Liu and Liu introduce GradLev, a dual-network architecture consisting of a primary learner P and an auxiliary predictor Q.
Parallel test-time training via costate prediction matches sequential online updates
GradLev uses an auxiliary network to predict activation gradients across tokens, enabling exact parallel forward and backward passes.
Academic
Bo Liu · Qiang Liu
The University of Texas at Austin
Research Digest··2 min read
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Why this paper
From The University of Texas at Austin
In one line
GradLev enables parallel training of token-level test-time backpropagation by predicting costates.
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