Unverified50% confidenceFactExact time
研究显示推理时计算的扩展律与训练时计算具有相似的效益曲线
1
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50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
VerifiedInference-time scaling improves model performance by increasing computation during the inference phase, and exhibits a power-law relationship similar to that of pre-training.80% similarUnverifiedTest-time compute scaling improves performance on complex reasoning tasks by investing more computation during the inference stage through methods such as extending the chain of thought, multi-path sampling with best-of-n selection, and self-verification reflection.75% similarUnverifiedThe core idea of the test-time compute paradigm is to shift resources from the training phase to the answer generation process, trading more inference compute for higher-quality output.75% similarUnverified研究表明，即使使用相同的基础模型，引入迭代反思机制后在代码生成、数学推理等任务上的准确率可提升20%-40%74% similarUnverifiedSnell等人2024年的研究量化了当模型规模固定时，增加推理算力的边际收益在许多任务上优于同等算力用于继续预训练72% similar
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