Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander

arXiv cs.LG4w4 min read

arXiv:2607.01736v1 Announce Type: new 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 c

Read the full story at arXiv cs.LG →

Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander

More in Machine Learning

Reducing Human Annotation with ML Active Learning

Learning What Matters: Supervising Sparse Attention Routing with Causal Evidence Sets

On the Depth Scalability of Logic Gate Networks

Pretraining EHR Foundation Models with Patient-Aware Sampling

From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

Parameter-free Adaptive Sparse Attention via Compression-Based Content Selection