MIT, LeCun et al. Close Train-Test Gap in AI Planning World Models

Researchers including Yann LeCun from MIT have developed a novel method to close the train-test gap in AI world models for gradient-based planning, enabling better generalization without additional test-time training. The work, published on arXiv, addresses a critical limitation in model-based reinforcement learning. A new method closes the train-test gap in world models for gradient-based planning, allowing models trained offline to generalize to new tasks at inference time. The approach trains world models on large datasets of expert trajectories. Unlike previous methods, it achieves improved generalization without requiring any additional training or computation during the planning phase, which is crucial for real-time applications. This advancement has significant implications for robotics, autonomous systems, and any domain requiring complex decision-making and planning under uncertainty, improving the reliability of AI agents in novel scenarios. The abstract does not detail specific quantitative benchmarks or limitations, focusing on the conceptual breakthrough of closing the train-test gap.

References

• Closing the Train-Test Gap in World Models for Gradient-Based Planning. Arjun Parthasarathy, Nimit Kalra, Rohun Agrawal, Yann LeCun, Oumayma Bounou, Pavel Izmailov, Micah Goldblum. arXiv:2512.09929v1. https://arxiv.org/abs/2512.09929v1