Reinforcement learning for long-horizon language agents requires identifying whether intermediate actions make eventual task success more or less likely when supervision is available only at trajectory level. The main difficulty is that a critic with aggregate return-prediction accuracy may still fail to resolve the local value differences needed to assign credit to actions, especially under sparse feedback and function approximation. Conventional critics estimate future return, but policy improvement also depends on knowing whether each intermediate decision makes task success more or less likely. We introduce SCALE, an actor-critic framework that augments conventional return estimation with explicit prediction of eventual task success. At each interaction prefix, the critic predicts a return value and a Success Value, defined as the probability that the task will eventually be completed from that prefix. SCALE converts changes in Success Value into multi-step action credit through Success-GAE and modulates its contribution according to the estimated reliability of the predictor, while retaining the token-level PPO objective. Across five agent benchmarks and three model backbones, SCALE improves task performance over the compared RL baselines in most settings, with gains of up to 4.1 percentage points in success rate. Separate critic-learning analyses show that SCALE also improves critic estimation and training stability.

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