Under review as a conference paper (arXiv:2510.25668v1)
ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents
Authors: Tianyu Yang, Terry Ruas, Yijun Tian, Jan Philip Wahle, Daniel Kurzawe, Bela Gipp
Current Vision-Language Models (VLMs) struggle with long, visually complex documents that demand analysis and integration of information spread across multiple pages. Existing approaches typically rely on fixed reasoning templates or rigid pipelines, forcing VLMs into a passive role and hindering efficiency and generalization.
Executive Impact: ALDEN's Breakthrough
ALDEN (Active Long-Document Navigation) is a multi-turn reinforcement learning framework that fine-tunes VLMs as interactive agents capable of actively navigating long, visually rich documents. This marks a significant step beyond passive document reading toward agents that autonomously navigate and reason across complex documents, offering a robust path to more accurate and efficient long-document understanding. ALDEN achieves state-of-the-art performance on five long-document benchmarks, with an average answer accuracy improvement of 9.14% over strong baselines.
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Core Innovations Introduced
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Deep Analysis & Enterprise Applications
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Performance Highlights
Methodology
Key Innovations
Training Robustness
Reward Mechanism
9.14%
Average Answer Accuracy Improvement Over Strong Baselines
ALDEN achieves state-of-the-art performance on five long-document benchmarks, significantly improving answer accuracy. This validates the Agentic VRDU paradigm for autonomous navigation and reasoning across complex, visually rich documents.
Enterprise Process Flow
Active Agents (VLMs)
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Expanded Action Space (Search & Fetch)
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Cross-Level Reward (Turn & Token)
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Visual Semantic Anchoring
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State-of-the-Art A-VRDU
| Feature | Search Action | Fetch Action |
|---|---|---|
| Mechanism | Semantic query, retrieves ranked pages by relevance. | Direct page-index access. |
| Primary Use Case | Open-ended queries without explicit page references. | Explicit page references ("see page 12") or structured navigation. |
| Benefit | Effective for broad content discovery. | Efficiently handles document structure and specific references. |
Visual Semantic Anchoring: Stabilizing Training in VRDUs
Training VLMs for long, visually rich documents is challenging due to the large number of visual tokens which can lead to unstable training dynamics and entropy collapse. ALDEN addresses this with Visual Semantic Anchoring, applying a dual-path KL-divergence constraint to hidden states of generated and visual tokens. This mechanism ensures semantic grounding, prevents representation drift, and significantly improves training robustness, leading to more stable answer rewards and healthier policy exploration.
Key Highlight: Crucial for preventing hidden-state drift and maintaining semantic grounding with high-dimensional visual inputs.
Cross-Level Reward Function: Fine-Grained Guidance
ALDEN employs a novel cross-level reward function that provides supervision at both turn-level and token-level. The turn-level reward (ft + ut) enforces correct response formats and evaluates action outcomes, incorporating GAE for long-horizon credit assignment. The token-level reward applies a repetition penalty specifically to search query n-grams, preventing redundant actions. This dual-level approach offers fine-grained process supervision, encouraging informative evidence collection and discouraging repeated queries, which is vital for efficient multi-turn navigation.
Key Highlight: Integrates turn-level and token-level penalties for precise feedback and efficient exploration.
Calculate Your Potential AI ROI
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Annual Cost Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrating ALDEN-like capabilities into your enterprise.
Phase 1: Discovery & Assessment
Identify core document workflows, assess current VLM limitations, and define key performance indicators for ALDEN integration.
Phase 2: Data Preparation & Model Training
Curate and process enterprise-specific document datasets. Fine-tune ALDEN with custom rewards and visual semantic anchoring for optimal performance.
Phase 3: Integration & Pilot Deployment
Integrate ALDEN agents into existing systems. Conduct pilot programs with real-world documents, gather feedback, and iterate on agent behavior.
Phase 4: Scaling & Monitoring
Expand ALDEN deployment across relevant departments. Continuously monitor performance, refine models, and explore new applications for autonomous document navigation.
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