dailyai.report

GRASP optimizes trajectories by lifting them into virtual states to enable parallelization across time. This gradient-based planner solves the instability issues common in long-horizon dynamics. By adding stochasticity directly to the optimization process, it allows world models to plan more reliably over extended sequences. Practitioners can now scale planning horizons without linear compute increases.