Learning
to (Learn at Test Time): RNNs with Expressive Hidden
States
https://papers.cool/arxiv/2407.04620
Authors: Yu Sun ; Xinhao Li ; Karan Dalal ; Jiarui
Xu ; Arjun Vikram ; Genghan Zhang ; Yann Dubois ; Xinlei Chen ; Xiaolong
Wang ; Sanmi Koyejo ; Tatsunori Hashimoto ; Carlos Guestrin
Summary: Self-attention performs well in long
context but has quadratic complexity. Existing RNN layers have linear
complexity, but their performance in long context is limited by the
expressive power of their hidden state. We propose a new class of
sequence modeling layers with linear complexity and an expressive hidden
state. The key idea is to make the hidden state a machine learning model
itself, and the update rule a step of self-supervised learning. Since
the hidden state is updated by training even on test sequences, our
layers are called Test-Time Training (TTT) layers. We consider two
instantiations: TTT-Linear and TTT-MLP, whose hidden state is a linear
model and a two-layer MLP respectively. We evaluate our instantiations
at the scale of 125M to 1.3B parameters, comparing with a strong
Transformer and Mamba, a modern RNN. Both TTT-Linear and TTT-MLP match
or exceed the baselines. Similar to Transformer, they can keep reducing
perplexity by conditioning on more tokens, while Mamba cannot after 16k
context. With preliminary systems optimization, TTT-Linear is already
faster than Transformer at 8k context and matches Mamba in wall-clock
time. TTT-MLP still faces challenges in memory I/O, but shows larger
potential in long context, pointing to a promising direction for future
research.
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