The One-Step Trap (In AI Research)
The author critiques the 'one-step trap' in AI research, where developers rely on iterating single-step predictions to model long-term outcomes. This approach is argued to be computationally infeasible and prone to compounding errors, suggesting temporally abstract models as a better alternative.
Why it matters
This technical critique challenges common methodologies in reinforcement learning and AI agent design, potentially influencing future research directions in predictive modeling.
The one-step trap is the common mistake of thinking that all or
most of an AI agent�s learned predictions can be one-step ones,
with all longer-term predictions generated as needed by iterating
the one-step predictions. The most important place where the trap
arises is when the one-step predictions constitute a model of the
world and of how it evolves over time. It is appealing to think
that one can learn just a one-step transition model and then �roll
it out� to predict all the longer-term consequences of a way of
behaving. The one-step model is thought of as being analogous to
The appeal of this mistake is that it contains a grain of truth:
if all one-step predictions can be made with perfect accuracy,
then they can be used to make all longer-term prediction with
perfect accuracy. However, if the one-step predictions are not
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