dailyai.report

GRASP lifts trajectories into virtual states to parallelize optimization across time. This gradient-based planner solves the instability common in long-horizon dynamics by adding stochasticity directly to the process. The approach allows world models to plan more reliably over extended periods. Practitioners can now optimize complex sequences without the typical gradient collapse.