Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
arXiv:2610.40130v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching. While prior methods have sought to address this challenge through various approaches, a key challenge remains path mirage, where agents overcommit to spuriously successful trajectories in offline datasets induced by stochasticity, often leading to failure on difficult ta…