Vikash Singh
2026
Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers
Van Yang | Shouren Wang | Debargha Ganguly | Xinpeng Li | Chaoda Song | Vikash Singh | Vipin Chaudhary | Xiaotian Han
Findings of the Association for Computational Linguistics: ACL 2026
Van Yang | Shouren Wang | Debargha Ganguly | Xinpeng Li | Chaoda Song | Vikash Singh | Vipin Chaudhary | Xiaotian Han
Findings of the Association for Computational Linguistics: ACL 2026
Reasoning language models are controlled through explicit modes such as Think and No-think, yet we find that these behaviors are largely governed by a few token-level triggers rather than high-level instructions. Through attention analysis and controlled prompting experiments, we show that a leading “Okay” token induces reasoning behavior, while the newline pattern following ‘</think>‘ 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.