On-policy distillation provides dense teacher supervision on a language model's own trajectories. In self-distillation with privileged context, this supervision comes from the model itself, conditioned on a hint or solution trace hidden from the student. When the teacher's preferences hinge on privileged information, it can assign higher probability to continuations the student cannot infer from its own context. Matching these preferences throughout training can induce predictive drift and degrade out-of-distribution (OOD) reasoning. We propose GeoSD, a self-distillation method that controls this drift through two complementary geometric terms. A Hellinger loss weights each teacher preference by the student--teacher overlap, reducing the influence of tokens to which the student assigns low probability. Because these influences can still accumulate, a Fisher--Rao penalty regulates predictive distance from a copy of the student refreshed periodically during training. Both terms compare next-token distributions in Fisher--Rao geometry and are jointly optimized with a preconditioner motivated by the natural gradient. Across three model families, GeoSD retains strong in-distribution gains while improving average mathematical OOD accuracy by 5.7--8.6 points over the base model. OOD gains hold across five model scales from 1.7B to 32B and transfer to code generation, where GeoSD improves code accuracy by 1.9 points on average despite distilling on mathematics alone. Our analysis of mathematical reasoning shows that standard matching rapidly concentrates probability mass at high-entropy states and that its samples confidently agree on incorrect answers. In contrast, GeoSD preserves alternative token mass and reduces false consensus.

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