Unverified50% confidenceOpinionExact time
单纯扩大参数规模带来的性能提升呈现边际递减趋势，被称为Scaling Law的天花板效应
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50%
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Long-term
Relevance
7/10/2026
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Related Claims
UnverifiedAs model parameters scale from hundreds of billions to trillions, the performance gains delivered by each additional order of magnitude of compute become increasingly marginal — a phenomenon the academic community calls the 'ceiling effect' of Scaling Laws.81% similarUnverified在收敛型任务上，模型达到一定规模后存在明显的性能饱和现象，即scaling law的收益递减区间75% similarUnverifiedThe marginal returns of simply increasing model parameters and training data are diminishing, a phenomenon known as 'Scaling Law slowdown.'73% similarUnverifiedOpenAI、DeepMind等机构的研究表明模型性能随参数量、数据量和计算量的增加呈幂律提升（Scaling Law）70% similarUnverifiedInvesting more computational resources at the inference stage can yield performance gains (test-time compute scaling theory), and the gains exhibit a明显 region of diminishing returns.70% similar
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