An arXiv paper tackles the accumulation of errors in world models that make step-by-step predictions, by allowing the length of each prediction step to adapt.
Latent world models learn to represent the state of an environment in the form of compact vectors, on the basis of which an agent can plan without having to refer back to the raw observations. Their limitation is structural: most predict one step at a time and must be run recursively to cover a long time horizon, meaning that even the slightest error propagates and amplifies with each iteration.
The paper submitted to arXiv takes this observation as its starting point to challenge the very concept of prediction. Rather than imposing a fixed step size, it allows the length of each leap to vary: the model can cover a predictable portion of the trajectory in a single step and refine its granularity where the environment becomes uncertain. The number of steps required to reach the same horizon is reduced accordingly, and with it the accumulation of errors.
The issue extends beyond robotics and control. Agents based on language models encounter exactly the same bottleneck in long-horizon tasks, where each autoregressive step re-evaluates the entire prior context. Planning carried out in a latent space, with jumps of an appropriate size, is one of the avenues being explored to overcome this increasing cost.
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