Anchoring the Drift: Gated-Anchor On-Policy Distillation for Faithful Radiology Report Generation

Small vision-language models are attractive for deploying clinical report generation on local hardware, yet they need supervision from a stronger model. On-policy distillation supplies dense token-level supervision on the student's own rollouts, and a growing family of methods improves it by reshaping the teacher Kullback-Leibler term. Every repair in that family acts on the teacher term itself, so what it can deliver stays bounded by what the teacher's own distribution contains, and wherever the teacher is unreliable the student inherits that failure; on hard studies a second failure compounds it, since the student drifts and leaves the teacher scoring an already-wrong prefix. We present GA-OPD (Gated-Anchor On-Policy Distillation), which answers both with one construction. A ground-truth anchor, a teacher-forced cross-entropy term on the reference report, never passes through the teacher and therefore stays valid where the teacher's own distribution is misaligned with the reference; a per-token drift gate routes it in by attenuating the on-policy term exactly where the student prefix has diverged from the reference. The mixing rule is what carries the result. In the regime where the teacher fails, the same anchor entering at a constant weight reaches 7.22 RadGraph and the drift-routed anchor reaches 19.36, against 7.15 for plain on-policy distillation; GA-OPD exceeds three state-of-the-art variants of this family there by 1.92 to 12.12 points, and two of them collapse together with plain on-policy distillation. Stratifying by the teacher's own per-sample faithfulness, an axis independent of the compared arms, the advantage grows with teacher failure. On the full three-corpus chest X-ray test set of 4,676 studies the same 4B student attains the highest CheXbert micro-F1 among all arms, 59.34, matching a frozen 30B medical teacher while serving on a single accelerator.