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OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing

Yilin Bao, Ziyao He, Zayden Yang

TL;DR

This work addresses the challenge of generating globally coherent, well-grounded scientific papers by castling outline construction as a long-horizon planning problem. It introduces a hierarchical, state-action RL framework that evolves outlines through explicit diff-based edits, combined with a two-stage optimization: backward outline reconstruction and forward RL with rewards for information coverage, discourse coherence, and citation fidelity. A new arXiv-derived benchmark and data pipeline enable training signals from real document evolutions, and experiments on survey-generation tasks show improvements over strong baselines, including better long-range structure and citation reliability, especially for moderately sized, finetuned models. Overall, the approach provides a principled, controllable pathway to scalable, verifiable long-form scientific writing with potential applicability beyond surveys to broader scholarly communication.

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.

OUTLINEFORGE: Hierarchical Reinforcement Learning with Explicit States for Scientific Writing

TL;DR

This work addresses the challenge of generating globally coherent, well-grounded scientific papers by castling outline construction as a long-horizon planning problem. It introduces a hierarchical, state-action RL framework that evolves outlines through explicit diff-based edits, combined with a two-stage optimization: backward outline reconstruction and forward RL with rewards for information coverage, discourse coherence, and citation fidelity. A new arXiv-derived benchmark and data pipeline enable training signals from real document evolutions, and experiments on survey-generation tasks show improvements over strong baselines, including better long-range structure and citation reliability, especially for moderately sized, finetuned models. Overall, the approach provides a principled, controllable pathway to scalable, verifiable long-form scientific writing with potential applicability beyond surveys to broader scholarly communication.

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.
Paper Structure (20 sections, 14 equations, 3 figures, 1 table)

This paper contains 20 sections, 14 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. AI for Scientific Research and Agentic Reasoning
  4. Scientific LLMs and Automated Scientific Writing
  5. Citation Accuracy, Evaluation Benchmarks, and Scientific Text Structure
  6. Reinforcement Learning and Value Modeling for Long-Form Language Generation
  7. Method
  8. Formulation of Scientific Writing
  9. Input Space
  10. Output Space
  11. Basic Conditional Generation Objective
  12. Information Coverage and Structural Constraints
  13. Citation Consistency
  14. Constraint-based View
  15. Reinforcement Learning
  16. ...and 5 more sections

Figures (3)

  • Figure 1: Paragraph length and citation density distributions by category.
  • Figure 2: State-action complexity under varying de-informatiation rates.
  • Figure 3: Overall caption for the 3x2 figure.