The One-Step Trap: Why Simple AI Models Fail at Long-Term Prediction

The One-Step Trap (In AI Research)

I argue that relying on one-step predictions to model the world is a dangerous mistake in AI research. While appealing, these models fail because small errors compound rapidly over time, and calculating long-term outcomes becomes computationally impossible. Instead of iterating simple steps, we must build temporally abstract models using options and GVFs to truly understand complex futures.

"The bottom line is that one-step models of the world are hopeless, yet extremely appealing, and are widely used in POMDPs, Bayesian analyses, control theory, and in compression theories of AI."