@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://preview.aclanthology.org/ingest-acl/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 $\texttt{fetch}$ 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."
}