@inproceedings{yang-etal-2026-alden,
title = "{ALDEN}: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents",
author = "Yang, Tianyu and
Ruas, Terry and
Tian, Yijun and
Wahle, Jan Philip and
Kurzawe, Daniel and
Gipp, Bela",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.611/",
pages = "13371--13392",
ISBN = "979-8-89176-390-6",
abstract = "While Vision{--}language models (VLMs) interpret text-rich images effectively, they struggle with reasoning across long, multi-page documents. We present $\textbf{A}$ctive $\mathbf{L}$ong $\mathbf{D}$ocum$\mathbf{E}$nt $\mathbf{N}$avigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents capable of actively navigating long, visually rich documents rather than passive readers. ALDEN features a novel $fetch$ action that allows direct page indexing, complementing the classic $\textbf{search}$ action and better exploiting document structure. To ensure training efficiency and stability, we introduce a rule-based cross-level reward for dense supervision and a visual-semantic anchoring mechanism utilizing dual-path KL-divergence constraints. We train ALDEN on a curated corpus built from open-source datasets where trivial samples are filtered, and queries are rewritten to incentivize multi-turn navigation and fetch usage. Empirically, ALDEN achieves state-of-the-art results on five long-document benchmarks, offering a more accurate and efficient path for long-document understanding."
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<abstract>While Vision–language models (VLMs) interpret text-rich images effectively, they struggle with reasoning across long, multi-page documents. We present Active \mathbfLong \mathbfDocum\mathbfEnt \mathbfNavigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents capable of actively navigating long, visually rich documents rather than passive readers. ALDEN features a novel fetch action that allows direct page indexing, complementing the classic search action and better exploiting document structure. To ensure training efficiency and stability, we introduce a rule-based cross-level reward for dense supervision and a visual-semantic anchoring mechanism utilizing dual-path KL-divergence constraints. We train ALDEN on a curated corpus built from open-source datasets where trivial samples are filtered, and queries are rewritten to incentivize multi-turn navigation and fetch usage. Empirically, ALDEN achieves state-of-the-art results on five long-document benchmarks, offering a more accurate and efficient path for long-document understanding.</abstract>
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%0 Conference Proceedings
%T ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents
%A Yang, Tianyu
%A Ruas, Terry
%A Tian, Yijun
%A Wahle, Jan Philip
%A Kurzawe, Daniel
%A Gipp, Bela
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-alden
%X While Vision–language models (VLMs) interpret text-rich images effectively, they struggle with reasoning across long, multi-page documents. We present Active \mathbfLong \mathbfDocum\mathbfEnt \mathbfNavigation (ALDEN), a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents capable of actively navigating long, visually rich documents rather than passive readers. ALDEN features a novel fetch action that allows direct page indexing, complementing the classic search action and better exploiting document structure. To ensure training efficiency and stability, we introduce a rule-based cross-level reward for dense supervision and a visual-semantic anchoring mechanism utilizing dual-path KL-divergence constraints. We train ALDEN on a curated corpus built from open-source datasets where trivial samples are filtered, and queries are rewritten to incentivize multi-turn navigation and fetch usage. Empirically, ALDEN achieves state-of-the-art results on five long-document benchmarks, offering a more accurate and efficient path for long-document understanding.
%U https://aclanthology.org/2026.acl-long.611/
%P 13371-13392
Markdown (Informal)
[ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents](https://aclanthology.org/2026.acl-long.611/) (Yang et al., ACL 2026)
ACL