PROWL: Prioritized Regret-Driven Optimization for World Model Learning 事件

PROWL: Prioritized Regret-Driven Optimization for World Model Learning arXiv:2605.18803v2 Announce Type: replace-cross Abstract: Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream planning and policy performance. Because passive demonstration data systematically under-samples these high-impact regimes, improving robustness requires actively eliciting model failures rat