External context enables language models to accomplish tasks which rely on information beyond what is captured during pretraining. A standard way to improve contextual faithfulness in these settings is to fine-tune on synthetic, modified questions for which the model must adhere to provided contexts inconsistent with information that may have been seen during training. While this approach reliably improves contextual faithfulness, we find that adapting to a narrowly targeted counterfactual signal yields degradation on other faithfulness evaluations and regression on tasks that evaluate a model’s parametric world knowledge and reasoning. We find that data augmentation, in which a model is trained on both parametrically consistent and counterfactual examples improves performance on faithfulness tasks with reduced regression on parametric world knowledge and reasoning tasks. We find that these improvements are largely consistent across post-training objectives (i.e. SFT, DPO) and across ground-truth, counterfactual data augmentation ratios.