When Partial Credit Opposes Completion

AI agents are increasingly trusted with long, open-ended work. They fix bugs across large repositories, operate terminals for hours, and chain numerous tool calls toward a single goal. The strongest models now solve a meaningful share of such tasks, yet on the hardest ones failure is still the common outcome, and closing that gap is one of the central problems in post-training. The standard recipe is reinforcement learning against a verifier. The agent attempts a task many times, an automatic checker scores each attempt, and the model is pushed toward the attempts that scored best. When success is rare, this recipe runs into a basic problem. If the checker reports only whether the task was fully solved, almost every attempt on a hard task scores zero, and a group of identical zeros teaches nothing. The natural remedy is partial credit, scoring each attempt by the fraction of requirements it met, and it is a common choice in practice. Used naively, partial credit can work against the very goal it is meant to serve. Even though it is built from the same checks that define success, a model trained on it can learn to satisfy the easy checks while giving up…