CEF-OPD: Learning to Correct Consequential Errors in Radiology Report Generation

Radiology report generators are typically trained on reference prefixes but deployed on their own predictions. An early clinical contradiction may influence subsequent claims, yet its local prediction error does not directly quantify the downstream clinical consequences. We introduce a clinical error frontier on-policy distillation (CEF-OPD) framework that allocates corrective supervision according to the estimated downstream consequences of model-generated errors. CEF-OPD identifies the earliest contradiction with extracted reference facts and constructs a reference-supported minimal correction. Paired continuations from the erroneous and corrected prefixes estimate the change in downstream clinical cost, excluding the directly edited claim. This estimate determines the weight assigned to local correction and distillation, emphasizing errors with greater estimated downstream impact. Reference clinical facts provide privileged guidance for self-distillation from the pre-error state, targeting the decision that introduces the contradiction. We characterize the paired cost difference as a model-internal cost-to-go advantage and bound local clinical risk differences through propagation-weighted distillation under stated assumptions. Experiments on MIMIC-CXR and IU X-ray datasets demonstrate the effectiveness of our proposed method.