Reinforcement learning with verifiable rewards (RLVR) directly optimizes verifier-defined outcomes for multi-turn tool-use agents, yet its trajectory-level credit assignment conflates heterogeneous per-turn outcomes into a single reward signal. On-policy distillation provides dense per-token supervision but uses a teacher-defined token-level objective and can be prone to gradient concentration collapse. We introduce $\textbf{CrEST}$, a hierarchical credit assignment framework that anchors each sampled token's scalar advantage sign to verified rewards while incorporating dense magnitude signals from a privileged self-teacher. $\textbf{CrEST}$ resolves credit at two levels: turn-segmented verified advantages address inter-turn dilution, while entropy-gated self-teacher modulation refines intra-turn token contributions. Experiments on BFCL V3 and WildToolBench show that $\textbf{CrEST}$ consistently outperforms both RL and distillation baselines across two model scales, with the largest gains on long-trajectory and strict session-level metrics. Our work demonstrates that the teacher's role in policy optimization can be reduced from determining token-level reinforcement or suppression to modulating update magnitudes, enabling dense credit assignment while keeping verifier rewards as the training objective.

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