arXiv — cs.AI preprintsInternational9 October 2026
Reward Observability and the Limits of Offline Checkpoint Selection in RSSM World Models
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arXiv:2607.01736v3 Announce Type: replace-cross Abstract: We study the closed-loop properties of a recurrent state-space model (RSSM) world model trained on human demonstrations in Gymnasium's LunarLander-v3. We use the trained world model for zero-shot CEM model-predictive control (MPC) and for actor-critic (A2C) training in imagination. Scored on 100 held-out episodes, the selected model-based A2C policy (trained on world-model checkpoint 280) reaches a mean return of +189.5, matching the best model-free A2C checkpoint (+183.7; 600- and 1000-step episode caps respectively) with ~65x fewer re
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