用模块化方法生成高质量长文本数据，提升大模型的上下文理解能力。
Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation
- 通过提示工程与大模型交互生成长文本数据
- 支持SFT、DPO、GRPO等多种训练目标
- 适合研究长上下文推理与评估的开发者
大语言模型处理和推理长文本输入的能力对众多实际应用至关重要。然而，该领域进展受限于缺乏高质量、多样且可验证的长上下文数据集，难以用于训练与评估。本文提出一种模块化、可扩展的合成长上下文数据生成框架，基于提示与大模型交互实现。该框架支持监督微调（SFT）、直接偏好优化（DPO）和组相对策略优化（GRPO）等多种训练与对齐目标。包含四种核心生成范式：多轮对话、文档支撑的输入输出对、可验证的指令-响应任务以及长上下文推理示例。通过模板化提示、模型无关架构及带元数据的输出，实现可扩展、可控且目标对齐的数据集构建，助力大模型长上下文能力的发展。
原文摘要 · Abstract (English)
The ability of large language models (LLMs) to process and reason over long textual inputs is critical for a wide range of real-world applications. However, progress in this area is significantly constrained by the absence of high-quality, diverse, and verifiable long-context datasets suitable for both training and evaluation. This work introduces a modular, extensible framework for synthetic long-context data generation via prompt-based interaction with LLMs. The framework supports multiple training and alignment objectives, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). It encompasses four core generation paradigms: multi-turn conversational dialogues, document-grounded input-output pairs, verifiable instruction-response tasks, and long-context reasoning examples. Through templated prompting, a model-agnostic architecture, and metadata-enriched outputs, the proposed approach facilitates scalable, controllable, and purpose-aligned dataset creation for advancing long-context capabilities in LLMs.
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