Advanced Academy Reader

Constrained Training Manifolds

Stabilize large-model training by restricting weight updates to curated manifolds that align with desired behaviors and safety envelopes.

advanced•5 / 9

Implementation considerations

Initialization: Start models on the manifold (e.g., initialize orthogonal matrices) to avoid expensive early projections.
Computational cost: Projection steps add overhead; weigh benefits against training throughput requirements.
Hyperparameters: Learning rates may need adjustment; some manifolds prefer smaller steps to maintain accuracy.
Compatibility: Ensure manifold constraints coexist with other techniques (LoRA adapters, mixture-of-experts gating, quantization).

Section 5 of 9•