Unverified50% confidenceFactExact time
当模型参数量超过某一阈值，其处理复杂推理任务的能力会出现非线性跃升，这一涌现现象被Google Brain等机构的研究系统记录
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7/16/2026
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Related Claims
Unverified大模型的涌现能力指模型参数规模突破某一阈值后算术推理等能力突然出现，由谷歌2022年的论文系统记录83% similarUnverifiedResearchers found that when model parameters exceed a threshold of approximately 10 billion, models exhibit a nonlinear emergent capability leap in tasks such as logical reasoning and multi-step planning.77% similarVerifiedThe Chain-of-Thought prompting paper published in 2022 by Jason Wei et al. of Google Brain was the first to systematically demonstrate that adding step-by-step reasoning examples significantly improves the accuracy of large models such as PaLM on mathematical reasoning tasks76% similarUnverified思维链推理的效果随模型规模呈现涌现性，在较小模型上几乎无效，在足够大的模型上才能显著提升推理准确率74% similarUnverifiedReasoning Effort is essentially a parameter that controls the depth of the model's chain of thought; the higher the level, the more internal reasoning steps the model is allowed, and latency and cost rise proportionally.73% similar
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