Thought Manipulation: External Thought Can Be Efficient for Large
  Reasoning Models
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.
6805fa66fddd500b98039425
Yule Liu
6805fa66fddd500b98039426
Jingyi Zheng
6805fa66fddd500b98039427
Zhen Sun
6805fa66fddd500b98039428
Zifan Peng
6805fa66fddd500b98039429
Wenhan Dong
6805fa66fddd500b9803942a
Zeyang Sha
6805fa66fddd500b9803942b
Shiwen Cui
6805fa66fddd500b9803942c
Weiqiang Wang
6805fa66fddd500b9803942d
Xinlei He
Thought Manipulation: External Thought Can Be Efficient for Large
  Reasoning Models
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.
ThoughtMani reduces unnecessary reasoning steps in large reasoning models by incorporating external chain-of-thoughts, improving efficiency and safety without degrading performance.
large reasoning models overthinking thinking token chain-of-thoughts ThoughtMani LiveBench/Code dataset safety alignment