大型推理模型中迈向简洁与自适应思考：综述
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
AI总结：
本综述全面总结了大型推理模型中简洁与自适应思考的最新进展，旨在解决冗长推理链导致资源浪费的问题，通过根据输入难度在快慢思考间自适应切换来提升推理效率。
AI中文摘要：
像OpenAI o1和DeepSeek R1这样的大型推理模型（LRMs），与传统大型语言模型（快速思考）相比，在具有长思维链（CoT）推理序列（慢速思考）的数学和编程等复杂推理任务上展现出了令人印象深刻的性能。然而，这些推理模型也面临着一个巨大挑战，即即使是面对简单琐碎的问题，也会生成不必要且冗长多余的推理链。这种现象导致了推理资源的严重浪费，增加了简单查询的响应时间，并阻碍了LRMs在现实产品中的实际应用。为此，缩短冗长的推理链，并根据输入难度在快慢思考之间学习自适应推理至关重要。在这篇综述中，我们全面概述了LRMs高效推理在简洁与自适应思考方面的最新进展，包括方法论、基准测试以及未来探索的挑战。我们希望这篇综述能帮助研究人员快速了解该领域的全貌，并激发新颖的自适应思考理念，以促进LRMs的更好使用。
英文摘要：
Large reasoning models (LRMs) like OpenAI o1 and DeepSeek R1 have demonstrated impressive performance on complex reasoning tasks like mathematics and programming with long Chain-of-Thought (CoT) reasoning sequences (slow-thinking), compared with traditional large language models (fast-thinking). However, these reasoning models also face a huge challenge that generating unnecessarily lengthy and redundant reasoning chains even for trivial questions. This phenomenon leads to a significant waste of inference resources, increases the response time for simple queries, and hinders the practical application of LRMs in real-world products. To this end, it is crucial to shorten lengthy reasoning chains and learn adaptive reasoning between fast and slow thinking based on input difficulty. In this survey, we provide a comprehensive overview of recent progress in concise and adaptive thinking for efficient reasoning of LRMs, including methodologies, benchmarks, and challenges for future exploration. We hope this survey can help researchers quickly understand the landscape of this field and inspire novel adaptive thinking ideas to facilitate better usage of LRMs.
