Effectively Controlling Reasoning Models through Thinking Intervention
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.
67eb4fff13ca8dcb9cca5f9b
Tong Wu
extracted_pending
2025-04-01T02:31:28.689Z
62fae9328e137d7c4b896498
/avatars/1bda39dec585c099417cc9daa9f53c42.svg
Tong Wu
user
tongwu2020
67eb4fff13ca8dcb9cca5f9c
Chong Xiang
admin_assigned
2025-04-01T08:00:19.547Z
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/avatars/755efb5829f3b6d3cef886fee26e1ba9.svg
Chong Xiang
user
cxiang
67eb4fff13ca8dcb9cca5f9d
Jiachen T. Wang
67eb4fff13ca8dcb9cca5f9e
Prateek Mittal
Effectively Controlling Reasoning Models through Thinking Intervention
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.
Thinking Intervention guides reasoning in large language models by manipulating internal reasoning steps, enhancing performance across various tasks.
large language models intermediate reasoning steps Thinking Intervention thinking tokens instruction following instruction hierarchy safety alignment DeepSeek R1 models