arXiv:2607. 15142v1 Announce Type: new Abstract: World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation.
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The paper investigates the discrepancy between strong agents and their underlying weak world models in Atari Pong by reproducing five visual world-model agents and evaluating their frozen models. Closed‑loop rollouts reveal visual and dynamical failures such as ball disappearance and incorrect motion, while zero‑shot model‑based RL policies trained entirely within the frozen models perform poorly compared to the original agents. To address these issues, the authors introduce Concept‑Guided Spatial Regularization (CGSReg), an auxiliary loss focused on task‑critical ball regions, which improves both pixel‑space zero‑shot MBRL performance and closed‑loop rollouts for several agents.
arXiv:2606. 28128v1 Announce Type: cross Abstract: Video generation models have emerged as a promising paradigm for embodied world simulation.
arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...
arXiv:2609.37907v1 Announce Type: new Abstract: Video games offer scalable environments for studying perception and control in embodied agents.Abundant online gameplay videos could supply demonstrati...
arXiv:2608.29904v1 Announce Type: new Abstract: Modern video generators routinely fail at physical dynamics: objects float, trajectories violate gravity, contacts vanish. Standard denoising and flow-...
arXiv:2602. 13977v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision--Language--Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots.