- August 21, 2025: Updated.
- July 14, 2025: "Stop Overthinking" is accepted by TMLR, Transactions on Machine Learning Research.
- April 22, 2025: Updated.
- March 20, 2025: We release the first survey for efficient reasoning of LLMs "Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models".
Feel free to cite, contribute, or open a pull request to add recent related papers!
In this paper, we present the first structured survey that systematically investigates and organizes the current progress in achieving efficient reasoning in LLMs.
Below is a taxonomy graph summarizing the current landscape of efficient reasoning research for LLMs:
- Awesome-Efficient-Reasoning-LLM
- Model-based Efficient Reasoning
- Reasoning Output-based Efficient Reasoning
- Input Prompt-based Efficient Reasoning
- Reasoning Abilities with Efficient Data and Small Language Models
- Evaluation and Benchmark
"(.)" stands for "To Be Updated" in the survey paper.
- Demystifying Long Chain-of-Thought Reasoning in LLMs [Paper]
- O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning [Paper]
- Kimi k1.5: Scaling Reinforcement Learning with LLMs [Paper]
- Training Language Models to Reason Efficiently [Paper]
- L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning [Paper]
- DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models [Paper]
- Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning [Paper]
- HAWKEYE: Efficient Reasoning with Model Collaboration [Paper]
- THINKPRUNE: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning [Paper]
- Think When You Need: Self-Adaptive Chain-of-Thought Learning [Paper]
- Concise Reasoning via Reinforcement Learning [Paper]
- Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning [Paper]
- ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models [Paper]
- Scalable Chain of Thoughts via Elastic Reasoning [Paper]
- S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models [Paper]
- SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning [Paper]
- Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement [Paper]
- Efficient RL Training for Reasoning Models via Length-Aware Optimization [Paper]
- Optimizing Anytime Reasoning via Budget Relative Policy Optimization [Paper]
- Learn to Reason Efficiently with Adaptive Length-based Reward Shaping [Paper]
- Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning [Paper]
- LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling [Paper]
- Walk Before You Run! Concise LLM Reasoning via Reinforcement Learning [Paper]
- Stable Reinforcement Learning for Efficient Reasoning [Paper]
- Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models [Paper]
- Thinkless: LLM Learns When to Think. [Paper]
- Think Only When You Need with Large Hybrid-Reasoning Models. [Paper]
- When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning. [Paper]
- AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning. [Paper]
- Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL. [Paper]
- AdaptThink: Reasoning Models Can Learn When to Think. [Paper]
- Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning [Paper]
- How Far Are We from Optimal Reasoning Efficiency? [Paper]
- Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning. [Paper]
- Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty. [Paper]
- Optimizing Length Compression in Large Reasoning Models. [Paper]
- AdapThink: Adaptive Thinking Preferences for Reasoning Language Model. [Paper]
- AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length Control. [Paper]
- Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model. [Paper]
- SmartThinker: Learning to Compress and Preserve Reasoning by Step-Level Length Control. [Paper]
- Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning. [Paper]
- Train Long, Think Short: Curriculum Learning for Efficient Reasoning. [Paper]
- Sample More to Think Less: Group Filtered Policy Optimization for Concise Reasoning. [Paper]
- SABER: Switchable and Balanced Training for Efficient LLM Reasoning. [Paper]
- Promoting Efficient Reasoning with Verifiable Stepwise Reward. [Paper]
- Aware First, Think Less: Dynamic Boundary Self-Awareness Drives Extreme Reasoning Efficiency in Large Language Models. [Paper]
- TokenSkip: Controllable Chain-of-Thought Compression in LLMs [Paper]
- C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness [Paper]
- CoT-Valve: Length-Compressible Chain-of-Thought Tuning [Paper]
- Self-Training Elicits Concise Reasoning in Large Language Models [Paper]
- Distilling System 2 into System 1 [Paper]
- Can Language Models Learn to Skip Steps? [Paper]
- Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning. [Paper]
- Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models [Paper]
- Z1: Efficient Test-time Scaling with Code [Paper]
- Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization [Paper]
- Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models [Paper]
- DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models [Paper]
- AutoL2S: Auto Long-Short Reasoning for Efficient Large Language Models [Paper]
- Can Pruning Improve Reasoning? Revisiting Long-CoT Compression with Capability in Mind for Better Reasoning [Paper]
- VeriThinker: Learning to Verify Makes Reasoning Model Efficient [Paper]
- Assembly of Experts: Linear-time construction of the Chimera LLM variants with emergent and adaptable behaviors [Paper] [Model Card] [Free access via OpenRouter]
- R1-Compress: Long Chain-of-Thought Compression via Chunk Compression and Search [Paper]
- Not All Tokens Are What You Need In Thinking [Paper]
- A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings [Paper]
- ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning [Paper]
- TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression [Paper]
- OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation. [Paper]
- Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning [Paper]
- ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization. [Paper]
- Compressing Chain-of-Thought in LLMs via Step Entropy. [Paper]
- Pruning the Unsurprising: Efficient Code Reasoning via First-Token Surprisal. [Paper]
- Training Large Language Models to Reason in a Continuous Latent Space [Paper]
- Compressed Chain of Thought: Efficient Reasoning through Dense Representations [Paper]
- Efficient Reasoning with Hidden Thinking (MLLM) [Paper]
- SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs [Paper]
- Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning [Paper]
- Reasoning with Latent Thoughts: On the Power of Looped Transformers [Paper]
- CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation [Paper]
- Efficient Reasoning with Hidden Thinking [Paper]
- Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning [Paper]
- Back Attention: Understanding and Enhancing Multi-Hop Reasoning in Large Language Models [Paper]
- SEAL: Steerable Reasoning Calibration of Large Language Models for Free [Paper]
- Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains [Paper]
- Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs [Paper]
- Controlling Thinking Speed in Reasoning Models. [Paper]
- Efficiently Serving LLM Reasoning Programs with Certaindex [Paper]
- When More is Less: Understanding Chain-of-Thought Length in LLMs [Paper]
- Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching [Paper]
- Reward-Guided Speculative Decoding for Efficient LLM Reasoning [Paper]
- Fast Best-of-N Decoding via Speculative Rejection [Paper]
- FastMCTS: A Simple Sampling Strategy for Data Synthesis [Paper]
- Dynamic Parallel Tree Search for Efficient LLM Reasoning [Paper]
- Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding [Paper]
- LightThinker: Thinking Step-by-Step Compression (training LLMs to compress thoughts into gist tokens) [Paper]
- InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models [Paper]
- Reasoning Without Self-Doubt: More Efficient Chain-of-Thought Through Certainty Probing [Paper]
- SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning [Paper]
- AdaptiveStep: Automatically Dividing Reasoning Step through Model Confidence [Paper]
- Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time [Paper]
- Efficient Reasoning for LLMs through Speculative Chain-of-Thought. [Paper]
- Can atomic step decomposition enhance the self-structured reasoning of multimodal large models? [Paper]
- Think smarter not harder: Adaptive reasoning with inference aware optimization [Paper]
- Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling [Paper]
- Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning [Paper]
- Confidence Improves Self-Consistency in LLMs [Paper]
- Make every penny count: Difficulty-adaptive self-consistency for cost-efficient reasoning [Paper]
- Path-consistency: Prefix enhancement for efficient inference in llm [Paper]
- Bridging internal probability and self-consistency for effective and efficient llm reasoning [Paper]
- Towards thinking-optimal scaling of test-time compute for llm reasoning [Paper]
- Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods[Paper]
- Reasoning models can be effective without thinking [Paper]
- Retro-search: Exploring untaken paths for deeper and efficient reasoning [Paper]
- Thought manipulation: External thought can be efficient for large reasoning models [Paper]
- Sleep-time compute: Beyond inference scaling at test-time [Paper]
- Unlocking the capabilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thought [Paper]
- THOUGHTTERMINATOR: Benchmarking, Calibrating, and Mitigating Overthinking in Reasoning Models [Paper]
- Dynamic Early Exit in Reasoning Models [Paper]
- AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time [Paper]
- Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers. [Paper]
- Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence. [Paper]
- Fractured Chain-of-Thought Reasoning [Paper]
- Value-Guided Search for Efficient Chain-of-Thought Reasoning. [Paper]
- Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning. [Paper]
- First Finish Search: Efficient Test-Time Scaling in Large Language Models [Paper]
- Accelerating Large Language Model Reasoning via Speculative Search. [Paper]
- FlashThink: An Early Exit Method For Efficient Reasoning [Paper]
- Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning [Paper]
- Accelerating Chain-of-Thought Reasoning: When Goal-Gradient Importance Meets Dynamic Skipping [Paper]
- ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy [Paper]
- Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning [Paper]
- TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling [Paper]
- CoThink: Token-Efficient Reasoning via Instruct Models Guiding Reasoning Models [Paper]
- Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning. [Paper]
- AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time. [Paper]
- Every Rollout Counts: Optimal Resource Allocation for Efficient Test-Time Scaling. [Paper]
- SPECS: Faster Test-Time Scaling through Speculative Drafts. [Paper]
- BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute. [Paper]
- Accelerated Test-Time Scaling with Model-Free Speculative Sampling [Paper]
- Answer Convergence as a Signal for Early Stopping in Reasoning [Paper]
- Collaborative LLM Inference via Planning for Efficient Reasoning. [Paper]
- Wait, We Don't Need to "Wait"! Removing Thinking Tokens Improves Reasoning Efficiency [Paper]
- Efficient Reasoning Through Suppression of Self-Affirmation Reflections in Large Reasoning Models. [Paper]
- Steering LLM Thinking with Budget Guidance. [Paper]
- Exploring and Exploiting the Inherent Efficiency within Large Reasoning Models for Self-Guided Efficiency Enhancement. [Paper]
- Activation Steering for Chain-of-Thought Compression. [Paper]
- R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning. [Paper]
- MUR: Momentum Uncertainty guided Reasoning for Large Language Models. [Paper]
- Test-time Prompt Intervention. [Paper]
- Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression. [Paper]
- Token-Budget-Aware LLM Reasoning [Paper]
- Chain of Draft: Thinking Faster by Writing Less [Paper]
- How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach [Paper]
- The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models [Paper]
- Brevity is the soul of sustainability: Characterizing LLM response lengths. [Paper]
- PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models. [Paper]
- ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation. [Paper]
- Claude 3.7 Sonnet and Claude Code [website]
- Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching [Paper]
- Learning to Route LLMs with Confidence Tokens [Paper]
- Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization [Paper]
- RouteLLM: Learning to Route LLMs with Preference Data [Paper]
- ThinkSwitcher: When to Think Hard, When to Think Fast. [Paper]
- Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models. [Paper]
- SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model. [Paper]
- LIMO: Less is More for Reasoning [Paper]
- s1: Simple test-time scaling [Paper]
- S2R: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning [Paper]
- Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond [Paper]
- Small Models Struggle to Learn from Strong Reasoners [Paper]
- Towards Reasoning Ability of Small Language Models [Paper]
- Mixed Distillation Helps Smaller Language Models Reason Better [Paper]
- Small language models need strong verifiers to self-correct reasoning [Paper]
- Teaching Small Language Models Reasoning through Counterfactual Distillation [Paper]
- Improving Mathematical Reasoning Capabilities of Small Language Models via Feedback-Driven Distillation [Paper]
- Probe then retrieve and reason: Distilling probing and reasoning capabilities into smaller language models [Paper]
- Distilling Reasoning Ability from Large Language Models with Adaptive Thinking [Paper]
- SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models [Paper]
- TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation [Paper]
- Improving mathematical reasoning capabilities of small language models via feedback-driven distillation [Paper]
- Probe then retrieve and reason: Distilling probing and reasoning capabilities into smaller language models [Paper]
- TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers’ Guidance [Paper]
- When Reasoning Meets Compression: Benchmarking Compressed Large Reasoning Models on Complex Reasoning Tasks [Paper]
- Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling [Paper]
- The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks [Paper]
- Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights [Paper]
- Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs [Paper]
- The Impact of Reasoning Step Length on Large Language Models [Paper]
- S1-bench: A simple benchmark for evaluating system 1 thinking capability of large reasoning models [Paper]
- When reasoning meets compression: Benchmarking compressed large reasoning models on complex reasoning tasks. [Paper]
- Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models [Paper]
- A Technical Study into 0.5B Reasoning Language Models. [Paper]
If you find this work useful, please cite us.
@misc{sui2025stopoverthinkingsurveyefficient,
title={Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models},
author={Yang Sui and Yu-Neng Chuang and Guanchu Wang and Jiamu Zhang and Tianyi Zhang and Jiayi Yuan and Hongyi Liu and Andrew Wen and Shaochen Zhong and Hanjie Chen and Xia Hu},
year={2025},
eprint={2503.16419},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.16419},
}🧩 Layout inspired by zzli2022/Awesome-System2-Reasoning-LLM and the latest works are referred to as hemingkx/Awesome-Efficient-Reasoning. Many thanks for the great structure!
