Governed Training Regimes: Admissible Parameter Transformation Under Law

Governed Training Regimes (GTR) introduces a systems-level framework for constraining parameter evolution in machine learning training pipelines. Conventional optimization procedures treat gradient updates as automatic state transitions conditioned solely on loss minimization. Governance mechanisms, where present, typically operate post hoc through logging, evaluation dashboards, or checkpoint review. They do not determine whether a parameter update is permitted to alter canonical model state. GTR formalizes training as a sequence of admissible parameter state transitions. Each optimizer proposal Δₜ is evaluated by an admissibility operatorA(θₜ, Δₜ, ℒ) ∈ {ALLOW, REFUSE, ESCALATE},where ℒ encodes declared training law, invariants, and protected parameter constraints. State advances only when updates satisfy admissibility conditions. Checkpoint promotion is likewise governed through evaluation gates and receipted evidence. The framework introduces a first-class admissibility predicate over parameter state transitions, distinguishing it from constrained optimization, trust-region methods, experiment tracking systems, and post-training verification approaches. GTR positions optimization as a governed act rather than an ambient authority over parameter space. This release contains the conceptual framework, formal core, architectural specification, and experimental validation pathway for implementing governed optimizer loops in large-scale training environments.