Understanding Reasoning LLMs【译】
原文：Understanding Reasoning LLMs
This article describes the four main approaches to building reasoning models, or how we can enhance LLMs with reasoning capabilities. I hope this provides valuable insights and helps you navigate the rapidly evolving literature and hype surrounding this topic.
本文描述了构建推理模型的四种主要方法，或者说我们如何增强大型语言模型的推理能力。我希望这篇文章能提供有价值的见解，帮助您在快速发展的文献和围绕这个话题的炒作中找到方向。
In 2024, the LLM field saw increasing specialization. Beyond pre-training and fine-tuning, we witnessed the rise of specialized applications, from RAGs to code assistants. I expect this trend to accelerate in 2025, with an even greater emphasis on domain- and application-specific optimizations (i.e., “specializations”).
在 2024 年，大型语言模型领域见证了日益增长的专业化趋势。除了预训练和微调之外，我们还看到了专业应用的兴起，从 RAG（检索增强生成）到代码助手。我预计这种趋势将在 2025 年加速，更加强调特定领域和特定应用的优化（即“专业化”）。