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New research details data-memory scaling in autoregressive prediction

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the relationship between data-memory scaling and autoregressive prediction. The study introduces a predictive-energy spectrum to model how learned memory is required to leverage additional data. The findings suggest that data set resolution and memory state are governed by this spectrum, with a minimax law describing their relationship. The paper also details how a masked query-key attention head can learn to implement these laws, with experiments validating the data-memory collapse and coupling exponents. AI

IMPACT This research provides theoretical insights into the scaling properties of autoregressive models, potentially informing future model design and training strategies.

RANK_REASON The cluster contains a single academic paper detailing new theoretical findings and experimental results in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details data-memory scaling in autoregressive prediction

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The cluster contains a single academic paper detailing new theoretical findings and experimental results in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chiwun Yang, Xiaoyu Li ·

    One Spectrum, Two Resources: Data-Memory Scaling in Autoregressive Prediction

    arXiv:2609.13500v1 Announce Type: cross Abstract: How much learned memory is needed to benefit from more data? We show that the two resources are governed by one predictive-energy spectrum in a positive-entropy autoregressive retrieval source. Each coordinate contributes its quer…

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