思维机器：基于 LLM 的推理策略综述
Thinking Machines: A Survey of LLM based Reasoning Strategies
AI总结：
本文系统综述 LLM 与 VLM 的推理技术，比较现有方法并梳理挑战，以弥合语言能力与推理能力之间的差距。
摘要：
大型语言模型（LLM）在基于语言的任务上表现极为熟练。其语言能力使其处于未来 AGI（通用人工智能）竞赛的前沿。然而，仔细审视后，Valmeekam 等人（2024）、Zecevic 等人（2023）以及 Wu 等人（2024）强调，它们的语言熟练度与推理能力之间存在显著差距。LLM 和视觉语言模型（VLM）中的推理旨在通过使这些模型能够思考并重新评估其行动和响应来弥合这一差距。推理是复杂问题求解的一项关键能力，也是建立对人工智能（AI）信任的必要步骤。这将使 AI 适合部署在医疗、银行、法律、国防、安全等敏感领域。近来，随着 OpenAI O1 和 DeepSeek R1 等强大推理模型的出现，推理赋能已成为 LLM 中的关键研究课题。在本文中，我们对现有推理技术进行了详细概述和比较，并对具备推理能力的语言模型进行了系统综述。我们还研究了当前挑战并给出了我们的发现。
英文摘要：
Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024); Zecevic et al. (2023); Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI). This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like OpenAI O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings.
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。
