Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents

arXiv:2608.13179v2 Announce Type: replace Abstract: 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

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