Large reasoning models have recently demonstrated emergent “aha moments” that enable reflective reasoning. However, a non-trivial portion of reflective steps are “low-utility,” where reasoning errors are not corrected, or even correct reasoning trajectories are shifted toward incorrect ones. To address low-utility reflective continuation, this paper proposes RUAR, a utility-guided advantage rescaling method for reflective reasoning that estimates step-level utility from changes in correct-answer probability, without human process labels or a separately learned process reward model. RUAR augments outcome-supervised RL by rescaling the token-level advantages within each reflective reasoning step according to the estimated utility of the reflective continuation, rather than applying a uniform trajectory-level advantage across the reasoning trace. The utility is estimated through answer-forcing utility estimation, which measures how the model’s correct-answer probability changes before and after the reflective step by forcing answer generation from both reasoning states using answer-forcing prompts. Furthermore, RUAR introduces an overthinking-mitigating rescaling mechanism, where utility-guided rescaling is no longer applied from the earliest answer-ready reflection step onward. Experiments demonstrate that RUAR improves the accuracy–efficiency trade-off, substantially reducing generated token usage while often preserving or improving final-answer accuracy.