mlsanityData to 14 Oct 2026

Patience Is The Key to Large Language Model Reasoning

arXiv 2411.13082 ↗First posted 20 Nov 2024cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Recent advancements in the field of large language models, particularly through the Chain of Thought (CoT) approach, have demonstrated significant improvements in solving complex problems. However, existing models either tend to sacrifice detailed reasoning for brevity due to user preferences, or require extensive and expensive training data to learn complicated reasoning ability, limiting their potential in solving complex tasks. To bridge this gap, following the concept of scaling test-time, we propose a simple method by encouraging models to adopt a more patient reasoning style without the need of introducing new knowledge or skills. To employ a preference optimization approach, we generate detailed reasoning processes as positive examples and simple answers as negative examples, thereby training the model to favor thoroughness in its responses. Our results demonstrate a performance increase of up to 2.1% on GSM8k with training just on a lightweight dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 15 references that are in the map. First posted on arXiv 20 Nov 2024.

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12 of 15 references.

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Same direction, newest first

  1. 2024Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps6 Oct

Other references · 11

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  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  4. 2024GPT-4o System Cardcited by 4,588
  5. 2024Qwen2 Technical Reportcited by 2,120
  6. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  7. 2024Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMscited by 273
  8. 2024The Impact of Reasoning Step Length on Large Language Modelscited by 153
  9. 2024O1 Replication Journey: A Strategic Progress Report -- Part 1cited by 143
  10. 2023Towards Better Chain-of-Thought Prompting Strategies: A Surveycited by 62
  11. 2024Weak-to-Strong Reasoningcited by 33

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  5. 2024Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought
  6. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
  7. 2025Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning
  8. 2025Large Language Models Imitate Logical Reasoning, but at what Cost?