title: Effectively Controlling Reasoning Models through Thinking Intervention
summary: Reasoning-enhanced large language models (LLMs) explicitly generate
  intermediate reasoning steps prior to generating final answers, helping the
  model excel in complex problem-solving. In this paper, we demonstrate that this
  emerging generation framework offers a unique opportunity for more fine-grained
  control over model behavior. We propose Thinking Intervention, a novel paradigm
  designed to explicitly guide the internal reasoning processes of LLMs by
  strategically inserting or revising specific thinking tokens. We conduct
  comprehensive evaluations across multiple tasks, including instruction
  following on IFEval, instruction hierarchy on SEP, and safety alignment on
  XSTest and SORRY-Bench. Our results demonstrate that Thinking Intervention
  significantly outperforms baseline prompting approaches, achieving up to 6.7%
  accuracy gains in instruction-following scenarios, 15.4% improvements in
  reasoning about instruction hierarchies, and a 40.0% increase in refusal rates
  for unsafe prompts using open-source DeepSeek R1 models. Overall, our work
  opens a promising new research avenue for controlling reasoning LLMs.
paper_authors._id: 67eb4fff13ca8dcb9cca5f9b
paper_authors.name: Tong Wu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-01T02:31:28.689Z
paper_authors._id: 62fae9328e137d7c4b896498
paper_authors.avatarUrl: /avatars/1bda39dec585c099417cc9daa9f53c42.svg
paper_authors.fullname: Tong Wu
paper_authors.type: user
paper_authors.user: tongwu2020
paper_authors._id: 67eb4fff13ca8dcb9cca5f9c
paper_authors.name: Chong Xiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T08:00:19.547Z
paper_authors._id: 653319c135ad8b9e58a6b874
paper_authors.avatarUrl: /avatars/755efb5829f3b6d3cef886fee26e1ba9.svg
paper_authors.fullname: Chong Xiang
paper_authors.type: user
paper_authors.user: cxiang
paper_authors._id: 67eb4fff13ca8dcb9cca5f9d
paper_authors.name: Jiachen T. Wang
paper_authors._id: 67eb4fff13ca8dcb9cca5f9e
paper_authors.name: Prateek Mittal
paper_title: Effectively Controlling Reasoning Models through Thinking Intervention
paper_summary: Reasoning-enhanced large language models (LLMs) explicitly generate
  intermediate reasoning steps prior to generating final answers, helping the
  model excel in complex problem-solving. In this paper, we demonstrate that this
  emerging generation framework offers a unique opportunity for more fine-grained
  control over model behavior. We propose Thinking Intervention, a novel paradigm
  designed to explicitly guide the internal reasoning processes of LLMs by
  strategically inserting or revising specific thinking tokens. We conduct
  comprehensive evaluations across multiple tasks, including instruction
  following on IFEval, instruction hierarchy on SEP, and safety alignment on
  XSTest and SORRY-Bench. Our results demonstrate that Thinking Intervention
  significantly outperforms baseline prompting approaches, achieving up to 6.7%
  accuracy gains in instruction-following scenarios, 15.4% improvements in
  reasoning about instruction hierarchies, and a 40.0% increase in refusal rates
  for unsafe prompts using open-source DeepSeek R1 models. Overall, our work
  opens a promising new research avenue for controlling reasoning LLMs.
paper_ai_summary: Thinking Intervention guides reasoning in large language models by manipulating internal reasoning steps, enhancing performance across various tasks.
paper_ai_keywords: large language models intermediate reasoning steps Thinking Intervention thinking tokens instruction following instruction hierarchy safety alignment DeepSeek R1 models