title: A Survey of Efficient Reasoning for Large Reasoning Models: Language,
    Multimodality, and Beyond
summary: Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have
  demonstrated strong performance gains by scaling up the length of
  Chain-of-Thought (CoT) reasoning during inference. However, a growing concern
  lies in their tendency to produce excessively long reasoning traces, which are
  often filled with redundant content (e.g., repeated definitions), over-analysis
  of simple problems, and superficial exploration of multiple reasoning paths for
  harder tasks. This inefficiency introduces significant challenges for training,
  inference, and real-world deployment (e.g., in agent-based systems), where
  token economy is critical. In this survey, we provide a comprehensive overview
  of recent efforts aimed at improving reasoning efficiency in LRMs, with a
  particular focus on the unique challenges that arise in this new paradigm. We
  identify common patterns of inefficiency, examine methods proposed across the
  LRM lifecycle, i.e., from pretraining to inference, and discuss promising
  future directions for research. To support ongoing development, we also
  maintain a real-time GitHub repository tracking recent progress in the field.
  We hope this survey serves as a foundation for further exploration and inspires
  innovation in this rapidly evolving area.
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paper_title: A Survey of Efficient Reasoning for Large Reasoning Models: Language,
    Multimodality, and Beyond
paper_summary: Recent Large Reasoning Models (LRMs), such as DeepSeek-R1 and OpenAI o1, have
  demonstrated strong performance gains by scaling up the length of
  Chain-of-Thought (CoT) reasoning during inference. However, a growing concern
  lies in their tendency to produce excessively long reasoning traces, which are
  often filled with redundant content (e.g., repeated definitions), over-analysis
  of simple problems, and superficial exploration of multiple reasoning paths for
  harder tasks. This inefficiency introduces significant challenges for training,
  inference, and real-world deployment (e.g., in agent-based systems), where
  token economy is critical. In this survey, we provide a comprehensive overview
  of recent efforts aimed at improving reasoning efficiency in LRMs, with a
  particular focus on the unique challenges that arise in this new paradigm. We
  identify common patterns of inefficiency, examine methods proposed across the
  LRM lifecycle, i.e., from pretraining to inference, and discuss promising
  future directions for research. To support ongoing development, we also
  maintain a real-time GitHub repository tracking recent progress in the field.
  We hope this survey serves as a foundation for further exploration and inspires
  innovation in this rapidly evolving area.
paper_ai_summary: The survey focuses on addressing inefficiencies in reasoning efficiency of Large Reasoning Models (LRMs) by examining methods across their lifecycle and identifying common inefficiency patterns.
paper_ai_keywords: Large Reasoning Models DeepSeek-R1 OpenAI o1 Chain-of-Thought reasoning traces redundant content token economy