How should an AI or robot decide what to do next?

In this episode, we explore a new approach to planning that rethinks how world models are trained. The episode is based on the paper "Closing the Train-Test Gap in World Models for Gradient-Based Planning" Many AI systems can predict the future accurately, yet struggle when asked to plan actions efficiently. We explain why this train–test mismatch hurts performance and how gradient-based planning offers a faster alternative to traditional trial-and-error or heavy optimization.

The key idea is simple but powerful: if you want a model to plan well, you must train it the way it will be used. By exposing world models to planning-style objectives during training, researchers dramatically reduce computation time while matching or exceeding previous methods.

This conversation breaks down what changed, why it works, and what it means for building faster, more practical planning-based AI systems.

Resources:

Paper : Closing the Train-Test Gap in World Models for Gradient-Based Planning https://www.arxiv.org/pdf/2512.09929 Need help building computer vision and AI solutions? https://bigvision.ai

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