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arXiv · 2601.07036

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

Wang Yang·Debargha Ganguly·Xinpeng Li·Chaoda Song·Shouren Wang·Vikash Singh·Vipin Chaudhary·Xiaotian Han
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Abstract

Hybrid reasoning language models are commonly controlled through high-level Think/No-think 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 fixed-token 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.

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Wang Yang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Shouren Wang, Vikash Singh, Vipin Chaudhary, Xiaotian Han. 2026-01-11. Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers. https://arxiv.org/abs/2601.07036

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