Choosing the next action after a coding agent produces a patch requires distinguishing current correctness from the outcome of further work. We investigate whether matched executions can train a compact, non-generative controller under a fixed repair model. A three-seed CPU study trains a 0.6B current-correctness predictor, reaching 77.1\% mean accuracy on 32 development states from eight tasks. A 108-execution pilot on 12 final-candidate training states yields no repair of the three initially unsuccessful candidates; capped restart frequently damages correct patches. In a separately frozen diagnostic enriched for 12 initially unsuccessful training tasks, 83 of 84 executions are judged. Public-test feedback repairs one task at both seeds, but no pair of actions exhibits replicated opposite preferences across tasks. The prespecified feasibility gate fails. These results establish an executable collection protocol but do not establish sufficiently task-diverse intervention gains for the planned utility training. An action-utility model and independent test-set evaluation remain outstanding.