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name: prompt_training_loop description: Optimize only universal visual prompt parameters while frozen model components remain unchanged.

Frozen-Model Prompt Training Loop

Use this skill when a recovery or implementation must demonstrate the paper's central adaptation mechanism: the model is frozen and only a shared visual prompt is optimized with cross-entropy. It is not for fine-tuning, linear probing, or per-image adversarial perturbation.

Inputs

• A frozen model or differentiable proxy that maps prompted examples to logits.
• Prompt parameters initialized once for the task.
• Training examples and labels.
• Learning-rate and step-count settings.

Outputs

• Updated prompt parameters.
• A trace containing loss before and after optimization.
• Invariant checks showing frozen weights did not change.

Workflow

1. Evaluate the unprompted or initial-prompt loss.
2. Compute cross-entropy gradients with respect to prompt parameters only.
3. Apply SGD or a recorded optimizer update to the prompt.
4. Re-evaluate loss and task metric.
5. Record params_before, params_after, loss_before, loss_after, and frozen-parameter checks.

Validation

A tiny logistic proxy test verifies that one or more prompt updates reduce loss and leave frozen weights unchanged.

Limitations

The helper script is intentionally scalar and lightweight for deterministic validation. Real CLIP recovery can replace the gradient provider while preserving the same contracts.