What this is
Research teams move faster when they can iterate on objective functions in plain Python and validate each checkpoint in production-like serving conditions.Why this approach
- Full-parameter updates maximize headroom for difficult reasoning and alignment tasks.
- Custom losses eliminate waiting for vendor-specific algorithm implementations.
- Serving-integrated evaluation avoids divergence between offline metrics and user-facing behavior.
Workflow
- Define objective and reward logic in your loop.
- Run short controlled experiments with frequent checkpoints.
- Hotload checkpoints into serving and evaluate with production-style prompts.
- Promote only checkpoints that pass both offline and serving evaluations.
Operational guidance
- Treat train-state checkpoints, sampler checkpoints, and deployment revisions as a single experiment bundle.
- Run small regression suites on every hotload candidate.