bicache enables shared-prefix KV caching in diffusion language models by dynamically selecting reusable shallow-layer depths based on prefix fraction, delivering 36.3-98.3% throughput gains with 0-1.8% accuracy difference.
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The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.
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Enabling KV Caching of Shared Prefix for Diffusion Language Models
bicache enables shared-prefix KV caching in diffusion language models by dynamically selecting reusable shallow-layer depths based on prefix fraction, delivering 36.3-98.3% throughput gains with 0-1.8% accuracy difference.
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How to deal with machine learning bias in economic history
The paper guides ML use in economic history, identifies systematic prediction bias that distorts coefficients, and shows debiasing via small expert-labeled samples can correct it while preserving scale.