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

Jul 10, 2026

Berkeley AI Research has introduced GRASP, a gradient-based planner designed for learned world models to enable more robust long-horizon planning. GRASP addresses optimization fragility by lifting trajectories into virtual states, introducing stochasticity for exploration, and reshaping gradients to avoid brittle signals in high-dimensional vision models.

Why it matters: GRASP offers a practical solution to key optimization challenges in long-horizon planning as world models become more general-purpose.

Full story at: Berkeley AI Research ↗