Learning to (Learn at Test Time)
I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on input tokens. We call our method “Test-Time-Training layers.” TTT layers directly replace attention, and unlock linear complexity architectures with expressive memory, allowing us to train LLMs with millions (someday billions) of tokens in context. Our instantiations, TTT-Linear…
Learning to (Learn at Test Time) launched on July 8, 2024 with 13 votes, #1,242 of 5,184 launches that month and more than 75% of that year's launches.
In plain words
Learning to (Learn at Test Time) is a language model architecture that replaces traditional attention mechanisms with Test-Time-Training layers, which compress context through gradient descent on input tokens. Designed for researchers and developers building large language models, it enables linear complexity while maintaining expressive memory, allowing training with millions of tokens in context. The method achieves performance comparable to or better than Transformers and Mamba while offering improved efficiency for handling extended sequences.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I’m excited to share a project I’ve been working on for over a year, which I believe will fundamentally change our approach to language models. We’ve designed a new architecture, which replaces the hidden state of an RNN with a machine learning model. This model compresses context through actual gradient descent on…
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