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    <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 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. 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 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.</abstract>
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%0 Conference Proceedings
%T Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents
%A Wang, Zechuan
%A Lu, Siyuan
%A Zhang, Hongxuan
%A Mo, Linjian
%A Zhuang, Chenyi
%A Gan, Leilei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wang-etal-2026-teach
%X 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 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. 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 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.
%U https://aclanthology.org/2026.emnlp-main.2634/
%P 59877-59891

Markdown (Informal)

[Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents](https://aclanthology.org/2026.emnlp-main.2634/) (Wang et al., EMNLP 2026)

• Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents (Wang et al., EMNLP 2026)

ACL

• Zechuan Wang, Siyuan Lu, Hongxuan Zhang, Linjian Mo, Chenyi Zhuang, and Leilei Gan. 2026. Teach the Magnitude, Not the Direction: Verifier-Anchored Credit Assignment for Multi-Turn Multi-Step LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 59877–59891, Budapest, Hungary. Association for Computational Linguistics.