A Gravitational Interpretation of Fine-Tuning Reversion

Fine-tuning on harmless data can partially undo behaviors acquired earlier in train-
ing. Safety can erode under benign post-alignment updates, unlearned capabilities
can re-emerge, latent traits can transfer through apparently unrelated supervision,
and related post-alignment fragility appears in other generative settings. We argue
these phenomena are usefully viewed through a common training-history lens.
Our hypothesis is geometric: large early training phases create dominant behav-
ioral manifolds, while later alignment or specialization phases are shallower dis-
placements from them. Subsequent fine-tuning can therefore inherit a persistent
reversion component pointing back toward a witness of the dominant manifold.
We call this the gravitational interpretation of fine-tuning reversion. Across our
main settings, representational drift rapidly acquires a component along a history-
defined reversion direction (vrev). In our main track, alignment with vrev rises
from cos = 0.429 ±0.052 after the first update to 0.647 ±0.021 by step 20.
Across 24 run-step pairs, every observed alignment exceeds the p99 of an isotropic
activation-space null. We demonstrate that selectively blocking motion along vrev
changes the final alignment at T = 100 from 0.648 ±0.009 to−0.211 ±0.021
and reduces harmfulness from 19.0% ±4.0% to 8.5% ±1.5% with little task cost.
These results support vrev as a causally relevant mediator of early post-alignment
reversion in our setup. Importantly, we do not claim that vrev is the unique safety
direction, nor that the dominant manifold is directly observed; rather, we iden-
tify a robust, history-defined direction that explains and partially controls early
reversion dynamics.