One Model to Loop Them All: Elastic Latent Recurrence for Language Models

Not provided in the abstract

language modelsrecurrencetraining efficiencymodel optimization

2610.12582

Builder Relevance

80%

8h ago

Abstract

The paper presents Elastic Latent Recurrence (ELR), a method for training looped language models that eliminates the one-loop tax, allowing for similar accuracy to non-recurrent models at one loop.

Reality Card

Core Claim

ELR enables looped language models to achieve non-recurrent model accuracy at one loop without additional cost, while allowing for improved accuracy with more loops.

Method / Result

At one loop, ELR models are 0.52 points below and 1.04 points above their matched baseline on a ten-task average.

Limitations

The paper does not specify author names or provide detailed reproducibility metrics.

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