ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents

摘要

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.