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name: conditional_flow_matching_loss description: Compute Conditional Flow Matching squared vector-field losses and deterministic one-parameter proxy updates.

Conditional Flow Matching Loss

Use this skill when a recovery or implementation needs the simulation-free CFM objective from Flow Matching: regress a model vector field against conditional target vectors sampled from a conditional path. Do not use it to construct OT path samples or to solve the CNF ODE.

Inputs

• A batch of predicted vector fields with shape [batch, dim].
• A matching batch of conditional target vectors u_t.
• Optional nonnegative sample weights.

Outputs

• Mean squared CFM loss.
• Per-sample squared errors.
• A deterministic scalar-parameter update log for reduced recovery checks.

Workflow

1. Validate batch size, dimensionality, finite values, and optional weights.
2. Use scripts/cfm_loss.py for loss computation or target-scaled proxy predictions.
3. For reduced recovery, use one_parameter_update to show an optimizer-relevant loss decrease without claiming image-scale model training.
4. Keep loss computation separate from path construction and ODE sampling.

Validation

Run:

python tests/test_cfm_loss.py
python scripts/cfm_loss.py --targets '[[1, 2]]' --scale 0.5

Limitations

This skill validates the CFM regression mechanism using deterministic vectors. It does not provide neural network layers, image data loaders, or large-scale optimization.

Cycle refinement: reduced recovery should record both loss-before and loss-after so no-update ablations are visible.