Choosing How Coding Agents Continue: A Study on BigCodeBench

After producing a program, a coding agent can stop, revise it, request a review, or start again. We ask whether information from the first attempt helps choose among these actions. On an eligible subset of BigCodeBench, we train a routing policy on exploratory executions and freeze it before evaluation on unseen tasks. Compared with a fixed strategy, regeneration by the stronger model, the observed gain was 2.5 percentage points (95% CI: -1.0 to 5.5). It was not statistically conclusive and fell below the preregistered practical target. A post-hoc analysis locates the net gain in stopping and stronger-model review. That review action performed poorly across the full evaluation set, but produced no execution errors where the policy selected it. All its execution errors coincided with output-limit hits; review and regeneration used different effective output allowances. These descriptive associations do not establish why the selections worked or whether they will recur. We report state-acquisition, continuation and learning costs separately. The task-level comparison exposes differences hidden by aggregate action rankings while leaving the generalisable benefit of adaptive continuation unresolved.