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name: movement_scores description: Update movement-pruning importance scores from weight gradients and diagnose whether weights move away from zero.

Straight-Through Movement Score Updates

Use this skill when a pruning or recovery harness needs the core first-order movement-pruning update. Do not use it as a generic magnitude pruning score; its purpose is to accumulate task fine-tuning movement.

Inputs

• weights: numeric list or nested list.
• scores: same shape as weights.
• gradients: same shape, representing dL/dW.
• lr_score: score learning rate.
• Optional lr_weight for diagnostics of the associated SGD weight update.

Outputs

Updated scores and per-parameter diagnostics including score_delta, away_from_zero, and movement_label.

Workflow

1. Flatten and shape-check weights, scores, and gradients.
2. Compute the straight-through score gradient grad_s = grad_w * weight.
3. Apply gradient descent score_new = score - lr_score * grad_s.
4. Label weights as moving away from zero when an SGD step would increase absolute value.
5. Return updated scores in the original shape.

Validation

Run python tests/test_movement_scores.py. Tests cover all sign combinations of weight and gradient and confirm scores increase exactly for away-from-zero movement.

Limitations

This skill does not compute model gradients; callers must supply gradients from their training objective.