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About this paper
Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen and 4 more
Researchers from Rice University and the University of Houston present the first structured survey on efficient reasoning for Large Language Models, classifying methodologies that address the "overthinking phenomenon" by optimizing the length and necessity of generated reasoning sequences. This survey provides a comprehensive taxonomy and critical insights to guide the development of more practical and cost-effective LLM applications.
Key insights
- Efficient reasoning is emerging as a crucial subfield for the widespread and sustainable adoption of advanced LLMs, moving beyond marginal optimization.
- Current LLM training paradigms often implicitly encourage verbosity, necessitating dedicated research to achieve efficiency in reasoning.
- A fundamental trade-off exists between reasoning efficiency and model safety, which requires careful consideration in future research endeavors.
Summary by alphaXiv.
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