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name: diffused_score_training description: Build and validate reduced denoising score-training traces for single-observation simulation-based inference posteriors.

Diffused Score Training

Use this skill when a recovery or implementation needs the F-NPSE training contract for single-observation posterior scores. Do not use it to claim full neural score-network training unless a real neural model, simulator dataset, and optimizer loop are actually run.

Inputs

• Simulator pairs (theta, x) or reduced analytic Gaussian parameters.
• A noise level or reduced diffusion setting.
• Initial trainable surrogate parameters.
• Learning rate and seed.

Outputs

• Score targets for diffused or analytic conditional posterior samples.
• A training trace with loss_before, loss_after, params_before, and params_after.
• Booleans distinguishing reduced surrogate training from full score-network training.

Workflow

1. Convert the SBI task to a standard-normal-prior parameterization when possible.
2. For an analytic Gaussian reduced run, compute the posterior mean and variance for one observation.
3. Fit a small affine score surrogate a * theta + b * x + c to the exact score by one or more gradient steps.
4. Save the loss and parameter-change trace for recovery validation.
5. Keep the trace honest: mark reduced training as reduced and keep full-model flags false.

Validation

Run python tests/test_diffused_score_training.py or validate the whole skill with the Distiller validate_skill_tree.py --run-tests command.

Limitations

This skill provides deterministic reduced-training utilities. It does not implement the paper's full residual MLP score network or 400-level training loop.