@inproceedings{xu-etal-2026-stamp,
title = "{STAMP}: Provenance-Guided Credit Assignment for Deep Search Agents",
author = "Xu, Ke and
Xu, Han and
Chen, Xinran and
Wang, Yuqian and
Li, Zhixuan and
Liu, Xiaojian and
Wu, Changwo and
Xia, Jianqiang and
Li, Yuchen",
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.637/",
pages = "13360--13374",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement learning for deep-search agents has largely focused on trajectory-level scoring{---}outcome correctness, citation-aware rewards, and evidence coverage. Yet the actions that expose supporting documents receive no targeted credit, a gap we call the reward-credit mismatch. We propose STAMP, in which a reference-based verifier judges whether each cited document supports an entity or relation in a training-time evidence graph, and first-exposure attribution traces each supported citation back to the action that first surfaced it. This step credit is injected through sign-preserving advantage modulation, which redistributes advantage across steps without changing the trajectory-level reward or the relative ranking of trajectories within each group. On BrowseComp, BrowseComp-ZH, and xbench-DS, STAMP improves the strongest GRPO baseline by +2.0/+5.5/+3.0 points under matched SFT initialization, training data, and search tools, and composes with both outcome-only and citation-rubric base rewards. Component ablations confirm that the provenance-based credit signal and the sign-preserving advantage modulation each contribute to the gains."
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<abstract>Reinforcement learning for deep-search agents has largely focused on trajectory-level scoring—outcome correctness, citation-aware rewards, and evidence coverage. Yet the actions that expose supporting documents receive no targeted credit, a gap we call the reward-credit mismatch. We propose STAMP, in which a reference-based verifier judges whether each cited document supports an entity or relation in a training-time evidence graph, and first-exposure attribution traces each supported citation back to the action that first surfaced it. This step credit is injected through sign-preserving advantage modulation, which redistributes advantage across steps without changing the trajectory-level reward or the relative ranking of trajectories within each group. On BrowseComp, BrowseComp-ZH, and xbench-DS, STAMP improves the strongest GRPO baseline by +2.0/+5.5/+3.0 points under matched SFT initialization, training data, and search tools, and composes with both outcome-only and citation-rubric base rewards. Component ablations confirm that the provenance-based credit signal and the sign-preserving advantage modulation each contribute to the gains.</abstract>
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%0 Conference Proceedings
%T STAMP: Provenance-Guided Credit Assignment for Deep Search Agents
%A Xu, Ke
%A Xu, Han
%A Chen, Xinran
%A Wang, Yuqian
%A Li, Zhixuan
%A Liu, Xiaojian
%A Wu, Changwo
%A Xia, Jianqiang
%A Li, Yuchen
%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 xu-etal-2026-stamp
%X Reinforcement learning for deep-search agents has largely focused on trajectory-level scoring—outcome correctness, citation-aware rewards, and evidence coverage. Yet the actions that expose supporting documents receive no targeted credit, a gap we call the reward-credit mismatch. We propose STAMP, in which a reference-based verifier judges whether each cited document supports an entity or relation in a training-time evidence graph, and first-exposure attribution traces each supported citation back to the action that first surfaced it. This step credit is injected through sign-preserving advantage modulation, which redistributes advantage across steps without changing the trajectory-level reward or the relative ranking of trajectories within each group. On BrowseComp, BrowseComp-ZH, and xbench-DS, STAMP improves the strongest GRPO baseline by +2.0/+5.5/+3.0 points under matched SFT initialization, training data, and search tools, and composes with both outcome-only and citation-rubric base rewards. Component ablations confirm that the provenance-based credit signal and the sign-preserving advantage modulation each contribute to the gains.
%U https://aclanthology.org/2026.findings-emnlp.637/
%P 13360-13374
Markdown (Informal)
[STAMP: Provenance-Guided Credit Assignment for Deep Search Agents](https://aclanthology.org/2026.findings-emnlp.637/) (Xu et al., Findings 2026)
ACL
- Ke Xu, Han Xu, Xinran Chen, Yuqian Wang, Zhixuan Li, Xiaojian Liu, Changwo Wu, Jianqiang Xia, and Yuchen Li. 2026. STAMP: Provenance-Guided Credit Assignment for Deep Search Agents. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13360–13374, Budapest, Hungary. Association for Computational Linguistics.