World Models and the Limits of Simulated Experience

Published September 2026

World models interest me because they could change how AI systems learn from experience. Rather than discovering every consequence by acting, a system could use a learned representation of its environment to compare possible actions before taking them. In the approach Yann LeCun has proposed, this connects with planning at different levels of abstraction (i.e., relating a longer-term goal to the shorter-term steps needed to pursue it), which remains a research direction, but not necessarily an assertion that machines already think like we do as people. [1]

I’m also coming at this from my perspective of working on open-weight model assessments where a recurring question is what organizations can reasonably infer about a system they did not train. With world models, I’m interested in how that question changes when the model also helps determine which actions  are worth considering.

It’s important to keep in mind that a world model and a planning process are different things. The model can predict what might happen after an action, while the planner uses those predictions to choose actions in relation to a goal. For example, for V-JEPA 2 planning uses predictions in a learned representation space rather than requiring a rendered video of each of several possible futures. Even though researchers demonstrate robot manipulation using supplied goals (and that is very much a meaningful result) it isn’t necessarily evidence of unrestricted human-like planning. [2]

This distinction is important because an accurate prediction doesn’t mean that a goal is appropriate. That is, a system might correctly anticipate the consequences of an action while pursuing an objective that the people impacted by it would reject. Conversely, a reasonable objective won’t compensate for a model that misrepresents what will happen; therefore, understanding the prediction, the goal, and the action-selection process have related but separate questions.

Researchers have already demonstrated that agents can exploit imperfections in learned environments. For example, Ha and Schmidhuber's World Models work describes agents finding behavior that succeeds inside the learned simulation but does not transfer to the original game environment. The problem wasn’t  that the simulation was imperfect, but rather that it was that the agent learned to exploit those imperfections. [3]

That finding makes me pause when I think about treating the amount of simulated experience as a measure of how much has been learned about the world since repeating an experiment inside the same model can help explore what that model predicts, but it does not necessarily (at least not by itself) provide an independent test of the assumptions producing those predictions; moreover, additional iterations might reveal a limitation or they may make a strategy that exploits it easier to discover.

The implication here isn’t  that simulations aren’t helpful. They can make experimentation possible where physical trials would be expensive or dangerous; however, the usefulness of that experience depends on its relationship to the setting where the resulting behavior will be used, including whether observations from outside the model can correct what it has learned.

World models are not inherently open-weight, and the ability to use a model through an API is different from having permission and practical means to change it. Where weights and suitable modification rights are available, users may be able to investigate or adapt behavior without waiting for the original developer. That is a possibility worth examining, but doesn’t ensure that the model becomes understandable or repairable.

For world models, the relevant mitigations would likely involve its training data, its learned representations, the planner, and/or the objective being pursued. Since these are not interchangeable and changing the objective won’t necessarily correct a mistaken prediction; moreover, improving prediction accuracy won’t settle conflicts about whose interests the objective should reflect; therefore, any mitigation would also need to be evaluated for the new failure modes it could introduce.

Access to weights alone would not resolve these questions. An organization could possess the model but lack the data, expertise, compute, or relevant observations needed to diagnose a problem; however, my argument for openness is more about the ability to investigate and revise a system alongside the resources and validation that make the right to do so useful. That is, it is not an argument that a released model has to become more self-explanatory.

All useful representations leave things out, and it would be unreasonable to ask a world model to reproduce everything about reality. The more consequential question is whether the representation preserves what matters for the use case being proposed, and who gets to determine what matters.

Consider a hypothetical warehouse application. A model might predict travel times accurately enough for a planner to identify efficient routes, while also representing interactions with workers poorly. Separately, the planner's objective might reward throughput without accounting for disruption to their work. One issue concerns the representation of the environment while the other concerns what the organization chooses to optimize for. Treating both as a single question of model performance would obscure the different responses they require.

If world models become part of institutional planning, those distinctions become questions of accountability just as much as they are questions about engineering. Who can challenge an assumption that makes a proposed course of action look attractive? What happens when people affected by a plan identify something the model does not represent? The answers don’t all need to be implemented inside the model, but they do need to exist somewhere in the process through which it is used.

The commercial opportunity is also connected to this. A simulator that can be examined and adapted for a particular application may be more useful than one whose impressive general demonstration leaves its practical limits unclear. For institutions, the value is not only the ability to explore more possibilities but also the ability to understand why some possibilities were favored while others were deprioritized.

The lesson I would carry over from my assessment of open-weight is to evaluate a model for a specific use rather than make a broad judgment about its capabilities. A system used to illustrate a scenario has a different calculus of responsibility from one used to train an agent that will operate around people. The questions concern both what the model predicts and what happens when a particular planner or user relies on those predictions.

I’d want to know whether improvements seen in the simulation also hold up in the real world, and whether failures are actually used to improve the system. I’d also keep that technical question separate from whether the system’s goals (as well as the way it distributes benefits and risks) are acceptable to the people affected. Both questions matter, and answering one does not answer the other. World models could expand our ability to learn and experiment, which is a substantial reason to take them seriously. My interest is in how that capability develops alongside the ability to question and correct the representations on which it depends, so that simulated experiences improve our understanding of the world rather than simply strengthening our confidence in a particular version of it.

References

[1] Yann LeCun. A Path Towards Autonomous Machine Intelligence. Version 0.9.2, June 27, 2022. Abstract and Introduction: a proposed architecture for predictive world models and hierarchical planning. https://openreview.net/pdf?id=BZ5a1r-kVsf

[2] Assran et al. V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning. 2025. Introduction; Section 3.2, Inferring Actions by Planning; Section 4, Planning: Zero-shot Robot Control. https://arxiv.org/html/2506.09985v1

[3] David Ha and Jurgen Schmidhuber. World Models. March 27, 2018. Interactive paper, especially Cheating the World Model and Discussion. The cited transfer example concerns a game environment, not a field deployment. https://worldmodels.github.io/