at each turn a generator model proposes N candidate actions and a selector model picks one to execute. On long-horizon terminal tasks this gave 5–10% accuracy improvements and more consistent repeated attempts, with trajectories shifting toward fewer attempts, better instruction following, and more debugging and testing — using off-the-shelf open-weight models as the selectors.