Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander
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Computer Science > Machine Learning
arXiv:2607.01736 (cs)
[Submitted on 2 Jul 2026]
Title:Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander
Authors:Nikolai Smolyanskiy
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Abstract:We study how to predict the downstream closed-loop performance of a learned latent world model from validation-time diagnostics alone. Choosing the right checkpoint from a world-model training run is difficult: validation loss and multi-step prediction RMSE keep improving long after closed-loop performance has collapsed. We present a suite of structural validation-time diagnostics drawn from optimal-control theory and apply them to Gymnasium's LunarLander v3, which features shaped rewards. We train an RSSM [5, 4] world model on it and treat per checkpoint CEM-MPC return as the oracle for closed-loop quality. By evaluating 40 metrics against this oracle, we find that the strongest single predictor is the Reward Observability Fraction (ROF), which measures the reward predictor's dependence on the observable subspace. We combine ROF with three structural regularizers into a single-number offline checkpoint selection score, the Composite Reward Observability Fraction (CROF). The CROF-selected world model trains a model-based A2C policy that beats a fairly evaluated model-free A2C baseline by ~24.5 return points while using ~65x fewer real-environment interactions, and the same world model also drives a strong zero-shot CEM-MPC policy. Code and data: this https URL.
| Comments: | Preprint, 19 pages (16 main text + 3 pages appendix), 7 figures, 4 tables. Video: this https URL , Code: this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| ACM classes: | I.2.6; I.2.8; I.2.9 |
| Cite as: | arXiv:2607.01736 [cs.LG] |
| (or arXiv:2607.01736v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01736
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Nikolai Smolyanskiy [view email][v1] Thu, 2 Jul 2026 05:46:03 UTC (2,597 KB)
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