Beyond Plan Selection: Attributing and Improving Execution in RLVR

Does reinforcement learning with verifiable rewards (RLVR) improve reasoning by selecting better plans or by executing them more reliably? We study this question through a matched plan-conditioned interface that allows Base and RLVR-trained selectors and executors to be interchanged while keeping the execution context fixed for each task–plan pair. Using a 1.5B model in two controlled two-plan environments, we separate realized performance contributions from available routing opportunity and the effects of targeted training. In arithmetic, the prespecified component-replacement analysis assigns 2.27 percentage points more improvement to execution than selection (95% CI: 1.14–3.41), although a common-repeat sensitivity analysis does not establish this ordering. Motivated by this diagnosis, we evaluate Execution-Focused RLVR (EF-RLVR), which removes the direct selector-policy loss while retaining shared parameters and KL regularization.EF-RLVR improves accuracy over Standard Joint RLVR by 1.28 points across eight fresh paired training seeds (95% CI: 0.77–1.79). A factorial follow-up and a supplementary direct comparison support a larger benefit from selector-term removal than executor up-weighting. In Search, input-dependent routing remains beneficial after training under both structured and final-answer scoring, but neither the original selector objective nor a uniform-action-weighting variant learns the conditional routing rule. These results show how component attribution can guide a useful training intervention, while demonstrating that available selection opportunity need not translate into learned routing.