Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, e.g., Large Language Models (LLMs), there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, and contribute to the development of AGI.

翻译：推理作为解决复杂问题的关键能力，在谈判、医疗诊断、刑事侦查等现实场景中具有重要应用价值，是通用人工智能（AGI）领域的基础方法论。随着大语言模型（LLMs）等基础模型的持续发展，学界对其推理任务能力的探索日益深入。本文系统梳理了基础推理模型的发展脉络，重点阐述了各类推理任务、方法及基准测试的最新进展，继而探讨了基础模型推理能力涌现的潜在研究方向。我们还讨论了多模态学习、自主智能体及超级对齐在推理领域的相关性。通过展望这些前沿方向，我们期望激发学界对该领域的探索热情，推动基于基础模型的推理研究取得新突破，为通用人工智能的发展贡献力量。