Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey

2025/07/13 by Hongyu Li, Zhu, Jason, Li, Hongyu · 11 citations
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.2507.09662

openalex publication_date 2025/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• MMD-Thinker: Adaptive Multi-Dimensional Thinking for Multimodal Misinformation Detection
• From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models
• The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute
• Trace Length is a Simple Uncertainty Signal in Reasoning Models
• Concise Reasoning in the Lens of Lagrangian Optimization
• ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
• ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code
• Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle
• BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
• ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
• Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression

Related

• A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law
• A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond
• Adaptive Deep Reasoning: Triggering Deep Thinking When Needed
• Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models