A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond

• URL: http://arxiv.org/abs/2503.21614v1
• Date: Thu, 27 Mar 2025 15:36:30 GMT
• Title: A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
• Authors: Xiaoye Qu, Yafu Li, Zhaochen Su, Weigao Sun, Jianhao Yan, Dongrui Liu, Ganqu Cui, Daizong Liu, Shuxian Liang, Junxian He, Peng Li, Wei Wei, Jing Shao, Chaochao Lu, Yue Zhang, Xian-Sheng Hua, Bowen Zhou, Yu Cheng,
• Abstract summary: Large Reasoning Models (LRMs) have demonstrated strong performance gains by scaling up the length of Chain-of-Thought (CoT) reasoning during inference.<n>A growing concern lies in their tendency to produce excessively long reasoning traces.<n>This inefficiency introduces significant challenges for training, inference, and real-world deployment.
• Score: 88.5807076505261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Efficient Inference for Large Reasoning Models: A Survey [42.61170621552432]
  Large Reasoning Models (LRMs) significantly improve the reasoning ability of Large Language Models (LLMs) by learning to reason. However, their deliberative reasoning process leads to inefficiencies in token usage, memory consumption, and inference time. This survey provides a review of efficient inference methods designed specifically for LRMs, focusing on mitigating token inefficiency while preserving the reasoning quality.
  arXiv  Detail & Related papers  (2025-03-29T13:27:46Z)
• ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation [38.64751082999587]
  Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely primarily on parametric knowledge, limiting factual accuracy. We propose ReaRAG, a factuality-enhanced reasoning model that explores diverse queries without excessive iterations. Our study enhances LRMs' factuality while effectively integrating robust reasoning for Retrieval-Augmented Generation (RAG)
  arXiv  Detail & Related papers  (2025-03-27T17:44:18Z)
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities [101.77467538102924]
  Recent advancements in Large Reasoning Models (LRMs) have demonstrated remarkable performance in specialized reasoning tasks.<n>We show that acquiring deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs.<n>We demonstrate that adaptive reasoning -- employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking -- can effectively alleviate these drawbacks.
  arXiv  Detail & Related papers  (2025-03-23T08:18:51Z)
• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention.<n>Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data.<n>We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models [39.781889862599854]
  Long chain-of-thought (Long CoT) characteristics enhance reasoning abilities and enable the solution of intricate problems.<n>We first distinguish Long CoT from Short CoT and introduce a novel taxonomy to categorize current reasoning paradigms.<n>We then investigate key phenomena such as the emergence of Long CoT with these characteristics, including overthinking, and test-time scaling.
  arXiv  Detail & Related papers  (2025-03-12T17:35:03Z)
• Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives [52.863024096759816]
  Misaligned research objectives have hindered progress in adversarial robustness research over the past decade. We argue that realigned objectives are necessary for meaningful progress in adversarial alignment.
  arXiv  Detail & Related papers  (2025-02-17T15:28:40Z)
• Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models [33.13238566815798]
  Large Language Models (LLMs) have sparked significant research interest in leveraging them to tackle complex reasoning tasks.<n>Recent studies demonstrate that encouraging LLMs to "think" with more tokens during test-time inference can significantly boost reasoning accuracy.<n>The introduction of OpenAI's o1 series marks a significant milestone in this research direction.
  arXiv  Detail & Related papers  (2025-01-16T17:37:58Z)
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [92.89673285398521]
  o1-like reasoning systems have demonstrated remarkable capabilities in solving complex reasoning tasks.<n>We introduce an imitate, explore, and self-improve'' framework to train the reasoning model.<n>Our approach achieves competitive performance compared to industry-level reasoning systems.
  arXiv  Detail & Related papers  (2024-12-12T16:20:36Z)
• Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [50.67937325077047]
  This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL) Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
  arXiv  Detail & Related papers  (2024-11-15T15:18:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.