RASPRef: 用于大型推理模型的检索增强自监督提示精炼
RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models
AI总结：
RASPRef通过检索相关示例和推理轨迹，利用多样本一致性、验证反馈和模型生成的批评信号，迭代优化提示，提升推理任务性能。
摘要：
近期专注于推理的语言模型如DeepSeek R1和OpenAI o1在结构化推理基准如GSM8K、MATH和多跳问答任务上表现出色。然而，其性能高度依赖于提示的制定，设计有效的提示通常是手动且迭代的过程，难以跨任务或领域扩展。为此，我们引入了检索增强自监督提示精炼（RASPRef）框架，通过检索相关示例和先前生成的推理轨迹，利用多样本一致性、验证反馈和模型生成的批评信号，迭代优化提示。与以往主要改进模型输出的方法不同，RASPRef将提示作为优化目标，通过检索引导的迭代精炼过程改进提示。在GSM8K风格的数学推理任务上，检索引导的提示方法相比静态提示基线表现更优。我们进一步讨论了检索质量、轨迹选择和自监督反馈信号如何影响提示精炼的有效性。这些发现表明，提示设计仍是面向推理的语言模型的关键因素，而自我改进的提示提供了一种实用且可扩展的提升推理性能的策略。
英文摘要：
Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks. However, their performance remains highly sensitive to prompt formulation, and designing effective prompts is typically a manual and iterative process that does not scale well across tasks or domains. To address this limitation, we introduce Retrieval-Augmented Self-Supervised Prompt Refinement (RASPRef), a framework that improves prompts without requiring human annotations or task-specific supervision. The approach retrieves relevant examples and previously generated reasoning trajectories, and leverages signals such as multi-sample consistency, verifier feedback, and model-generated critiques to iteratively refine the prompt. Unlike prior approaches that focus primarily on improving model outputs, RASPRef directly treats the prompt as the optimization target and improves it through an iterative retrieval-guided refinement process. Experiments on GSM8K-style mathematical reasoning tasks show that retrieval-guided prompting improves performance compared with a static prompting baseline. We further discuss how retrieval quality, trajectory selection, and self-supervised feedback signals may influence the effectiveness of prompt refinement. These findings suggest that prompt design remains a critical factor for reasoning-oriented language models, and that self-improving prompts offer a practical and scalable strategy for improving reasoning performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
