[2603.12372] Efficient Reasoning with Balanced Thinking

Computer Science > Artificial Intelligence arXiv:2603.12372 (cs) [Submitted on 12 Mar 2026 (v1), last revised 2 Apr 2026 (this version, v3)] Title:Efficient Reasoning with Balanced Thinking Authors:Yulin Li, Tengyao Tu, Li Ding, Junjie Wang, Huiling Zhen, Yixin Chen, Yong Li, Zhuotao Tian View a PDF of the paper titled Efficient Reasoning with Balanced Thinking, by Yulin Li and 7 other authors View PDF HTML (experimental) Abstract:Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to mitigate overthinking, such as suppressing reflective keywords or adjusting reasoning length, may inadvertently induce underthinking, compromising accuracy. Therefore, we propose ReBalance, a training-free framework that achieves efficient reasoning with balanced thinking. ReBalance leverages confidence as a continuous indicator of reasoning dynamics, identifying overthinking through high confidence variance and underthinking via consistent overconfidence. By aggregating hidden states from a small-scale dataset into reasoning mode prototypes, we compute a steering vector to guide LRMs' reasoning trajectories. A dynamic control functio...