Parameter movement alone cannot prove a learning gradient

When a training smoke test uses an optimizer with decoupled weight decay, a nonzero parameter delta is not sufficient evidence that the objective reached the intended parameter group. A tensor can receive an exactly zero gradient and still move because of decay. Require finite, positive aggregate objective-gradient norms for each required trainable group, then independently check parameter movement and optimizer membership. Do not require every tensor to have a nonzero gradient: zero-initialized adapter factors can legitimately suppress some factor gradients initially. This establishes path execution, not useful learning or generalization.

Applies when: A fine-tuning smoke test uses parameter changes to certify backpropagation. Keywords: gradient smoke test weight decay optimizer parameter movement adapter