dailyai.report

The GRASP planner lifts trajectories into virtual states to parallelize optimization across time. This approach overcomes the computational bottlenecks of traditional gradient-based planning in learned dynamics. By integrating stochasticity directly into the process, it allows world models to handle longer horizons. Practitioners can now optimize complex sequences without linear time scaling.