Learning When to Abort in Transformer Planning: A Mechanistic Study

Planning does not by itself imply the ability to recognise errors, but recognising errors can improve planning capacity. In this paper, we study this phenomenon in Transformer planning. To analyze the inner mechanism, we follow the approach of Wang et al. (2024) and abstract planning as path-finding in graphs. In this setting, we train the model to detect the error of invalid transitions, i.e., after the model generates a next node that is not adjacent with the current node, the model should then generate a special abort token x. For expressiveness, we construct a Transformer with feed-forward width $O(|V|)$ for any fixed graph with node set $V$. It can detect all possible invalid transitions, and select a valid next node if the prefix is valid. We then propose a worst-case lower bound on feed-forward width, which makes the detector's linear width near-optimal up to logarithmic factors. In trained models, we demonstrate that invalid-transition detection increases previous-node attention, and most of the valid–invalid separation locates in the feed-forward network's output. This accords with our theoretical insights. Finally, we use this error detection to improve the model's performance: at inference, generating the abort token x will directly cause an external procedure to discard the attempt and restart from the original prompt. Experiments show that error detection combined with retry improves model's planning reliability on graphs, Countdown, and mazes.