Verified90% confidenceFactTime unknown
Test-time Compute Scaling improves model performance by investing more computational resources during the inference stage, and research indicates it is more efficient than simply increasing model parameter count on certain tasks.
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90%
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Long-term
Relevance
6/1/2026
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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.84% similarUnverifiedInference-time computation improves output quality by allocating more computational resources during the inference stage while keeping model parameters fixed.80% similarUnverified推理时扩展在推理阶段增加计算量（如beam search、Best-of-N采样、蒙特卡洛树搜索），模型性能可随计算量增加持续提升并存在幂律关系79% 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.79% similarPartially VerifiedLarger model parameters bring stronger reasoning capabilities but also higher computational costs and longer response latency.79% similar
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