In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.

In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer. · CiteArk

测量结果

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保留同一结论下的完整结果组。已有评估排在前面；单项判断不替代整组结论。展开行查看条件、来源与历次证据。

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指标与条件	论文报告	最近可用实测	证据判断
model_variant: LoGo · reported_scope: single reported evaluation · parameter_count: 1.5B · attention_budget: 0.5 · context_extension: 32k and 128k stages · pretraining_tokens: 100B	2.112 loss	—	尚未评估
dataset: Lambada · model_variant: LoGo · parameter_count: 1.5B · attention_budget: 0.5	7.5 perplexity	—	尚未评估
model_variant: LoGo · reported_scope: single reported evaluation · parameter_count: 1.5B · attention_budget: 0.5	56.3 percentage_points	—	尚未评估

In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.

报告指标与观测值

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测量结果