# Behavior Regularization

Hierarchical Joint-Embedding Predictive Architectures (H-JEPA) previously planned by running gradient descent on actions at test time, and the iterative solver consumed 96.6%–97.7% of episode runtime on long-horizon tasks. WARP-JEPA keeps the world models frozen and replaces online optimization with learned multi-candidate trajectory proposers, scoring candidates with the frozen predictor inside the generation loop. Across 30 frozen two-, three- and four-level models on four benchmarks, it speeds long-horizon episode inference by 9.6–18.8× (2.9–3.0× on Cube) and raises task success by up to 33.3 percentage points.

Hierarchical Joint-Embedding Predictive Architectures (H-JEPA) previously planned by running gradient descent on actions at test time, and the iterative solver consumed 96.6%–97.7% of episode runtime on long-horizon tasks. WARP-JEPA keeps the world models frozen and replaces online optimization with learned multi-candidate trajectory proposers, scoring candidates with the frozen predictor inside the generation loop. Across 30 frozen two-, three- and four-level models on four benchmarks, it speeds long-horizon episode inference by 9.6–18.8× (2.9–3.0× on Cube) and raises task success by up to 33.3 percentage points.