Language models pick their own replay targets: merging two task banks lifts held-out retention by up to 4.42 points
Fine-tuning a language model on a new task can erode old abilities, and the prior data kept for protection covers only part of what the model knows. Researchers organize task descriptions and examples into Task Banks, let a frozen pre-update model generate its own replay, and test protection on four held-out tasks excluded from bank construction. Merging two partial banks raises mean held-out retention above either bank alone at all three tested sizes, by up to 4.42 percentage points; at 4B, selection informed by the incoming task beats random selection by 2.75 points, an advantage absent at smaller sizes.