dailyai.report

GRASP lifts trajectories into virtual states to parallelize optimization across time. This gradient-based planner solves the computational bottleneck of long-horizon dynamics in world models. By adding stochasticity directly to transitions, it avoids local minima. Researchers can now optimize complex agent behaviors over longer timeframes without the typical exponential increase in compute costs.