OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing

• URL: http://arxiv.org/abs/2601.09858v1
• Date: Wed, 14 Jan 2026 20:37:26 GMT
• Title: OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing
• Authors: Yilin Bao, Ziyao He, Zayden Yang,
• Abstract summary: We present a reinforcement learning framework that casts scientific outline construction as a long-horizon planning problem.<n>We also introduce a benchmark for scientific paper generation that evaluates document planning, input utilization, reference faithfulness, outline organization, and content-level factual accuracy.
• Score: 5.930754928033565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scientific paper generation requires document-level planning and factual grounding, but current large language models, despite their strong local fluency, often fail in global structure, input coverage, and citation consistency. We present a reinforcement learning framework that casts scientific outline construction as a long-horizon planning problem over hierarchical document structures. Our approach models edit evolving outlines through structured actions, enabling the system to incrementally build a complete scientific manuscript. To support effective and stabilize learning,we introduce a two-stage optimization procedure consisting of (i) backward outline reconstruction from partial plans to enforce global structural consistency, and (ii) forward value-guided reinforcement learning with rewards explicitly modeling scientific correctness, discourse coherence, and citation fidelity. In addition, We further introduce a benchmark for scientific paper generation that evaluates document planning, input utilization, reference faithfulness, outline organization, and content-level factual accuracy. Our results show consistent improvements over strong neural and LLM baselines, particularly in long-range structural coherence and citation reliability.

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