Post-trained language models fabricate citations at high rates. We ask whether that is reward hacking: scoring well on a proxy for scholarship while doing worse on what it stands for. We ran four pre-registered experiments on 900 frozen questions (US federal case law, computationally adjacent scientific literature, and medical literature) across twelve open-weights checkpoints covering four post-training stages of three model families, then refit on fifteen.
The clearest result holds the weights fixed and changes only the prompt.
Asking for citations raises fabrication sharply in post-trained checkpoints and not in base ones: a 47.8-point interaction on twelve checkpoints (Holm-adjusted p = 2e-34) and 41.5 points on fifteen, in all three domains.
A length-matched control separates the citation cue from a demand for a longer answer, so the effect is specific to citations, not verbosity.
Post-training also raises fabrication overall, from 4.8% to 56.7%, but our registered prediction that it would rise most where fabrication is most tempting did not hold.
A preference model trained only on surface citation features prefers post-trained output 86% of the time, and holding citation count fixed shows it rewards quantity.
Our answer: possibly, but not proven.
Post-trained models produce fabricated citations at far higher rates when asked for sources, and a naive evaluator that counts sources without checking them would prefer their output for citing more.
But the targeted pattern we registered did not appear, and plain instruction-following could explain the same results.
Proving that the reward causes fabrication needs an experiment that changes the reward during training, which this study does not do.
Pre-registration: doi:10.17605/OSF.IO/S53HY. Code and data: https://github.com/zainnab-sparq/citation-reward-hacking