Most RLVR’s reported cross-domain gain may be answer formatting
RLVR may improve a model’s score on unfamiliar multiple-choice tests without improving its answers: on four released 7B base-RL pairs, 82% of the reported gain disappeared when the researchers used an extractor that read answers both inside and outside \boxed{}. The usual extractors look first for the answer box, so training appears to move answers into that format. Re-scoring stored outputs with a second extractor can expose the difference without rerunning training.