GRASP: A Gradient-Based Planner for Long-Horizon World Model Planning

Berkeley AI Research introduces GRASP, a gradient-based planner for learned world models that aims to make long-horizon planning more robust.

GRASP lifts trajectories into virtual states for parallel optimization across time, adds stochasticity to state iterates for exploration, and reshapes gradients to avoid brittle state-input gradients through high-dimensional vision models.

The post identifies ill-conditioned gradients and non-greedy loss landscapes as core failure modes of standard rollout-based planning.