Gradient-based Planning for World Models at Longer Horizons

The post introduces GRASP (Gradient Relaxed Stochastic Planner), a gradient-based planner for learned world models that lifts trajectories into virtual states for parallel-in-time optimization, adds Gaussian noise to state iterates for exploration, and stops brittle state-input gradients through the model while retaining action gradients. It argues that long-horizon planning with modern world models is fragile due to ill-conditioned rollouts, non-greedy local minima, and adversarial-robustness-style sensitivity of state Jacobians, and reports Push-T results where GRASP outperforms CEM, GD, and LatCo at horizons from 40 to 80. The work is by Michael Psenka with Mike Rabbat, Aditi Krishnapriyan, Yann LeCun, and Amir Bar (arXiv 2602.00475).