Density-Guided Bootstrap Boundaries for Latent World-Model Reinforcement Learning

arXiv:2610.41022v1 Announce Type: new Abstract: Latent world models allow reinforcement learning agents to learn policies from imagined trajectories, yet determining the optimal depth of model reliance remains a fundamental challenge. Fixed imagination horizons apply uniform prediction depths regardless of trajectory reliability, exposing target values to compounding model error or over-relying on critic approximations. We introduce density-guided bootstrap boundaries, a trajectory-dependent mechanism that adapts the depth of imagined returns using a score-informed relative density proxy. The proxy accumulates the estimated divergence of a score-corrected auxiliary field formed by a feature displacement network and a noise-conditioned score estimator along imagined paths.