Year: 2026
Type: conference-paper
Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/Nothink instructions to regulate reasoning behavior, yet we found that such mode switching is largely driven by a small set of trigger tokens rather than the instructions themselves.Through attention analysis and controlled prompting experiments, we show that a leading "Okay" token induces reasoning behavior, while the newline pattern following " " suppresses it.Based on this observation, we propose Mid-Think, a simple training-free prompting format that combines these triggers to achieve intermediate-budget reasoning, consistently outperforming fixedtoken and prompt-based baselines in terms of the accuracy-length trade-off.Furthermore, applying Mid-Think to RL training after SFT reduces training time by approximately 15% while improving final performance of Qwen3-8B on AIME from 69.8% to 72.4% and on GPQA from 58.5% to 61.1%, demonstrating its effectiveness for both inference-time control and RL-based reasoning training.Our code is available at https://github.com/uservan/ Mid-Think.
Institution: Case Western Reserve University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166843587