Efficient Reasoning with Balanced Thinking

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 function modulates this vector's strength and direction based on real-time confidence, pruning redundancy during overthinking, and promoting exploration during underthinking. Extensive experiments conducted on four models ranging from 0.5B to 32B, and across nine benchmarks in math reasoning, general question answering, and coding tasks demonstrate that ReBalance effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust LRM deployment. Code is available at https://github.com/yu-lin-li/ReBalance .

Key observations. To address this issue, we need to develop a dynamic mechanism capable of explicitly modeling and controlling both overthinking and underthinking. Though recent works (Zhang et al., 2025a; Yang et al., 2025b; Lin et al., 2025a) have achieved dynamic control by adopting manually designed metrics to adaptively retain or discard entire reasoning paths, this rigid binary selection may sacrifice the potentially valuable intermediate reasoning steps, thus still risking underthinking. This motivates us to investigate a continuous and reliable indicator of reasoning states for providing dynamic fine-grained reasoning control.

As shown in Fig. 2, we can observe that the confidence values correlate with LRMs’ reasoning behaviors. Specifically, high confidence variance may reflect frequent indecisive switching between different reasoning paths, causing redundant steps and delayed answer convergence, i.e., overthinking. Conversely, consistent overconfidence can lead to premature commitment to incorrect reasoning paths, i.e., underthinking. Thus, confidence can be leveraged as an indicator of reasoning dynamics. Given that LRMs’ internal reasoning states are inherently represented by their hidden states (Su et al., 2025a), this observation prompts us to consider whether the efficient reasoning can be achieved through balanced thinking, by dynamically adjusting hidden states according to confidence levels.

Our solution. In this work, we propose ReBalance, a training-free method that achieves efficient Reasoning with Balanced thinking. To achieve dynamic control between overthinking and underthinking, we first identify reasoning steps indicating overthinking and underthinking from a smallscale seen dataset, aggregate their corresponding hidden states into reasoning mode prototypes, and compute a steering vector that encodes the transition between them, i.e., from overthinking to underthinking. Since the steering vector captures the model’s inherent reasoning dynamics, it exhibits strong generalization across diverse unseen data, as demonstrated in our experiments.