dailyai.report

The BAIR team introduced GRASP, a planner that lifts trajectories into virtual states to parallelize optimization across time. This approach solves the vanishing gradient problem common in long-horizon learned dynamics. By adding stochasticity directly to transitions, it prevents local minima. Practitioners can now optimize complex agent behaviors over longer timeframes without computational collapse.