@inproceedings{liu-etal-2026-beyond-trajectory,
title = "Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling",
author = "Liu, Yuchen and
Yingjie, Feng and
Qin, Lixiong and
Chen, Jiasi and
Yu, Jianing and
Gao, Sheng and
Yang, Sheng and
Xu, Weiran",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1140/",
pages = "24150--24165",
ISBN = "979-8-89176-462-0",
abstract = "In Agentic Search, trajectory-level outcome rewards fail to quantify the behavioral contributions of individual steps, while existing step-level reward methods typically rely on costly tree sampling. We view world knowledge as a latent world graph and each IS task as search within a latent task graph, where effective steps should make graph progress toward the answer node. Based on this prior, we propose Graph-Distance Contribution Reward (GDCR), a step-level process reward that scores newly-retrieved and newly-cited entities by their distance to the answer node in a training-time Entity-Relation (ER) graph. We further propose Step Advantage Policy Optimization (SAPO), which converts GDCR into step-level advantages and combines them with trajectory-level outcome advantages. Experiments on four challenging benchmarks validate the effectiveness of our method."
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%0 Conference Proceedings
%T Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling
%A Liu, Yuchen
%A Yingjie, Feng
%A Qin, Lixiong
%A Chen, Jiasi
%A Yu, Jianing
%A Gao, Sheng
%A Yang, Sheng
%A Xu, Weiran
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-beyond-trajectory
%X In Agentic Search, trajectory-level outcome rewards fail to quantify the behavioral contributions of individual steps, while existing step-level reward methods typically rely on costly tree sampling. We view world knowledge as a latent world graph and each IS task as search within a latent task graph, where effective steps should make graph progress toward the answer node. Based on this prior, we propose Graph-Distance Contribution Reward (GDCR), a step-level process reward that scores newly-retrieved and newly-cited entities by their distance to the answer node in a training-time Entity-Relation (ER) graph. We further propose Step Advantage Policy Optimization (SAPO), which converts GDCR into step-level advantages and combines them with trajectory-level outcome advantages. Experiments on four challenging benchmarks validate the effectiveness of our method.
%U https://aclanthology.org/2026.findings-emnlp.1140/
%P 24150-24165
Markdown (Informal)
[Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling](https://aclanthology.org/2026.findings-emnlp.1140/) (Liu et al., Findings 2026)
ACL
- Yuchen Liu, Feng Yingjie, Lixiong Qin, Jiasi Chen, Jianing Yu, Sheng Gao, Sheng Yang, and Weiran Xu. 2026. Beyond Trajectory Rewards: Step-level Credit Assignment for Agentic Search via Graph Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24150–24165, Budapest, Hungary. Association for Computational Linguistics.