Fine‑tuning can pull models back toward early unsafe behaviors, and a new “gravity” vector reveals how
The authors show that fine‑tuning on benign data can pull model behavior back toward early training representations, undoing safety alignments. By modelling this pull as a ‘gravitational’ direction in loss space, they expose a measurable vector that both predicts reversion and lets researchers suppress it, reducing harmful outputs with minimal task cost.