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Constrained Training Manifolds

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

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Further reading and reference materials

  1. Manifold optimization tutorials for deep learning (2024–2025) – mathematical foundations and projection operators.
  2. Continual learning research using constrained updates (2025) – preventing catastrophic forgetting.
  3. Safety-focused fine-tuning papers (2024–2025) – policy vector removal and constrained alignment.
  4. Low-rank adaptation studies (2025) – trade-offs between efficiency and expressiveness.
  5. Orthogonalization techniques for attention networks (2024) – empirical results on convergence and robustness.
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