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name: deberta_sift_finetuning description: Run deterministic SiFT style normalized perturbation and optimizer traces for DeBERTa reduced recovery evidence.

DeBERTa SiFT Fine-Tuning

Use this skill when a recovery needs to demonstrate the paper's scale-invariant fine-tuning idea without mutating a shared environment or requiring deep learning libraries. The helper normalizes vectors, applies bounded perturbations, and performs a real scalar optimizer update that can be logged in training_trace.json.

Inputs

• Numeric feature vector or scalar features for a candidate.
• Trainable parameters.
• Gold target or desired margin.
• Perturbation scale and learning rate.

Outputs

• Normalized feature vector.
• Perturbed feature vector.
• Loss before and after the update.
• params_before and params_after suitable for Distiller reduced-recovery validation.

Workflow

1. Normalize the feature vector with scripts/sift_finetuning.py.
2. Apply a deterministic perturbation to the normalized vector.
3. Compute logistic classification loss.
4. Update trainable parameters by gradient descent.
5. Record whether the optimizer state or parameters changed.

Validation

Run the skill-tree validator with tests. The tests verify stable normalization, bounded perturbation, numeric loss, and parameter changes after one optimizer step.

Limitations

This skill is a reduced SiFT mechanism probe. It is not full adversarial training of a DeBERTa model and must be marked as reduced recovery when used without the real model stack.