The First Impression Problem: Internal Bias Triggers Overthinking in Reasoning Models
This addresses a computational inefficiency in AI reasoning models, but it is incremental as it builds on known issues of overthinking without solving it.
The paper tackled the problem of overthinking in reasoning models, where internal bias from the input question triggers redundant reasoning steps, and found that this bias persists despite mitigation attempts, with interventions like removing the input question reducing redundant reasoning across tasks.
Reasoning models often exhibit overthinking, characterized by redundant reasoning steps. We identify \emph{internal bias} elicited by the input question as a key trigger of such behavior. Upon encountering a problem, the model immediately forms a preliminary guess about the answer, which we term an internal bias since it may not be explicitly generated, and it arises without systematic reasoning. When this guess conflicts with its subsequent reasoning, the model tends to engage in excessive reflection, resulting in wasted computation. We validate the association between internal bias and overthinking across multiple models and diverse reasoning tasks. To demonstrate the causal relationship more rigorously, we conduct two counterfactual interventions, showing that removing the input question after the model reduces the redundant reasoning across various complex reasoning tasks, and manually injecting bias affects overthinking accordingly. Further interpretability experiments suggest that excessive attention to the input question serves as a key mechanism through which internal bias influences subsequent reasoning trajectories. Finally, we evaluated several methods aimed at mitigating overthinking, yet the influence of internal bias persisted under all conditions.