Reset Cost Does Not Determine Reset Value The Role of Post Reset Update Budgets

Neuron resets aim to restore plasticity in deep reinforcement learning, and recent work frames the choice of which units to reset as a cost–benefit problem with an estimable immediate cost. Does that cost predict a reset's long-run value? From common SAC checkpoints we measure, for matched reset interventions, the immediate change in the critic's TD loss and the returns that follow. An operator-aligned sensitivity score ranks the held-out loss change of random masks accurately (Spearman $0.83$), where a signed first-order prediction does not. This local accuracy does not yield a reliable ordering of long-run value: within matched checkpoint–pressure regimes, pre-registered tests detect no monotonic cost–value relation that is stable across tasks and update pressures. Across six DeepMind Control tasks, repeatedly resetting the highest-scoring units is substantially less harmful under high update pressure than under low pressure on every task, despite several times the cumulative cost. A history $\times$ recovery-budget factorial on two tasks separates the pre-reset training history from the post-reset update budget: in an algebraic decomposition the recovery budget accounts for $87\%$ of the pooled high-versus-low-pressure difference, and training history contributes a smaller, task-dependent share. Immediate cost is therefore measurable but insufficient to decide whether a reset is worthwhile; its value depends on the learning that follows. A cost estimator settles which reset is cheap, not which one is worth making.