Author

Francois Chaubard

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#machine learning Preprint Sep 2026

Probe-Space Preconditioning for Fast and Stable Zero-Order Training

Backpropagation (BP) dominates deep learning but imposes a massive memory tax. For example, training OPT-30B with Adam requires $\approx$ 600GB of GPU memory (assuming batch size 8 and sequence length 2048). Alternatively, zero-order optimization (ZOO) trains in inference-mode (requiring only $\approx$ 60GB for the sam...

Francois Chaubard, M. Kochenderfer, Chris Ré · 0 citations

#machine learning Preprint Sep 2026

Scaling Zero-Order Pretraining through Model Sharding

Zero-order optimization (ZO) trains without backpropagation, making it relevant to forward-only hardware and non-differentiable loss, but its gradient variance grows with perturbed dimension, inhibiting large-model training. Sharded Optimization Mixture of Assemblies (SOMA) trains LSTM experts independently on $N$ data...

Francois Chaubard, M. Kochenderfer, Chris Ré · 0 citations