GRASP: gradient-based long-horizon planning for world models
Gradient-based Planning for World Models at Longer Horizons
What happened
A Berkeley AI Research team has proposed GRASP, a gradient-based planner that makes long-horizon planning with learned dynamics (world models) feasible. It lifts trajectories into virtual states for parallel optimization across time, injects stochasticity directly during iterative state refinement to explore, and reshapes gradients so actions receive clearer signals. The method addresses ill-conditioned computation graphs, local minima caused by non-greedy structure, and fragile gradients in high-dimensional visual models.
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