Power Sampling 越纠正反而越差，掉 18.5 个点，但有修复方案，比普通采样稳。
Power Sampling 通过锐化完整生成轨迹的分布来增强推理，但研究发现它可能将更多概率质量转向正确轨迹，同时使下游推理变差。以自一致性为例，在多个模型和推理基准上准确率下降最高达18.5个百分点。原因包括剂量失配：固定指数在不同问题上导致不同程度的分布变化；以及覆盖失配：全局锐化将质量集中在少数主导路径，损失了宽泛的推理路径支持。研究者提出变形控制、支持保留的 Power 目标，在加权自一致性的同预算设置下，修复后的采样器逆转了损失并超过标准多采样推理。
More Correct Mass, Worse Answers: Why Power Sampling Can Fail and How to Fix It
Power Sampling sharpens a language model's distribution over complete generation trajectories, offering a verifier-free way to improve reasoning at inference time. It also has the potential to serve as a general-purpose front end for a broad range of downstream sampling methods. However, we uncover a striking paradox: Power Sampling can drive more probability mass toward correct trajectories while degrading the downstream inference it is intended to enhance. Using self-consistency as a representative case, we observe accuracy drops of up to 18.5 percentage points across models and reasoning benchmarks. We trace this paradox to two mismatches. Dose mismatch arises because a fixed exponent induces drastically different amounts of distributional change across problems. Coverage mismatch arises because global sharpening concentrates mass on a narrow set of dominant paths: high pass@k, often interpreted as evidence of preserved diversity, can therefore coexist with the loss of broad reasoning-path support required for downstream aggregation, search, and selection. Guided by this diagnosis, we replace uniform trajectory exponentiation with a deformation-controlled, support-preserving Power target that calibrates sharpening across problems while limiting the suppression of moderate-probability paths. In a same-budget instantiation with weighted self-consistency, the repaired sampler reverses the losses caused by global Power and outperforms standard multi-sample inference across reasoning benchmarks.