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---
name: bar_zeroth_order_optimizer
description: Train BAR universal-program parameters with query-only one-sided zeroth-order gradient estimates and SGD updates.
---
# BAR Zeroth-order Optimizer
Use this skill when recovery must optimize adversarial-program parameters against a black-box classifier that exposes only prediction outputs. It is the core BAR training loop from the paper.
Do not use it when white-box gradients are available and the goal is ordinary fine-tuning.
## Inputs
- Black-box prediction callable that accepts programmed samples and returns source probabilities.
- Embedded target samples, labels, mask, and initial program parameters.
- Label mapping from source to target classes.
- Focal-loss parameters, smoothing `beta`, random-vector count `q`, learning rate, iterations, and seed.
## Outputs
- Updated parameters `W`.
- Loss before and after optimization.
- Prediction/accuracy trace.
- Query count and evidence that an optimizer step changed parameters.
## Workflow
1. Evaluate focal loss at current parameters.
2. Draw `q` normalized random directions with a fixed seed when reproducibility matters.
3. Evaluate perturbed losses at `W + beta*U_j` using black-box calls only.
4. Average one-sided gradient estimates and update `W` by SGD.
5. Log losses, query counts, and parameters before/after.
## Validation
Run `python tests/test_bar_optimizer.py` or `validate_skill_tree.py --run-tests`.
## Limitations
This skill can run a reduced deterministic proxy. Full paper-scale results require real source models and target datasets supplied by the caller.
