Unverified50% confidenceOpinionExact time
Improving model capabilities through larger parameters, more data, and greater computing power (Scaling Law) remains one of the core strategies of major laboratories.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/12/2026
First Seen
Valid until: 12/11/2026
Sources
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Related Claims
VerifiedTest-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.73% similarUnverifiedGiven sufficient data and available compute, building a sufficiently large over-parameterized model combined with an appropriate regularization strategy often yields better results than a medium-sized model.71% similarUnverifiedInference-time computation improves output quality by allocating more computational resources during the inference stage while keeping model parameters fixed.69% 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69% similarUnverifiedIn complex scenarios requiring deep reasoning, long-context processing, or specialized knowledge, larger-scale models still tend to hold an advantage.69% similar
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