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name: gsm8k_verifier_training description: Train a deterministic lightweight verifier on correctness-labeled GSM8K candidate solutions and record loss traces.

GSM8K Verifier Training

Use this skill when a GSM8K recovery or experiment needs a learned candidate scorer but the full paper model stack is unavailable. It trains a small logistic verifier with deterministic features and gradient descent.

Do not present this as full GPT-3 verifier training. It is a reduced mechanism test unless connected to a real language model verifier.

Inputs

• Candidate records with binary label values.
• Candidate text, calculator checks, and extracted answers.
• Learning rate and step count.

Outputs

• Trained verifier parameters.
• Loss before and after training.
• Scored candidate records.
• Validator-compatible trace fields: params_before, params_after, loss_before, and loss_after.

Workflow

1. Extract numeric features from each candidate.
2. Initialize weights and bias deterministically.
3. Compute binary cross-entropy loss.
4. Run one or more gradient-descent steps.
5. Save parameters, loss, and scores.

Validation

Run:

python scripts/verifier_training.py self-test
python tests/test_verifier_training.py

Limitations

The feature set is intentionally simple and inspectable. It exists to prove a trainable verifier mechanism in reduced recovery, not to match the full-scale neural verifier's generalization.