Tag
This paper presents the first application of calibration models for offline hyperparameter selection in a real-world industrial setting, using a municipal water treatment plant, and shows they can generate realistic rollouts and recover hyperparameter sensitivity trends.
The paper identifies 'temporal credit dilution' in learned dynamics models where global readouts focus on spurious correlates rather than brief physical events. It proposes CREST, a training-free method that re-anchors pooled representations using event core estimates, improving out-of-distribution robustness.
OpenAI introduces a method for learning complex nonlinear system dynamics using deep generative models over temporal segments, enabling stable long-horizon predictions and differentiable trajectory optimization for model-based control.