Learning to (Learn at Test Time): RNNs with Expressive Hidden States
The paper introduces Test-Time Training (TTT) layers for sequence modeling, featuring linear complexity and self-supervised learning for training on test sequences. TTT-Linear outperforms Transformer, while TTT-MLP shows potential for long contexts.
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The paper discusses a new class of sequence modeling layers called Test-Time Training (TTT) layers, which have linear complexity and an expressive hidden state. The key idea is to update the hidden state using self-supervised learning, allowing training even on test sequences. Two instantiations, TTT-Linear and TTT-MLP, are introduced, with the latter showing potential in long contexts. The layers are evaluated against Transformer and Mamba, with TTT-Linear already faster than Transformer at 8k context. TTT-MLP faces challenges in memory I/O but indicates promise for future research. The proposed layers aim to enhance performance in long contexts compared to existing RNN and Transformer models.
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