Effectively Controlling Reasoning Models through Thinking Intervention
Authors: name: Tong Wu, name: Chong Xiang, name: Jiachen T. Wang, name: Prateek Mittal

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.