Random-perturbation, forward-pass-only training is demonstrated on simulated and physical reservoir networks, but it underperforms backpropagation on the transformer test and shows no verified pre-reservoir learning.
Hybrid heterogeneous clusters can lower the energy consumption of llm inference workloads,
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Perturbative Gradient Training: A novel training paradigm for bridging the gap between deep neural networks and physical reservoir computing
Random-perturbation, forward-pass-only training is demonstrated on simulated and physical reservoir networks, but it underperforms backpropagation on the transformer test and shows no verified pre-reservoir learning.