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name: prompted_multitask_training description: Execute a deterministic minimal optimizer step on prompted multitask text examples and log loss/parameter changes.

Prompted Multitask Training Step

Use this skill for bounded recovery of the T0/P3 mechanism when full encoder-decoder fine-tuning is not feasible. It provides a small softmax classifier over bag-of-words features so an experiment can prove that prompted examples are consumed by a real parameter update.

Inputs

• Rendered training records with source text and target labels.
• Label vocabulary and optional learning rate/epoch count.

Outputs

• Parameters before and after training, loss before/after, and a prediction helper.

Workflow

1. Build a deterministic vocabulary from source-task prompted text.
2. Initialize label weights to zero for reproducibility.
3. Compute multiclass logistic loss and gradients.
4. Apply gradient descent updates and log parameter changes.

Validation

Run the included tests. Recovery should save training_trace.json with params_before and params_after.

Limitations

This is a declared proxy for supervised prompted fine-tuning, not a replacement for T5/T0 model training.