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Can Language Models Learn While They Think? Testing Gradient Adaptation Across Architectures
Author1, Author2, Author3, Author4, Author5
dynamic evaluationgradient adaptationlanguage modelsperformance improvement
2610.33025
Builder Relevance
1h ago80%
Abstract
This paper investigates the dynamic evaluation of language models by allowing them to take gradient steps on the text they read, revealing significant performance improvements across various architectures.
Reality Card
Core Claim
Dynamic evaluation allows softmax-attention models to reduce next-token loss by 5-8% over a document without training, outperforming frozen models with full context.
Method / Result
Masked-diffusion models show an improvement of 17-20% in next-token loss when gradient updates are applied.
Limitations
The adaptation observed is specific to style and vocabulary rather than retrieval, which may limit generalizability.
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