title: Thought Manipulation: External Thought Can Be Efficient for Large
    Reasoning Models
summary: Recent advancements in large reasoning models (LRMs) have demonstrated the
  effectiveness of scaling test-time computation to enhance reasoning
  capabilities in multiple tasks. However, LRMs typically suffer from
  "overthinking" problems, where models generate significantly redundant
  reasoning steps while bringing limited performance gains. Existing work relies
  on fine-tuning to mitigate overthinking, which requires additional data,
  unconventional training setups, risky safety misalignment, and poor
  generalization.
    Through empirical analysis, we reveal an important characteristic of LRM
  behaviors that placing external CoTs generated by smaller models between the
  thinking token (<think> and </think>) can effectively
  manipulate the model to generate fewer thoughts. Building on these insights, we
  propose a simple yet efficient pipeline, ThoughtMani, to enable LRMs to bypass
  unnecessary intermediate steps and reduce computational costs significantly. We
  conduct extensive experiments to validate the utility and efficiency of
  ThoughtMani. For instance, when applied to QwQ-32B on the LiveBench/Code
  dataset, ThoughtMani keeps the original performance and reduces output token
  counts by approximately 30%, with little overhead from the CoT generator.
  Furthermore, we find that ThoughtMani enhances safety alignment by an average
  of 10%. Since model vendors typically serve models of different sizes
  simultaneously, ThoughtMani provides an effective way to construct more
  efficient and accessible LRMs for real-world applications.
paper_authors._id: 6805fa66fddd500b98039425
paper_authors.name: Yule Liu
paper_authors._id: 6805fa66fddd500b98039426
paper_authors.name: Jingyi Zheng
paper_authors._id: 6805fa66fddd500b98039427
paper_authors.name: Zhen Sun
paper_authors._id: 6805fa66fddd500b98039428
paper_authors.name: Zifan Peng
paper_authors._id: 6805fa66fddd500b98039429
paper_authors.name: Wenhan Dong
paper_authors._id: 6805fa66fddd500b9803942a
paper_authors.name: Zeyang Sha
paper_authors._id: 6805fa66fddd500b9803942b
paper_authors.name: Shiwen Cui
paper_authors._id: 6805fa66fddd500b9803942c
paper_authors.name: Weiqiang Wang
paper_authors._id: 6805fa66fddd500b9803942d
paper_authors.name: Xinlei He
paper_title: Thought Manipulation: External Thought Can Be Efficient for Large
    Reasoning Models
paper_summary: Recent advancements in large reasoning models (LRMs) have demonstrated the
  effectiveness of scaling test-time computation to enhance reasoning
  capabilities in multiple tasks. However, LRMs typically suffer from
  "overthinking" problems, where models generate significantly redundant
  reasoning steps while bringing limited performance gains. Existing work relies
  on fine-tuning to mitigate overthinking, which requires additional data,
  unconventional training setups, risky safety misalignment, and poor
  generalization.
    Through empirical analysis, we reveal an important characteristic of LRM
  behaviors that placing external CoTs generated by smaller models between the
  thinking token (<think> and </think>) can effectively
  manipulate the model to generate fewer thoughts. Building on these insights, we
  propose a simple yet efficient pipeline, ThoughtMani, to enable LRMs to bypass
  unnecessary intermediate steps and reduce computational costs significantly. We
  conduct extensive experiments to validate the utility and efficiency of
  ThoughtMani. For instance, when applied to QwQ-32B on the LiveBench/Code
  dataset, ThoughtMani keeps the original performance and reduces output token
  counts by approximately 30%, with little overhead from the CoT generator.
  Furthermore, we find that ThoughtMani enhances safety alignment by an average
  of 10%. Since model vendors typically serve models of different sizes
  simultaneously, ThoughtMani provides an effective way to construct more
  efficient and accessible LRMs for real-world applications.
paper_ai_summary: ThoughtMani reduces unnecessary reasoning steps in large reasoning models by incorporating external chain-of-thoughts, improving efficiency and safety without degrading performance.
paper_ai_keywords: large reasoning models overthinking thinking token chain-of-thoughts ThoughtMani LiveBench/Code dataset safety alignment