Causes and mitigation of train–evaluation gap under high reset probabilities
Ascertain the underlying causes of the observed train–evaluation performance gap at high frontier reset probabilities in Frontier Checkpointing and investigate whether randomizing checkpoint states across procedurally generated layouts recovers the benefits of high reset without incurring overfitting.
References
While the train-eval gap at high reset probabilities shown in Figure~\ref{fig:eval-train-gap} suggests overfitting to checkpoint configurations, we cannot definitively rule out alternative explanations. Future work should investigate whether diversity interventions such as randomizing checkpoints across procedurally generated layouts can recover the benefits of high reset without the overfitting cost.
We did not investigate why the two outcomes differ for the sham specifically. The probe resets its random stream and evaluates 16 agents per island for 8 lifetimes in a fixed sequence of worlds, while the in-run number is the population's fitness in the worlds it was selected in; a condition whose advantage is partly specific to the worlds it evolved in would show the two diverging, but we have not tested that here.