title: Patience Is The Key to Large Language Model Reasoning
summary: 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 6.7% on GSM8k with training just on a lightweight dataset.
paper_authors._id: 673ebf5a4edfc6d3b57b8376
paper_authors.name: Yijiong Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-22T13:21:54.207Z
paper_authors._id: 6374c494958cd71fa7ea0a9d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6374c494958cd71fa7ea0a9d/2YCKv6tXCZXtsIOFIIXjs.png
paper_authors.fullname: yuyijiong
paper_authors.type: user
paper_authors.user: yuyijiong
paper_title: Patience Is The Key to Large Language Model Reasoning
paper_summary: 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 6.7% on GSM8k with training just on a lightweight dataset.
paper_ai_keywords: Thought (CoT) approach large language models detailed reasoning scaling test-time patient reasoning preference optimization GSM8k