle state-input gradients through high-dimensional vision models. The work addresses ill-conditioned computation graphs, non-greedy local minima, and high-dimensional latent failure modes that make long-horizon planning fragile. It was done with Mike Rabbat, Aditi Krishnapriyan, Yann LeCun, and Amir Bar.