למה נוצרת הפרדה בין האימון לבין האימון המבוקר? דינמיקה של צפיפות עדכונים בבקבוקי אימון מוקדם

תקציר מקורי באנגליתarXiv:2610.01425v1 Announce Type: new Abstract: Train-validation separation is the evolving difference between performance on observed training examples and a finite held-out validation set. We propose a dynamic structural account of how this gap develops during adaptation of pretrained models: continued fitting can shift update demand from broadly reusable support toward narrower support with weaker held-out transfer. A conditional local model links this shift to increasing heterogeneity in gradient allocation and train-validation separation. Fixed training probes make this structural evolution observable without validation examples entering the readouts; held-out performance is used separately to evaluate its relation to the gap. In a constructed hierarchy implemented with a residual mul