CROF, led by Reward Observability Fraction, offline-selects RSSM checkpoints whose closed-loop MPC and imagination A2C returns stay high after validation loss has collapsed on LunarLander.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2representative citing papers
TRM trains a small horizon-matched pairwise head on trajectory data to improve terminal-state ranking in latent MPC, raising success from 7% to 97% on TwoRoom and 32.7% to 84% on PLDM without changing the encoder or dynamics.
citing papers explorer
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Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander
CROF, led by Reward Observability Fraction, offline-selects RSSM checkpoints whose closed-loop MPC and imagination A2C returns stay high after validation loss has collapsed on LunarLander.
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Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics
TRM trains a small horizon-matched pairwise head on trajectory data to improve terminal-state ranking in latent MPC, raising success from 7% to 97% on TwoRoom and 32.7% to 84% on PLDM without changing the encoder or dynamics.