mlsanityData to 14 Oct 2026

Thinking Machines: A Survey of LLM based Reasoning Strategies

arXiv 2503.10814 ↗First posted 13 Mar 2025cs.CLcited by 25

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Large Language Models (LLMs) are highly proficient in language-based tasks. Their language capabilities have positioned them at the forefront of the future AGI (Artificial General Intelligence) race. However, on closer inspection, Valmeekam et al. (2024); Zecevic et al. (2023); Wu et al. (2024) highlight a significant gap between their language proficiency and reasoning abilities. Reasoning in LLMs and Vision Language Models (VLMs) aims to bridge this gap by enabling these models to think and re-evaluate their actions and responses. Reasoning is an essential capability for complex problem-solving and a necessary step toward establishing trust in Artificial Intelligence (AI). This will make AI suitable for deployment in sensitive domains, such as healthcare, banking, law, defense, security etc. In recent times, with the advent of powerful reasoning models like OpenAI O1 and DeepSeek R1, reasoning endowment has become a critical research topic in LLMs. In this paper, we provide a detailed overview and comparison of existing reasoning techniques and present a systematic survey of reasoning-imbued language models. We also study current challenges and present our findings.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 58 of its 71 references that are in the map. First posted on arXiv 13 Mar 2025.

Builds on

58 of 71 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoning31 Jan
  2. 2023Hypothesis Search: Inductive Reasoning with Language Models11 Sep
  3. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks5 Jul
  4. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debate23 May
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct
  6. 2022Making Large Language Models Better Reasoners with Step-Aware Verifier6 Jun

Other references · 52

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2023Let's Verify Step by Stepcited by 3,789
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  8. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  9. 2023Teaching Large Language Models to Self-Debugcited by 988
  10. 2023Reasoning with Language Model is Planning with World Modelcited by 861
  11. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  12. 2025SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-trainingcited by 672
38 more
  1. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 574
  2. 2025Process Reinforcement through Implicit Rewardscited by 504
  3. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervisioncited by 441
  4. 2022CodeT: Code Generation with Generated Testscited by 384
  5. 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 313
  6. 2023ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMscited by 309
  7. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 307
  8. 2023Alphazero-like Tree-Search can Guide Large Language Model Decoding and Trainingcited by 295
  9. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoningcited by 279
  10. 2024Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMscited by 273
  11. 2022Reasoning with Language Model Prompting: A Surveycited by 252
  12. 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaborationcited by 251
  13. 2024Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learningcited by 241
  14. 2024ReFT: Reasoning with Reinforced Fine-Tuningcited by 239
  15. 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planningcited by 231
  16. 2024V-STaR: Training Verifiers for Self-Taught Reasonerscited by 217
  17. 2022Re3: Generating Longer Stories With Recursive Reprompting and Revisioncited by 193
  18. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 183
  19. 2023Planning with Large Language Models for Code Generationcited by 182
  20. 2023LM vs LM: Detecting Factual Errors via Cross Examinationcited by 156
  21. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 150
  22. 2024Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizingcited by 125
  23. 2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinementscited by 103
  24. 2024LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoningcited by 91
  25. 2023On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)cited by 85
  26. 2022Solving Math Word Problems via Cooperative Reasoning induced Language Modelscited by 77
  27. 2024Step-level Value Preference Optimization for Mathematical Reasoningcited by 72
  28. 2024Interpretable Contrastive Monte Carlo Tree Search Reasoningcited by 64
  29. 2025SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scalingcited by 54
  30. 2024Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizingcited by 49
  31. 2023GRACE: Discriminator-Guided Chain-of-Thought Reasoningcited by 35
  32. 2024LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraintscited by 35
  33. 2025ReVISE: Learning to Refine at Test-Time via Intrinsic Self-Verificationcited by 35
  34. 2024Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewardscited by 31
  35. 2024Learning to Refine with Fine-Grained Natural Language Feedbackcited by 27
  36. 2023Faithful Question Answering with Monte-Carlo Planningcited by 16
  37. 2024What Are Step-Level Reward Models Rewarding? Counterintuitive Findings from MCTS-Boosted Mathematical Reasoningcited by 14
  38. 2023DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Modelscited by 6

50 most cited of 52 All 52 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 18; 2026: 7.

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  1. 2025Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Timecited by 48
  2. 2025Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarkscited by 33
  3. 2025Explain Before You Answer: A Survey on Compositional Visual Reasoningcited by 28
  4. 2025Mind with Eyes: from Language Reasoning to Multimodal Reasoningcited by 22
  5. 2025Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilitiescited by 20
  6. 2025Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Modelscited by 14
  7. 2025Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoningcited by 14
  8. 2025From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Modelscited by 12
  9. 2025Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Surveycited by 10
  10. 2025First Finish Search: Efficient Test-Time Scaling in Large Language Modelscited by 9
  11. 2025Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?cited by 9
  12. 2026Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggerscited by 6
13 more
  1. 2025A Study on Thinking Patterns of Large Reasoning Models in Code Generationcited by 5
  2. 2026Character-R1: Enhancing Role-Aware Reasoning in Role-Playing Agents via RLVRcited by 5
  3. 2026StaRPO: Stability-Augmented Reinforcement Policy Optimizationcited by 5
  4. 2025Simulating Student-Tutor Interactions with LLMs for Multilingual Math Feedbackcited by 3
  5. 2025Value Under Ignorance in Universal Artificial Intelligencecited by 3
  6. 2026AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoningcited by 3
  7. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  8. 2026One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Mergingcited by 2
  9. 2025See, Think, Learn: A Self-Taught Multimodal Reasonercited by 1
  10. 2025Where You Go is Who You Are: Behavioral Theory-Guided LLMs for Inverse Reinforcement Learningcited by 0
  11. 2025Information-Dense Reasoning for Efficient and Auditable Security Alert Triagecited by 0
  12. 2026Enhancing Vision Language Models with Logic Reasoning for Situational Awarenesscited by 0
  13. 2026(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigationscited by 0

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  2. 2023Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs
  3. 2025Reasoning Beyond Limits: Advances and Open Problems for LLMs
  4. 2025From System 1 to System 2: A Survey of Reasoning Large Language Models
  5. 2025From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models
  6. 2022Towards Reasoning in Large Language Models: A Survey
  7. 2025A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
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